The Impact of National and Global Macroeconomic Factors on ...

CARDIFF METROPOLITAN UNIVERSITY

The Impact of National and Global Macroeconomic Factors on Emerging Stock

Markets: A Multi-Statistical Analysis of the MINT Countries.

Thesis by

Adenike Adebola Adesanmi

1/29/2018

Supervised by:

Dr. Ignatius Ekanem

Dr. Nnamdi Obiosa

Thesis submitted to London School of Commerce and Cardiff Metropolitan University in

Partial Fulfilment of the requirements for the degree of Doctor of Philosophy.

ADENIKE ADESANMI (20071213) 1

DECLARATION

I declare that this work has not previously been accepted in substance for any degree and is

not being concurrently submitted in candidature for any degree.

I further declare that this thesis is the result of my own work and investigations, except where

otherwise stated. A bibliography is appended.

Finally, I hereby give consent for my thesis, if accepted, to be available for photocopying and

for inter – library loans, for deposit in the university’s e – Repository, and that the title and

abstract may be available to outside organisations.

Signed: ……………………………………………………………………………

Adenike Adebola Adesanmi

DEDICATION

To God Almighty who has made it possible for me to start and complete this thesis.

ACKNOWLEDGEMENTS

My appreciation goes to my supervisory team: Dr. Nnamdi Obiosa and Dr. Ignatius Ekanem,

whose critical comments have been helpful. I will also like to acknowledge the help of

Professor Peter Abel, Professor Eleri Jones, Dr. Nandish Patel and Mr. Sheku Fofanah whose

seminar classes, suggestions and comments help by making me more focused and getting it

right. In addition, I want to appreciate my loving husband Adeyemi for his financial support

and for putting up with me and my academic commitments. Finally, I appreciate my Mum,

brothers (Ayodeji, Opeyemi, Gboyega, Yinka, Lolu) and sisters (Lolo, Oyinade, Jummy) for

their contribution one way or the other. I will not leave out my colleagues for their critical

comments during the presentation of this research idea.

ABSTRACT

This research contributes to an ongoing debate in finance on whether stock markets are

integrated or segmented. The arbitrage pricing theory (APT) suggests that systematic and

unsystematic risks are major determinants of stock market movements which have inspired

scholars to examine factors responsible for high volatility in stock price movement. The

literature on this subject has focused on investigating the impact of microeconomic and

domestic macroeconomic factors on the stock market. However, only a little effort has been

made on the potential impact of global macroeconomic factors, especially the implementation

of monetary policy, through the Federal funds rate, on the emerging markets. There are many

emerging markets in the world, but Mexico, Indonesia, Nigeria and Turkey (MINT) are a group

of emerging markets which has been promoted by investment houses as an alternative

investment destination to international investors hence why the attention on these countries.

The target sample data analysed ranges from 1993 to 2014 and data was retrieved from reliable

sources. There have been discrepancies in the results of researchers, due to the nature of the

statistical methods employed which has created the need for this study to estimate the

statistically significant interaction between stock returns and Federal funds rate, MSCI global

equity index, commodity price index, exchange rate, interest rate and industrial production

using multi-statistical strategy. Time series data analysis was used to conduct ARDL

cointegration, impulse response function, variance decomposition and Granger causality tests

to determine the short and long-run relationships between the variables. The Granger causality

test shows the direction of causality between variables and all tests are conducted using

monthly data.

The findings of this research revealed that MINT countries have significant differences in the

magnitude and their association with domestic and global macroeconomic factors. The results

indicate domestic factors, such as interest rate and exchange rate as the major determinants of

stock return movement in Mexico, Indonesia and Turkey. On the other hand, global commodity

price index is identified as the primary determinants of stock return movement in Nigeria.

Policymakers would benefit from the findings in the preparation of new capital market policies

or modification of existing policies in the interest of the MINT stock markets.

CONTENTS

DECLARATION .................................................................................................................................... 1

DEDICATION ........................................................................................................................................ 2

ACKNOWLEDGEMENTS .................................................................................................................... 3

ABSTRACT ............................................................................................................................................ 4

Table of Figures .................................................................................................................................... 10

Table of Tables ..................................................................................................................................... 11

List of Abbreviations ............................................................................................................................ 12

CHAPTER ONE ................................................................................................................................... 14

1.0 Introduction ..................................................................................................................................... 14

1.1 Background and Context ............................................................................................................. 14

1.2 Purpose of the Study ................................................................................................................... 18

1.3 Origin and Rationale behind the Formation of the MINT Countries .......................................... 21

1.3.1 Similarities in the MINT countries ...................................................................................... 26

1.4 Statement of the Problem ............................................................................................................ 28

1.5 Research Aim and Objectives ..................................................................................................... 31

1.6 Research Questions ..................................................................................................................... 32

1.7 Original Contribution .................................................................................................................. 32

1.8 Organisation of the Study ........................................................................................................... 34

CHAPTER TWO .................................................................................................................................. 37

2.0 Literature review ............................................................................................................................. 37

2.1 Introduction ................................................................................................................................. 37

2.2. Historical background of the MINT stock exchange markets ................................................... 38

2.2.1 Overview of the Mexican Stock Exchange Market ............................................................. 38

2.2.2 Overview of the Indonesia Stock Exchange Market ............................................................ 40

2.2.3 Overview of the Nigerian Stock Exchange (NSE) ............................................................... 41

2.2.4 Overview of the Stock Market in Turkey ............................................................................ 43

2.3 Theories of Market Behaviour .................................................................................................... 47

2.4 Empirical Evidence of the Relationship Between Macroeconomic Variables and Stock Market

.......................................................................................................................................................... 56

2.4.1 Empirical Evidence of the Relationship Between Stock Market and Commodity Prices .... 60

2.4.2 Empirical Evidence of the Relationship Between the Global Factors and Emerging Stock

Markets Returns ............................................................................................................................ 60

2.4.3 Empirical Evidence of the Relationship Between Stock Market and Macroeconomic

Variables in Countries Other Than the MINT .............................................................................. 63

Variables in the MINT Countries .................................................................................................. 65

2.4.4.1 Mexico .............................................................................................................................. 65

2.4.4.2 Indonesia ........................................................................................................................... 66

2.4.4.3 Nigeria ............................................................................................................................... 69

2.4.4.4 Turkey ............................................................................................................................... 71

2.5 Summary ..................................................................................................................................... 81

CHAPTER THREE .............................................................................................................................. 86

3.0 Conceptual Framework ................................................................................................................... 86

3.1 Introduction ................................................................................................................................. 86

3.2 Broad Model ............................................................................................................................... 87

3.2 Stock Market ............................................................................................................................... 89

3.2.1 Exchange Rate ..................................................................................................................... 90

3.2.2 Interest Rates ........................................................................................................................ 96

3.2.3 Mexico Economic Growth and Capital Movement ............................................................. 99

3.2.4 Indonesia Economic Growth and Capital Movement ........................................................ 101

3.2.5 Nigeria Economic Growth, Oil Production, and Capital Movement ................................. 101

3.2.6 Turkey Economic Growth and Capital Movement ............................................................ 102

3.2.7 Federal Funds Rate............................................................................................................. 103

3.2.8 Global Commodity Price Index ......................................................................................... 103

3.3 Specific Model .......................................................................................................................... 104

3.4 Summary ................................................................................................................................... 107

CHAPTER FOUR ............................................................................................................................... 108

4.0. Research Methodology ................................................................................................................ 108

4.1 Introduction ............................................................................................................................... 108

4.2 Quantitative method .................................................................................................................. 108

4.2.1 Positivist Paradigm ............................................................................................................ 109

4.2.2 Research Approach, Strategy, Time horizon and Data Type ............................................. 110

4.3 Data Collection ......................................................................................................................... 112

4.4 Justification of Choice of Variables .......................................................................................... 115

4.4.1 Mexican IPC Index ............................................................................................................ 115

4.4.2 Jakarta Composite Index (JCI) ........................................................................................... 115

4.4.3 Borsa Istanbul National 100 Index (XU100) ..................................................................... 116

4.4.4 Nigeria All-share Index (NASI) ......................................................................................... 116

4.4.5 Interest Rate (Discount/Borrowing Rate) IR ...................................................................... 116

4.4.6 Commodity Price Index (CPI) ........................................................................................... 118

4.4.7 Exchange Rate (ER) ........................................................................................................... 118

4.4.8 Economic Growth (Industrial Production (IP) ................................................................... 119

4.4.9 Federal Funds Rate (FFR) .................................................................................................. 120

4.5 Model Specification .................................................................................................................. 121

4.5.1 Descriptive Statistics .......................................................................................................... 123

4.5.2 Diagnostic Tests ................................................................................................................. 124

4.5.3 Unit Root Test .................................................................................................................... 126

4.5.4 Augmented Dickey–Fuller (ADF) Test ............................................................................. 127

4.5.5 Phillips–Perron (PP) Test ................................................................................................... 128

4.5.6 Lag Selection Criteria ........................................................................................................ 128

4.5.7 Cointegration Test .............................................................................................................. 129

4.5.8 Vector Autoregressive (VAR) Model ................................................................................ 131

4.5.9 Vector Error Correction Model (VECM) ........................................................................... 132

4.5.10 Impulse Response Function (IRF) ................................................................................... 133

4.5.11 Variance Decomposition (VDC) ...................................................................................... 134

4.5.12 Granger Causality ............................................................................................................ 134

4.6 Summary ................................................................................................................................... 135

CHAPTER FIVE ................................................................................................................................ 137

5.0 Analysis......................................................................................................................................... 137

5.1 Introduction ............................................................................................................................... 137

5.2 Presentation of Results .............................................................................................................. 137

5.2.1 Descriptive Statistics .......................................................................................................... 138

5.2.2 Diagnostic Test Results ...................................................................................................... 143

5.2.3 Graphical Illustration of Each of the Variables in Their Level Form ................................ 146

5.2.4 Lag Length Selection Result .............................................................................................. 150

5.2.5 Unit Root Test Result ......................................................................................................... 150

5.2.6 Cointegration Result .......................................................................................................... 152

5.2.7 Long-Run Estimation ......................................................................................................... 153

5.2.8 Short-Run Component Estimation ..................................................................................... 158

5.2.9 Impulse Response Function ............................................................................................... 159

5.2.10 Variance Decomposition .................................................................................................. 164

5.2.11 Granger Causality Output ................................................................................................ 166

5.2.12 Variance Ratio test ........................................................................................................... 168

5.2.13 Diagnostic Tests ............................................................................................................... 169

5.3 Summary ................................................................................................................................... 171

CHAPTER SIX ................................................................................................................................... 172

6.0 Discussion ..................................................................................................................................... 172

6.1 Introduction ............................................................................................................................... 172

6.2 Variability of the Markets ......................................................................................................... 172

6.2.1 The Long-Run Estimation .................................................................................................. 173

6.2.2 Specific Interaction Between Stock Market and Each of the Macroeconomic Variables as

Reported by the IRF Output ........................................................................................................ 174

6.2.3 Macroeconomic Variables and How They Account for Variation in the Stock Market and

Their Causal Links ...................................................................................................................... 180

6.3 Review of Research Aim and Objectives ................................................................................. 182

6.3.1 Objective One .................................................................................................................... 183

6.3.2 Objective Two .................................................................................................................... 184

6.3.3 Objective Three .................................................................................................................. 186

6.3.4 Objective Four ................................................................................................................... 187

6.3.5 Objective Five .................................................................................................................... 187

6.4. Review of Research Hypotheses .............................................................................................. 188

6.5 Summary ................................................................................................................................... 191

CHAPTER SEVEN ............................................................................................................................ 192

7.0 Summary, Conclusion, Recommendation and Opportunities for Further Research ..................... 192

7.1 Introduction ............................................................................................................................... 192

7.2 Summary ................................................................................................................................... 192

7.2.1 Key Findings ...................................................................................................................... 193

7.2.2 Theoretical Evidence and Practical Implication ................................................................ 194

7.2.3 Conclusion ......................................................................................................................... 198

7.2.4 Main Contributions ............................................................................................................ 199

7.3 Limitations of the Research ...................................................................................................... 201

7.4 Further Research ....................................................................................................................... 202

Bibliography ....................................................................................................................................... 203

Appendices .......................................................................................................................................... 227

(i) Descriptive Statistics (MINT) ............................................................................................. 227

(ii)Variance Inflation factor ........................................................................................................ 228

(iii) Break point test .................................................................................................................... 229

(iv) Random Walk Result ........................................................................................................... 231

(v)Granger Causality Result ........................................................................................................ 233

(vi) ARDL Bounds Result .............................................................................................................. 242

(vii) Unit Root Result .................................................................................................................. 243

(viii)Variance Decomposition Result .......................................................................................... 256

(ix) Unit Root Result ................................................................................................................... 258

(x) ARDL Cointegration Result .................................................................................................. 260

(xi) Autocorrelation Result Output ............................................................................................. 264

Table of Figures

Figure 1 Annual Percentage Growth Rate (MINT and the US) ............................................................ 22

Figure 2 Mexico GDP Growth Rate Annual % .................................................................................... 23

Figure 3 Indonesia GDP Growth Rate Annual % ................................................................................. 24

Figure 4 Nigeria GDP Growth Rate Annual % ..................................................................................... 25

Figure 5 Turkey GDP Growth Rate Annual % ..................................................................................... 26

Figure 6 Structure of the chapter........................................................................................................... 38

Figure 7 Broad flow chart ..................................................................................................................... 88

Figure 8 Market for Dollars in Mexico (Increase in Demand) ............................................................. 91

Figure 9 Market for Peso in the United States (Increase in Supply) ..................................................... 92

Figure 10 The Relationship Between Interest Rates and Bonds ........................................................... 96

Figure 11 The Shift of Demand and Supply ......................................................................................... 97

Figure 12 Mexico, Indonesia, Nigeria and Turkey Foreign Direct Investment Inflow....................... 100

Figure 13 Illustration Showing the Conceptual Framework of the Dependent and Intervening

Variables That Are Used in the Research ........................................................................................... 106

Figure 14 Quantitative Research Stages ............................................................................................. 111

Figure 15 Federal Funds Rate Impact Process .................................................................................... 120

Figure 16 Cointegration Decision ....................................................................................................... 131

Figure 17 Graphical Illustration to detect outliers .............................................................................. 142

Figure 18 Graphical Illustration of the Variables in their Level Form ............................................... 145

Figure 19 Impulse Response Function Graphs ................................................................................... 161

Figure 20 Expanding the APT Framework ......................................................................................... 200

Table of Tables

Table 1 Statistical data on the MINT countries in 2016 ....................................................................... 22

Table 2 Events in the Mexican Stock Market ....................................................................................... 39

Table 3 Events in the Indonesia stock market ....................................................................................... 40

Table 4 Events in the Nigerian Stock Market ....................................................................................... 42

Table 5 Events in the Turkish Stock Exchange Market ........................................................................ 43

Table 6 MINT Stock Market Data ........................................................................................................ 45

Table 7 Definitions of Efficient Capital Market ................................................................................... 49

Table 8 Analysis Guideline ................................................................................................................... 57

Table 9 Summary Table of Significant Empirical Findings in Emerging Countries ............................ 74

Table 10 Research Design .................................................................................................................. 111

Table 11 Sampling Table .................................................................................................................... 113

Table 12 Research Variables .............................................................................................................. 114

Table 13 Descriptive Statistics Result for Mexico and Indonesia ...................................................... 138

Table 14 Descriptive Statistics Result for Nigeria and Turkey ........................................................... 140

Table 15 Variance Inflation Factor (VIF) Output for the MINT Countries ........................................ 144

Table 16 Lag Length Selection Output ............................................................................................... 149

Table 17 Unit Root with Structural Break Output .............................................................................. 151

Table 18 ARDL Cointegration test Result .......................................................................................... 153

Table 19 Long-Run Coefficients......................................................................................................... 154

Table 20 ARDL Long -Run Estimation .............................................................................................. 157

Table 21 ARDL Error Correction Term ............................................................................................. 158

Table 22 Variance Decomposition output .......................................................................................... 164

Table 23 Granger Causality Output .................................................................................................... 166

Table 24 Variance Ratio Output Table ............................................................................................... 168

Table 25 The Autocorrelation output .................................................................................................. 170

Table 26 Table of the Variability of the MINT Countries' Stock Markets ......................................... 173

Table 27 Table of the Response of MINT Stock Market to Exchange Rate, Interest Rate, Industrial/Oil

production Index, Commodity Price Index and Federal Funds Rate .................................................. 188

List of Abbreviations

Word Meaning

AFC Asian Financial Crisis

APT Arbitrage Pricing theory

BMV Bolsa Mexicana de Valores

BRIC Brazil, Russia, India, and China

CAPM Capital Asset Pricing Model

CBRT Central Bank of the Republic of Turkey

CIA Central Intelligence Agency

DCF Discounted Cash Flow

EMH Efficient Market Hypothesis

ER Foreign Exchange Rate

EU European Union

FAO Food and Agriculture Organisation

FDI Foreign Direct Investment

GDP Gross Domestic Product

GFC Global Financial Crisis

IBRD International Bank for Reconstruction and

Development

IMF International Monetary Fund

IMKB Istanbul Menkul Kiymetler Borsasi

IRF Impulse response Function

JSX Jakarta stock exchange

MINT Mexico, Indonesia, Nigeria, and Turkey

MSCI Morgan Stanley Capital International

MXN Mexican Peso Rates

NAFTA North American Free Trade Agreement

NSE The Nigerian Stock exchange

OECD Organisation for Economic Co-operation and

Development

OPEC Organisation of Petroleum Exporting

Countries

SAP Structural Adjustment Programs

SEC Securities and Exchange Commission

UK United Kingdom

UNCTAD United Nations Conference on Trade and

Development

US United states

USD United States Dollar

VAR Vector autoregressive

VDC Variance Decomposition

VECM Vector Error Correction model

WFE World Federation of Exchanges

WTO World Trade Organisation

CHAPTER ONE

1.0 Introduction

1.1 Background and Context

The financial system is a very broad one, and its functions range between financial

intermediation (insurance companies, banks and pension funds) and markets (stocks and bonds

market). An enormous portion of savings in an economy is channelled towards productive

investments through financial markets and intermediaries. Banks serve as financial

intermediaries between savers and borrowers by providing credit markets, which enable debt

financing for investments, while stock markets are alternative methods of intermediation

through equity financing; however, the latter is only possible through the development of

capital market (Aduda et al., 2012). The long-term growth of an economy is determined by the

rate at which capital is accumulated; therefore, an efficient capital market is a necessity for an

economy. The stock exchange as part of the capital market plays a pivotal role in the growth

of a nation; there is a focus in the financial sector of a large number of countries on its role in

directing idle resources into productive areas (Mohammed et al., 2009). Given the ability of

capital markets to instantaneously make changes in the economy, it is believed to be the

heartbeat of a nation’s economy (Maku & Atanda, 2009). It is, therefore, impossible to imagine

the world without the stock market (Agrawalla, 2005).

The stock market operation harmonizes the preferences of lenders and borrowers

(Buyuksalvarci & Abdioglu, 2010). Studying the performance of the stock market through

observing the composite index provides historical information about the performance of the

market. This report gives investors the opportunity to compare the performances of individual

assets in a portfolio. The information derived also helps in forecasting future trends in the

market (Naik & Padhi, 2012).

Investors use both potential economic fundamentals and other firm specific characteristics to

formulate their expectations. The stock market is said to contribute immensely by giving a

boost to savings as well as increasing quality and quantity of investment (Singh, 1997). Yartey

& Adjasi (2007) suggested that the stock market is expected to have a positive influence on

economic growth by creating avenues for equity financing and encouraging savings.

Over the past decades, emerging stock markets have accounted for a significant proportion of

the upsurge in the worlds’ stock market. There have been changes in the international financial

system, such as the gradual abolition of capital flow barriers and the emergence of new capital

markets, which have contributed largely to the variation in investment opportunities (Okpara

& Odionye, 2012). The stock market serves as a source of long-term capital to listed firms by

pooling funds from various investors and giving the opportunity for expansion in business; it

also offers investors the opportunity to have alternative avenues to invest their surplus funds

(Naik & Padhi, 2012). The stock market helps to provide substantial and long-term capital

through the issuing of shares for industries that are in need of finance to expand their business

(Elly & Oriwo, 2012). Advanced economies have fully explored the benefits of mobilizing

resources through the capital market while emerging markets are yet to fully benefit from

sourcing for capital through the stock market (Asaolu & Ogunmuyiwa, 2011). Attempts have

been made to stabilize and develop emerging stock markets as they are characterized as most

volatile (Engel & Rangel, 2005); various factors, such as globalization, deregulation and

advancement in information technology, have contributed to the growth of financial markets

in emerging economies (Aggarwal & Harper, 2010). These factors, however, have increased

the level of integration among capital markets all over the world. This has made market

integration form an important part of studies examining the relationship between

macroeconomic factors and stock market (Okoli, 2012). In spite of the increase in capital flight

from developed economies to emerging markets and the expected higher returns (Ushad et al.,

2008), emerging markets have not received considerable attention in this area of study.

Critics of the stock market have argued that markets characterized by weak corporate control

mechanisms may risk investors’ wealth, predominantly foreign investors who are likely to trade

their shares at discount prices (Khanna, 2009). This circumstance is, however, more prevalent

in emerging economies because they are characterized by inadequate systems of governance

and weak regulatory institutions (Hearn & Piesse, 2010). Pradhan et al. (2014) argued that

growth in the economy can be achieved when a country has an efficient financial system such

as the stock market and the banking sector.

In the early 1980s, the Bretton Woods’ Structural Adjustment Programme (SAP) was formed

to help promote the development of the stock market. However, the development of the stock

market after the SAP was not supported by some researchers, including Schleifer & Summers

(1988), who argued that stock market development could lead to counter-productive corporate

takeovers which could hinder economic growth. There have been studies that discourage the

development of the stock market in emerging economies, especially Africa; the critics of the

stock market in this circumstance suggest that emerging markets should focus on developing a

sound banking system as both institutions serve the same purpose (Singh, 1999). However, it

can be argued that a sound banking system requires a sound stock market for it to meet its

obligations; this is because when the stock market is flourishing, investment activities would

be on the increase and banks’ brokerage functions would be positively influenced.

Stern (1989) found no relationship between financial system development and economic

growth. The various critics of the stock market have been ignored by researchers, especially

when the stock market has been very efficient in the area of transferring available idle funds

from surplus units to units suffering from deficits (Naik & Padhi, 2012). Recent research also

shows the importance of capital markets in fostering economic growth (Asaolu &

Ogunmuyiwa, 2011).

There is an assumption that changes in macroeconomic events and financial policies

significantly influence stock markets and the overall economy (Adam & Tweneboah, 2008).

The question of whether macroeconomic factors can predict movement in the stock market is

of great concern; this line of thought in finance is called the macroeconomic approach 1

(Adaramola, 2011). There has been both financial2 and economic3 theories which argue that

stock markets are influenced by the movements or performance of main macroeconomic

factors (Ahmad & Ghazi, 2014). These arguments have been intensified because the stock

market reflects the extent of development in the domestic economy.

Developed economies have experienced expansion in productivity as a result of growth in their

stock market. Fama (1981) and Chen et al.(1986) researched into the linkages between the

stock market and domestic macroeconomic variables in advanced economies; their idea

supports that of those who believe in complete market segmentation.

1 It is a way of using factor analysis technique to identify factors that influence stock returns and prices (see Adaramola, 2011) 2 The arbitrage pricing theory by Ross (1976) which suggests that asset prices can be predicted when the relationship between common risk factors and asset prices are identified. 3 The quantity theory of money by Fisher which suggests that a percentage change in the quantity of money leads to the same percentage change in price level (Fisher, 1930).

Researchers (see Kutty, 2010; Adaramola, 2011; Castillo-Ponce et al., 2015) who are interested

in emerging markets have documented a number of ideas on the relationship between the stock

market and domestic macroeconomic factors and have found most emerging markets to exhibit

complete segmentation. However, Harvey (1995) has suggested that some emerging markets

exhibit time-varying integration as findings show emerging markets to be more integrated than

expected based on investment restrictions. Recent crashes in the global economy have

prompted investors to seek new means of diversifying their investment portfolios across

diverse geographical locations (Boako & Alagidede, 2016). The quest for investors to shift

from developed markets to emerging markets has therefore increased the number of researchers

examining how macroeconomic variables can be used to predict the movements of stock

markets in emerging economies.

The findings of Fama (1981) and Chen et al. (1986) have given reasons to doubt a widely-

acceptable theory in finance: the efficient market hypothesis, which suggests that all relevant

information about changes in factors, such as macroeconomic factors, are fully reflected in

current stock prices (Eakins & Mishkin, 2012). This makes it impossible to use information

about past movements in stock returns to predict their future movements. The efficient market

hypothesis assumes that the market is perfect i.e. there is a symmetric information environment

with perfect competition and no transaction costs. Stock index change on a daily basis, and

these changes are attributed to demand and supply. There are, however, some factors that

influence the increase and decrease in demand and supply of stock, which are company

fundamentals, market behaviour and external forces (Kurihara, 2006).

The stock market is also said to develop its own growth dynamics which are mostly guided by

irrational behaviour; this irrationality, therefore, is expected to have an adverse effect on the

real sector of the economy as the stock market is in danger of becoming the ‘by-product of a

casino’ (Yartey & Adjasi, 2007:5).

The interaction between macroeconomic factors and the stock market has attracted a significant

number of researchers for more than three decades (see Chen et al., 1986; Levine & Zervos,

1998; Huang et al., 2000; Wongbangpo & Sharma, 2002; Yuko & Ito, 2004; Kurihara, 2006;

Buyuksalvarci & Abdioglu, 2010; Adaramola, 2011; Okpara & Odionye, 2012; Hsing et al.,

2013; Ibrahim & Musah, 2014; Castillo-Ponce et al., 2015). The collapse of the housing bubble

in the US in 2006 spread across emerging markets which saw a massive drop in their shares in

2008 and 2009. Although much effort has been devoted to the relationship between stock

markets and macroeconomic factors, the primary focus of previous studies has been on how

domestic macroeconomic factors play a major role in determining stock movement. Domestic

macroeconomic factors such as exchange rates, interest rates, and growth in the economy are

expected to influence stock returns due to their effect on the firm’s cash flow streams.

Some studies argue that exchange rate risk is priced in stock index, suggesting no significant

impact of exchange rates on the stock market (see Mishra, 2005; Chen & Chen, 2012); some

confirm short-run interaction between exchange rates and the stock market (see Semra &

Ayhan, 2010); another group confirms a long-run relationship between the variables (see Naik

& Padhi, 2012); while others use Granger causality to determine whether the relationship

between the variables follows that of a portfolio or traditional approach. Some researchers

argue that currency depreciation is a product of a depressed economy; however, this is not

always the case. Although the effect of currency depreciation on a firm’s cash flow is a function

of how elastic the company’s products are to the exchange rate, at the same time, depreciation

of a currency decreases the prices of export produce which increases the demand for the firm’s

products abroad.

The Fisher effect (1930) suggests that a negative relationship is expected between interest rate

and the stock market. Loans are the primary source of external financing of firms, and a high

interest rate is projected to put an upward pressure on the servicing of loans. When their cost

of borrowing increases, firms pay more to finance their debts which affect earnings. On the

other hand, an increase in interest rates can be used as a tool to attract investors who are in

search of higher returns on their portfolios to the market. Some researchers have shown

simultaneity in the stock market development and economic growth (Peiro, 2016); this suggests

that returns on stocks are expected to be on the rise when there is expansion in the economy.

1.2 Purpose of the Study

The impact of the 1997 Asian crisis in Indonesia and the impact of the 2008 global financial

crisis in Mexico and Turkey are historical occurrences which prompted the need for examining

the channels through which external factors influence emerging countries (see Figures 2, 3 and

5). It is, therefore, of great importance for financial analysts, policymakers and

macroeconomists to understand the dynamic behaviour of the stock market concerning changes

in global macroeconomic factors. Movement and variability of asset prices are of great

importance to investors, who are also interested in knowing the various domestic factors or

events that determine or alter the persistent variability or movement over time (Malik, 2004).

High volatility in the stock market has led to the assumption that the stock market does not

work in a vacuum but rather responds to various factors, which could be internal or external.

To date, the literature that uses multifactor models to predict movement in emerging markets

focuses primarily on whether stock returns are predicted using financial ratios, microeconomic

and domestic macroeconomic factors (Kandir & Arioglu, 2014; Narayan et al., 2014). The

literature has not examined the potential impact of global macroeconomic events on emerging

stock markets. Domestic macroeconomic factors that are responsible for volatility in the stock

market can be classified into economic shocks and economic forces. Economic shocks are

unpredictable or can be expressed as the sudden impact of an occurrence that affects the

economy such as wars, natural disasters, terrorism and political instabilities (see Rose, 2009).

On the other hand, economic forces are described as factors that determine the competitiveness

of the environment in which firms that are listed on the stock market operate (Shanken &

Weinstein, 2006); these forces could be anticipated, managed or controlled by the government.

Such examples are levels of interest rates, inflation rates, exchange rates, economic growth

(measured using industrial production), money supply, foreign reserves, treasury bill rates and

oil prices, but to mention a few.

Engel & Rangel (2005) have identified emerging markets as the most volatile stock markets

and, therefore, have suggested that emerging stock markets are more likely to be volatile; they

show that low-frequency volatility is greater when factors such as short-term interest rates,

inflation and gross domestic product are more volatile. Some factors which are mainly business

environmental factors are identified as factors that can also impact the stock market. These are

dividend yields, taxes, price to earnings ratio, government regulations, mergers and

acquisitions, company earnings report, development of innovations, high level of competition,

technology, and company cash flows or profits.

Investors in the United States of America are the world’s largest international investors, and

they held up to 30% of the world’s investment portfolio assets before the global financial crisis

(GFC). It is, therefore, likely for changes in monetary policy in the US economy to influence

stock markets around the world (Hayo et al., 2012). The Federal Reserve implemented

quantitative easing to restore the US economy after the 2008 GFC, which saw the Federal funds

rate to near-zero rates. Although before this time there has been an increase in capital flow to

emerging markets, the near-zero rate aided capital flight as many investors took advantage of

the opportunity to invest heavily in emerging economies (Marwah et al., 2015). There are

higher risks and returns in emerging markets as compared to matured markets (Harvey, 1995);

that is why, despite exchange rate and default risk, investing in emerging markets is still

perceived to be attractive.

The Federal Reserve, after the recovery of the economy and giving account of the rate at which

capital flight increased, announced through the then Chairman, Mr. Ben Bernanke in 2013, that

they might taper the quantitative easing; the tapering talk caused capital outflow, an increase

in interest rate and the depreciation of currencies in emerging markets (Marwah et al., 2015).

The impact of the tapering talk was prominent in countries with huge current account deficits

and large borrowings in foreign currency. The reaction of emerging stock markets to the

tapering news is a source of concern to most fund and portfolio investors as they are in doubt

of the extent of the impact on emerging economies.

Emerging markets are the recipients of global investment and among the global consumers and

producers of commodities; therefore, changes in global economic factors such as commodity

prices could be a channel through which the world’s financial and economic conditions, like

the GFC, are transmitted to emerging stock markets (Mensi et al., 2014). Nearly half of the

growth in Sub-Sahara Africa is caused by commodity prices (Deaton, 1999).

There is increasing attention by academics, individuals, institutions and the general public on

capital markets after the notable performance of emerging economies in the world stock

market, hence the reason for looking beyond the impact of domestic factors and including

global factors in the research. The lagged effect of macroeconomic factors on stock price

movement should be expected to influence decisions made by investors as most of their

decisions are based on expectations, and if expectations are realized then there will be no

unexpected changes in prices of stock (Gunsel & Cukur, 2007). These authors confirm that this

suggestion can only be valid in an efficient market, whereas most markets are not efficient,

according to the arbitrage pricing theory, because markets respond to changes in

macroeconomic factors. Efficiency measurement of the stock market is, therefore, of great

importance to policymakers, investors and major players who ensure real long-term capital in

the economy (Alam & Uddin, 2009).

Maysami et al. (2004) and Asaolu & Ogunmuyiwa (2011) opined that the stock market respond

to movement in macroeconomic factors, but the signs and causal relationships of some of these

variables could vary due to country variations, change in different sample periods as well as

data frequency; thus, more in-depth studies are needed to be able to understand the

macroeconomic factors that might impact certain stock markets. Moreover, findings in a

particular emerging country cannot be generalized. We focus on emerging market returns

because, emerging markets currently make up 10 out of the 20 largest economies of the world

and are recognised as the global investment opportunity set (Graham et al., 2016).

Policymakers, investors and the investment community in this changing global environment

have found the relationship between exchange rates and stock markets to be of great

importance. Currency is often included in portfolios held by mutual funds, hedge funds and

professional portfolio managers; therefore, the relationship between the variables would help

managers to manage risk efficiency (Kutty, 2010). Perfect mobility of capital due to the

globally intertwined economy has helped create investment opportunities for multinational

corporations in developing countries.

Industrial production is a measure of economic growth, and cash flows of firms are expected

to respond positively to expansion in the economy; therefore, a positive relationship is expected

between stock returns and industrial production. Policymakers would benefit from the results

of the study in the preparation of new capital market policies or in modifying existing policies

in the interest of the MINT stock markets.

1.3 Origin and Rationale behind the Formation of the MINT Countries

In 2001, Brazil, Russia, India and China ‘BRIC’ were identified and the group accounted for

more than a quarter of the world’s land mass and also over 40% of the world’s population (CIA

Factbook, 2012). BRIC countries were performing as expected until recently. Now their

economies are taking a turn for the worse for various reasons, and many investors are

contemplating if they will be able to continue the rapid growth that had been witnessed in past

years prompting the need for a new investment destination to be identified. The MINT

acronym, a concept floated for the first time in late 2013, represents the loose grouping of

Mexico, Indonesia, Nigeria and Turkey. The idea was coined in a paper by Goldman Sachs as

part of an exercise to further forecast global economic trends after the identification of BRIC

countries. O’Neill (2013) mentioned that emerging countries are formulated and examined as

a result of a quest to shift global investment power from G7 (Canada, Italy, US, UK, Germany,

France and Japan) economies to more viable destinations (Rana, 2009). The MINT countries

are strategically placed to take advantage of large markets near them geographically; Mexico

is on the doorstep of America, Indonesia shares the same region as China, and Nigeria is the

hub of Africa’s economy while Turkey is close to the European Union. All the countries except

Nigeria are members of the G20 group. The IMF graph below shows the MINT countries’

GDP growth forecast in comparison with the US GDP growth.

Figure 1 Annual Percentage Growth Rate (MINT and the US)

Figure 1 suggests that the MINT countries’ growth rate surpasses that of the US, and it is

expected to maintain this growth until 2018. It is therefore of great importance to review

demographic information about the countries.

Table 1 Statistical data on the MINT countries in 2016

Countries

Population (millions) Continent GDP position World GDP position GDP (Per capital current

prices Millions of US$)

Mexico 127.54 2nd (North America) 15th 1,045,998

Indonesia 261.12 5th (Asia) 16th 932,259

Nigeria 185.99 1st (Africa) 23rd 405,082

Turkey 79.51 6th (Europe) 17th 857,748

Source: World Bank data 2017. http://databank.worldbank.org/data/reports.aspx?source=2&country=MEX&series=&period=

Mexico:

It is the fifth largest country in America and the thirteenth largest independent nation in the

world; Mexico is the tenth largest

producer of oil in the world. The

country is also a major producer

of silver. It is the first Latin

American member of the

Organisation for Economic Co-

operation and Development

(OECD) and was listed by the

World Bank as an upper-middle-

income country in 2011 (World

Bank IBRD, 2011). As shown in

Table 1 above, it has the fifteenth largest nominal gross domestic product, and the economy is

linked to 40 free trade partners championed by the United States of America with up to 78%

of its total export. Mexico is considered a newly industrialized economy. Mexico is a major

oil-exporting country with increasing diversified and competitive economy; it is the sixth

largest oil-exporting nation in the world and is one of the important foreign sources of oil to

the US. Mexico’s service sector accounts for up to 61% of its GDP, followed by industry with

34% and agriculture with 4%, and these industries are mostly tied to the US economy (Klepak,

2008).The debt crisis in 1983 slowed the growth of the economy to -4.2%, also the currency

(Peso) crisis of 1995 deepened the GDP growth rate to -6.2%. However, the economy

recovered significantly as is evidently seen by the speed of growth shown between 1995 and

2002 in the in-set graph (Data Source -World Bank World Development Indicator (graphed by

the author); the GDP growth from 1995 to 2002 was up to 5.1%, and this increase was

accompanied by foreign investments and European migration. The global financial recession

of 2008 saw the GDP growth rate to drop from 5.0% in 2006 to -4.7% in 2009; the recession

was very contagious as it spread from its principal exporting country (US) thereby having a

significant and immediate impact on the overall economy.

1990 1995 2000 2005 2010 2015

Mexico GDP growth rate annual %

Mexico

Figure 2 Mexico GDP Growth Rate Annual %

Indonesia: It is the fourth most populous country in the world and ranked sixteenth in the GDP

world ranking by the World Bank; it has a mixed economy with public and private sectors

playing significant roles in the development of the country (Durotoye, 2014). It is also

classified as the largest economy in South Asia and is a member of the G20 and Next-11

emerging economies. The country contributes up to 2.3% of global economic output (The

Jakarta Post, 2014). Indonesia is the largest economy in Southeast Asia and the world’s fourth

most populous nation after China, India and the US. The country is endowed with extensive

natural resources and its major export markets are Japan, Singapore, the US and China.

Indonesia has a diversified export base, and it is the largest tin market in the world.

Indonesia was hit hardest by the 1997 Asian financial crisis (AFC), which started as a result of

Thailand floating their foreign currency to support their fixed exchange rate regime. The

country was said to have acquired a burden of external debt, which landed it in bankruptcy

before the total collapse of the Thai Baht. The whole of the Asia region was affected as their

currencies also slumped, which resulted in the devaluation of asset prices (Leightner, 2007).

Indonesia’s GDP growth fell from 4.7% in 1997 to -13.1% in early 1998. The intervention of

the IMF, which initiated a 40 billion-US Dollar programme to bail out the currencies of the

most affected countries, helped a great deal and enabled a quick recovery to the economy in

subsequent years; the country maintained high interest rates to attract foreign investors who

may be seeking a high rate of return (Lane, 1999). This, however, resulted in large capital

inflow into the economy thereby giving asset prices a boost. Figure 2 shows the impact of the

AFC and the global financial crisis in 1998 and 2008 respectively; the country experienced

slow growth in GDP from 6.0% in 2008 to 4.6% in 2009. The economy seems to have

recovered as the growth rate afterwards hovered around an average of 6% per annum.

Figure 3 Indonesia GDP Growth Rate Annual %

Nigeria: It is the largest economy in Africa with GDP of over 500 billion US Dollars and the

twenty-first largest economy in the world (IMF, 2016). Nigeria is known for its abundance of

natural resources (crude oil) which is the source of growth in the economy; Nigeria was listed

as one of the Next-11 emerging economies. It is the seventh most populous country in the world

with over 180 million people (Goldman Sachs 2007; OPEC, 2016). Nigeria has the United

States of America as a large trading partner with 11% of its import supply (Bureau of

Economic, Energy and Business Affairs, 2010). Nigeria is the most populous country in Africa

and the largest producer of oil in the region. The growth in the economy has shifted attention

to the country as a major destination for investment (Durotoye, 2014).

The military regime in the late 1980s and early 1990s hindered economic development, but

the emergence of the democratic government in 1999 put the country back into exploring its

economic potentials (Ajayi, 2013). The accumulated foreign debt that was acquired during the

1970 oil boom was used to finance infrastructural investments; therefore, the country struggled

during the 1980s oil glut as external debts were accumulated and interest and penalties of

default on loans were overwhelming. By 2006, the democratic government was able to free

itself from the Paris Club debt (Romanus, 2014). Nigeria’s GDP growth rate has been in the

range of 6% – 8% on an annual basis.

Figure 4 Nigeria GDP Growth Rate Annual %

Turkey: It is an industrialized economy and a founding member of the Organisation for

Economic Co-operation and Development (OECD) (CIA Factbook, 2012). Turkey is also a

major exporting country to the European Union (EU) countries. Turkey’s trade with the EU

accounts for up to 50% of the country’s total exports while the EU share of total imports to

Turkey is about 45% (Durotoye, 2014). In the 1960s and 1970s, Turkey’s exports were mainly

agriculture and raw materials but, presently, about 80% of its total exports are manufactured

goods (Acar, 2014). Most foreign firms in Turkey are from the EU region. The government of

the country has experienced coups d’état in the early 1960s, 1970s, 1980s and late 1990s (Ozan,

2012; Ayhan & Won, 2014). In all these times, the country had a fragile financial sector.

However, liberalization of the government has given the country an unyielding economy as

well as political stability. The country experienced a financial crisis in 1999 and 2001, and the

GFC in 2008. The cause of the crisis in the late 1990s was due to the acquisition of a large

public-sector deficit and corruption, which led to inflation and a weak financial sector; as a

result, reform was initiated in the country after 2001 (Kaya & Yilar, 2011).

This, however, helped to keep the inflation rate to as low as a single digit and contributed to

building the confidence of investors and foreign investment development (World Bank, 2005).

The GDP growth rate from 2002 to 2007 was an average of 6.8% annually which was classified

as one of the fastest in the world at the time. The growth was slow again during the 2008 global

financial crisis but has since recovered to an average of 8% in 2010 to date (CIA Factbook,

2012).

A close look at the emerging markets suggests that the group of countries are characterized by

enviable geographical placements which give them an advantage of large export markets.

Investors’ attention has been shifted from emerging markets like BRIC to ‘Frontier Markets’

(Pre-emerging markets). This is the reason behind the formation of MINT countries which has

been presented as a new potential investment destination. Magalhaes (2013) in his paper

forecasted that MINT countries could rise into the ranks of the top 10 global economies by

Figure 5 Turkey GDP Growth Rate Annual %

1.3.1 Similarities in the MINT countries

The increase in the number of papers on the debate of asset pricing has increased, the rise is

as a result of emerging market providing diversification benefits to investors with new

investment opportunities as there is a low correlation between developed and emerging

markets. The combination of this, with higher expected returns triggers foreign investor’s

interest. As investors look out for suitable emerging markets to invest, it is worthy of note

that there is no clear definition of what qualifies a country to be classified as an emerging

market. MSCI classifies Mexico, Indonesia and Turkey as emerging markets and group

Nigeria with frontier markets. World Bank however classified the MINT countries as

developing economies (World Bank, 2014).

The BRICS countries when identified received a great attention, these countries performed to

expectations until recently. The MINT countries are also growing and emerging economies

like the BRICS, but they are not as powerful or large. The grouping of the ‘bloc’ is different

as there are no formal cooperation as they are in different continents unlike BRICS where

China and India are in the same continent and could share common risk.

The question that comes to mind is why these set of countries are important or better still

what contributions do they have to the world economy. The best way around these questions

is to analyse the economic, political and demographics of the MINT countries. The MINT

countries are struggling with several issues; Mexico has national security issues, deforestation

and lack of clean water. Indonesia also has the issue of deforestation and the country

experiences occasional natural hazards. The country still struggles with poverty, inadequate

infrastructure, unemployment and corruption; Nigeria even though the giant of Africa, is

prone to natural hazards like flooding and droughts. The country has also been hobbled by

restrictive trade policies, insecurity, inadequate power supply and pervasive corruption;

Turkey has experienced several terrorist violence in its major cities, this has affected the

tourism sector which is a good source of revenue in the country. The country has witnessed

attempted coup and the northern part of the country experience severe earthquake.

Having pointed out some challenges the MINT countries face, they share a number of factors

in common which makes them particularly attractive to international investors. They also

have high potentials for growth and development in the near future. Some of the features

identified are as follows;

The MINT countries have dynamic population with more than 50% of their population in the

working age bracket (15 and 54 years). The median age population (2017 est.) for the

countries to show the evidence of young population are Mexico 28 years, Indonesia 29.9

years, Nigeria 18.3 years; Turkey 30.5 years compared to ageing and shrinking population of

some emerging countries like Russia 39.6 years, China 37.4 years, not to mention developed

countries like the United States 38.1 years and the United Kingdom 40.5 years. A young

population implies an increased workforce in the medium term which is a crucial element in

terms of the foreign direct investment and employment creation. The young population of

these group of countries are regarded as future as well as present assets (Adibe, 2014).

Youths contribute through their intellects and can uniquely propel any society to its next

level. In as much as the involvement of the young population in designing and building the

society is a crucial part of societal development, the young men and women in the MINT

countries are faced with many challenges which the government and the private sector should

deal with if the youth would become the engine to propel economic growth in these countries

(Durotoye, 2014). There is a need to promote job creation to achieve inclusive growth in the

economy (World Bank, 2017).

The MINT countries with the exception of Turkey whose oil and gas production are not

sufficient for their needs, hence why they are net importers of both. All other countries are

characterised by the presence of natural resources such as gas and oil. Indonesia has gas as an

important resource while oil is a strategic resource to Nigeria. In Mexico, huge quantity of

silver is extracted with natural oil and gas. Mexico is also a member club of countries with

nuclear plants for energy production. Turkey is a natural resource importer but has its

industrial products exports booming. Mexico has received the highest foreign direct

investment in central America, Indonesia in South – Eastern Asia, Nigeria the highest in

Africa and Turkey in West Asia (World Bank, 2013).

There have not been many studies dedicated to the MINT countries like the BRICS, this is

understandable as the BRICS emerged as a bloc long before the MINT. However, Durotoye

(2014) explored the challenges as well as prospects of the MINT; Akap et al. (2014) also

examines foreign direct investment in fast – growing economies in both MINT and BRICS

countries. Ozturk & Yildirim (2015) stated that there may be some advantages in the MINT

that could propel the bloc to be in the ten largest economy in the world.

1.4 Statement of the Problem

Since the 1950s, investment decision has been a difficult task. Pilbeam (2010) notably suggests

that there have been significant changes in the world stock exchange markets since the 1960s,

which have made stock markets to be at the forefront of financial headlines. In an article in the

notable financial magazine – The Economist (2006) – there have been discussions about the

fall in investment of developed economy assets; a suggestion was proposed that the shortfall

could be bridged by adding investors from emerging economies. For this to be achievable,

consistent growth in the emerging markets needs to be maintained, in particular through

external investors. Potential investors are therefore beginning to view emerging markets as part

of their investment portfolios; it will, therefore, be of great importance for them to know how

emerging markets react to macroeconomic shocks or changes. Bubbles in the stock market

have had a huge impact on the overall economy, and certain monetary and fiscal policies by

the government of a nation are identified as a tool that can ‘steer the wheel’ of the stock market.

News and events around the world are major movers and shakers of stock markets. Most

investors are therefore glued to updates on policy changes that could affect returns on their

investments. Investors are accustomed to the system of making investment decisions based on

the return of an asset and they also speculate specific risks on their investment but have no

measure for it. The major concern for investors is to ascertain the direction of the movement

of the stock market in an attempt to be able to predict the potential returns on investment based

on some variables. Researchers (see Adam & Tweneboah, 2008; Kutty, 2010) have done

commendable work by determining how sensitive stock returns and prices are to interest rate

and exchange rate. They also tried to identify the relationship between the economic well-being

of a nation and the stock market (see Castillo-Ponce et al., 2015; Muhammad, 2011).

Fama (1981) and Chen et al. (1986) have urged researchers to move from efficient market

hypothesis assumptions to arbitrage pricing theory (APT). The acceptance of APT has made

finance researchers digress from the capital asset pricing theory model simplicity for

potentially large and undefined macroeconomic variables to explain stock returns (see Ikoku

& Okany, 2014). The intuition that stock market returns are determined by more than one

variable has motivated a growing number of investigations of the impact of macroeconomic

variables on the stock market.

Although the APT is a widely acceptable theory in finance, it exhibits a shortcoming by not

specifying the number of macroeconomic variables to be included when a research of this type

is carried out, which has resulted in contradictory findings by researchers in this field. The

capital asset pricing model (CAPM) developed by Sharpe (1964) is a one-factor model, but it

attracted critical comments because it is seen as a theory with one explanatory variable (which

is the risk premium) as a determinant of the market as a whole.

The presence of powerful statistical software which is readily available to facilitate regression

analysis on large data sets of stock returns and prices has given researchers the freedom to

include as many variables as deemed fit. Jamal (2014) argued that researchers could not agree

on which macroeconomic factors, or how many, to include in their research; moreover, some

have been involved in data dredging. This research would, therefore, propose six basic

macroeconomic factors which are a combination of randomly selected variables from the

literature and global macroeconomic factors, such as interest rate, economic growth, exchange

rate, commodity price index, MSCI Global equity index and Federal funds rate that could

influence emerging stock markets.

Although the numbers of researchers in this area have almost exhausted the use of various

domestic macroeconomic variables, some have included the stock market of countries’ export

partners; there’s still a need to explain the impact of changes in monetary policy of trade

partners on emerging stock markets. The US is one of the biggest economies in the world;

financial and economic events in the US could in one way or the other be transferred to

emerging markets. The impact of the global financial crisis (GFC) on the world economy has

given a meaning to this phrase in business ‘when the US sneezes, the world catches a cold’

(see Saudi 2012). Some unexplainable changes in stock movement can, therefore, be traced to

policy changes in the US economy.

The high rate of capital flight from developed to emerging economies has aided development

in most of the markets. Foreign investors who are the major source of capital movement across

the globe are mostly interested in knowing the co-movement of emerging markets with global

factors, given that speculation, investment, and risk diversification may arise (Mensi et al.,

2014). Assuming that the identification of common factors that influence stock markets is still

a challenge for academics and portfolio managers, the inclusion of global factors should

enhance the investigation on whether stock market factors are global. Some researchers have

investigated the impact of informal policy announcement on emerging markets and have

empirically shown its significance (see Hayo et al., 2012). The inclusion of formal monetary

policy and commodity prices as global factors would give answers to whether monetary policy

changes have significant economic impact on equity prices in emerging markets; if so, we

examine whether markets react differently to changes in the policy.

The MINT countries have not received considerable attention in the area of investigating the

impact of global factors on the stock markets. This research, therefore, considers global factors

alongside domestic macroeconomic factors to know whether dependence exists between the

equity market of the MINT group and important global economic factors.

1.5 Research Aim and Objectives

The main aim of this research is to critically compare and analyse the response of MINT

country stock markets to the same set of global and country macroeconomic factors using

multi-statistical analysis with a view to developing a set of global factors that can be used to

predict the stock markets in Mexico, Indonesia, Nigeria and Turkey. This research is unique as

researchers have not included global factors in their quest to examine the response of the MINT

countries’ stock markets to changes in external macroeconomic factors. This research will

provide a clear justification for the restriction of macroeconomic variables to six (which consist

of three domestic and three global factors) and would employ various statistical techniques,

such as cointegration through the error correction model to analyse the short and long-run

equilibrium relationship that exists between the stock market and selected macroeconomic

factors. This research goes further by utilizing the impulse response function and variance

decomposition to trace the effect of shocks introduced to returns of stock and also test the

transmission of periodic shocks respectively, as this will help to envisage the longevity of the

impact of various macroeconomic shocks.

To be able to achieve the above aim, it is important to state fundamental objectives that would

help to itemise the process in a systematic form. The objectives are as follows:

To review the various theoretical frameworks such as arbitrage pricing theory (APT),

efficient market hypothesis (EMH) and discounted cash flow (DCF) technique, and

analyse the empirical findings by various researchers on the impact of macroeconomic

factors on emerging markets as a prelude to this research.

To provide an evidence of efficiency of the MINT countries’ stock markets using trend

analysis and variance ratio tests.

To explain the reliability of the vector error correction model and cointegration in

estimating short-run and long-term equilibrium relationships between stock market

indices and macroeconomic variables.

To determine whether the MINT countries’ stock markets are integrated or segmented.

To develop a table of the stock markets according to their response to each country and

global macroeconomic variables that are selected, to suggest recommendations for

investment and policymaking in the MINT countries.

1.6 Research Questions

This research is set to give the solution to core questions, and this would enable the author to

stick to the main aim of the thesis. They are as follows:

Do the MINT countries’ stock markets exhibit a weak form of market efficiency?

Are there short- and long-run equilibrium relationships between stock markets and

macroeconomic variables in the MINT countries?

To what extent do the MINT stock markets show sufficient integration or

segmentation?

How do the stock markets respond to each country and global macroeconomic variable

proposed?

1.7 Original Contribution

Ben Bernanke on 22 May 2013, while giving his testimony to the US Congress, raised the

possibility of tapering (this is where the term ‘taper tantrum’ 4 originated from). This

testimony, when studied by Eichengreen & Gupta (2015), had a sharply negative impact on the

financial and economic conditions of emerging markets. In fact, commentators predicted that

emerging countries might be heading towards a similar crisis to that experienced in Mexico

and Asia in 1994 and 1998 respectively. Their results, however, showed different responses of

countries to the news. The impact of informal communication channels, especially speeches by

top members in the Federal Reserve Bank, is of great importance to foreign equity markets

4 ‘Taper tantrum’ is the word used to describe the reaction of the market to Ben Bernanke’s comment.

(Hayo et al., 2012); if the news of tapering could cause a shock that is regarded as sharp and

large, how much impact should one expect if actual increment is implemented?

The study above gave rise to an ongoing debate in the field of finance and economics as to

whether economic activities in mature markets and those of emerging markets are integrated.

Goh et al. (2013) in their findings suggest that the Chinese stock market is segmented from the

American global market. The findings of Huang et al. (1996) substantiated this when they

established no causal or cointegrating relationship between American and Chinese stock

markets. However, this has been debunked when Johansson (2009), using sample periods after

the admission of China into the World Trade Organisation (WTO) in December 2001, found

evidence suggesting an increasing level of integration of the Chinese stock market with major

financial markets in the world.

As soon as the BRIC countries were identified as the newly industrialised economies,

international investors began to see investment in BRIC countries as viable alternatives

(O'Neill, 2013). The global economic crisis has given investors the need to think of means to

diversify their investment portfolios across different geographical regions (Marwah et al.,

2015). BRIC countries benefited greatly when their economy improved due to stock market

development; for the MINT countries to enjoy a similar benefit, they require a clear

understanding of the extent that global and domestic macroeconomic policies have on their

markets. This study is important to help ascertain whether the stock markets in the MINT

countries could act as a sure hub for international investors. The focus on the MINT markets

as the promising candidate is committed to shielding international investors from unexpected

shocks in the return on their investments.

The study starts with a comprehensive review of existing literature on the relationship between

stock markets and macroeconomic variables and then identifying gaps in the literature. If the

gap identified is promising, the researcher develops the hypothesis and specifies the model. As

soon as the hypothesis and model are developed, the author proceeds to the collection of data

for testing hypothesis and estimation. Results of the findings are compared to literature and

implications of the findings can be suggested as well as ideas for further research on the topic.

This research focuses on three country macroeconomic variables, which are exchange rates,

industrial production (as a measure of economic growth) and interest rates. It also includes

three global factors, i.e. commodity price index, MSCI world equity index and Federal funds

rate, as major determinants of stock returns in MINT countries. The data employed in this study

is secondary. The macroeconomic variables are retrieved from the Federal Bank of St. Louis,

while the stock market indices are extracted from Yahoo Finance. Most of the data collected

for Nigeria is from the Central Bank of Nigeria website. Monthly data between January 1993

and December 2014 has been employed for the model estimation.

The empirical models in this study are estimated using some statistical techniques for time

series data, which includes ARDL cointegration, error correction model, impulse response

function (IRF) and Granger causality estimation. The model specification outcome determines

the type of estimator employed. Diagnostic tests are carried out to ensure the reliability of the

estimated model.

The research tries to consolidate results of the estimated model so as to have an unbiased

outcome, as the most important thing is to avoid where varying results are presented on the

same model due to multiple statistical tests. The cointegration, vector autoregressive (VAR),

vector error correction model (VECM), impulse response function (IRF), variance

decomposition (VDC) and Granger causality output are summarised. The macroeconomic

variables are reported according to their importance in influencing stock markets in the short

and long run. The use of multiple statistical tests to identify variables that have the most impact

is what makes this research a unique one. This research will compare the short and long-run

impact of global and domestic variables in the MINT stock markets. The outcome of this

research should be able to aid foreign investors, portfolio managers and policymakers in

decision making.

1.8 Organisation of the Study

Chapter 1: Introduction

This thesis contains seven chapters; the first chapter introduces the background of the

importance of research carried out on emerging stock markets and how theories express the

interactions between macroeconomic factors and the stock market. The chapter also describes

the demographics of the MINT countries and the reason behind the formulation of the group

of countries. The main aim and objectives of the study are established, and some research

questions are posed from the objectives to meet the aim that is set for this research work. The

primary contributions of this research piece are also identified.

Chapter 2: Literature Review

The second chapter is the literature review. This begins with the historical background of how

the MINT stock markets evolved; it also examines comprehensively and critically theoretical

foundations of market behaviours. Empirical studies of the interaction between stock returns

and macroeconomic factors are also reviewed. Besides, gaps in the literature are identified, and

new research ideas are suggested. Empirical results of researchers are tabulated to capture the

model used; this provides an advanced methodology for the issue.

Chapter 3: Conceptual Framework

It presents definitions of each of the macroeconomic factors and gives a broad perspective on

how the research is set to specify the model for estimation. A broad model flow chart is used

to illustrate the direction of the thesis. The specification of the entire model gives an idea of

the restriction the researcher has set by identifying the variables of interest. The dependent

variables (stock returns), independent variables and macroeconomic variables (exchange rate,

interest rate, commodity price index, Federal funds rate, MSCI global equity index and

industrial production) will form the basis of the model aimed at explaining the concept of the

research work.

Chapter 4: Research Methodology

This chapter begins with an elaborate description of the research design which encompasses

the philosophy adopted, approach, time horizon and techniques of data collection. It also tries

to link each of the macroeconomic variables selected with stock returns. The chapter provides

an empirical specification on how to go about the broad model estimation explained in Chapter

three. It gives a step-by-step approach on how the model is estimated while explaining the

advantages the selected techniques have over the others that have been used in the literature.

Some of the limitations of the techniques are identified and ways to eliminate them explained

accordingly.

Chapter 5: Analysis

This section presents the results derived from the software that is used for model estimation.

The chapter takes each of the step-by-step approaches as stated in the previous chapter to

analyse data and the interpretation of the output given based on the stated econometric rules.

Graphical illustrations and numerical values are attributed to the interactions between stock

returns and macroeconomic variables.

Chapter 6: Discussion

This chapter begins by comparing the MINT countries’ stock market variability and giving a

suggestion on the market that is most volatile in the group. It also explains, elaborately, the

long-run findings in Chapter five and supports the findings with theories mentioned in Chapter

two. Results are also compared to the ones in the literature. The IRF is used to interpret the

VAR and VECM estimations to have a clearer view of the results. Two techniques (variance

decomposition and Granger causality) are used to narrow down the findings and suggestions

of leading variables that impact the stock markets. This chapter also seeks to establish whether

the research objectives and hypothesis have been met.

Chapter 7: Summary, conclusion, recommendation and opportunity for further research

This chapter begins by giving suggestions to each of the research questions stated and tackles

basic arguments of how the selection of variables influences results by showing similarities or

dissimilarities that are observed when results are compared to past findings. The summary

gives a recap of the results in this thesis, followed by the conclusion that can be reached. It also

suggests the practical implications of the findings. The author suggests some recommendations

to policymakers, portfolio managers, foreign and local investors on what to consider in their

decision making. The problems encountered that limit the research are also highlighted.

CHAPTER TWO

2.0 Literature review

2.1 Introduction

This section provides an overview of previous research on the relationship between stock

markets and macroeconomic factors. It introduces the framework that comprises the primary

focus of the research described in the previous chapter. This chapter briefly explores the

historical background of the establishment and operations of the MINT countries’ stock

markets. It also provides a brief overview of the literature on determinants of stock market

movement. Theories are critical as they are the basis on which research assumptions are

formulated; they are also useful as they explain possible linkages between variables. Therefore,

theories that explain market behaviour are critically reviewed, and suggestions are made to

help synthesize the interactions between macroeconomic factors and the stock market. This

part of the research looks into how some of the theories are critiqued and how academics have

moved from one theory to another over the years. Reviewing these ideas gives rise to

unanswered questions and gaps in the area of finance and economics. This study helps to

suggest how to go about some of these issues, especially in the area of systemic risk selection

in the arbitrage pricing theory. The author gives a bit of attention to the issue of market

segmentation and integration, which is an important argument that ensued in considering factor

selection in this type of research. Stock market return movement has emerged due to many

factors, and some of these factors occur due to globalization, liberalization of capital circulation

and improvement in communication and technology. These factors have contributed to the

increasing number of variables that determine the movement in various stock market prices.

The chapter explains how researchers have extensively tried to investigate the impact of

macroeconomic variables on emerging equity markets using some of the factors suggested in

the literature. Empirical evidence of past researchers helps provide the mechanism for

examining the validity of the relationship between stock markets and macroeconomic factors;

therefore, it is imperative to review both theoretical and empirical evidence before proceeding

to investigate how the selected macroeconomic factors influence stock markets in the MINT

countries.

Figure 6 Structure of the chapter

2.2. Historical background of the MINT stock exchange markets

Mexico, Indonesia, Nigeria and Turkey are all developing countries; however, each can boast

of more than one stock exchange in the history of their financial dispensation as compared to

some of their peers. This section will help to outline the inception, mergers, acquisition and

how stock markets have evolved over years. Stock markets in emerging economies started on

a fluctuant note but later gained momentum in the 1990s, unlike the developed markets.

Emerging stock markets have gained so much recognition in the international market, which

has made countries pursue policies and programs that aid the growth of the market. An

overview of the MINT countries’ stock markets is necessary as it would help to understand the

development process of each of the stock markets.

2.2.1 Overview of the Mexican Stock Exchange Market

The first sort of capital market to be introduced in Mexico happened in 1933 with the primary

aim of facilitating share transactions and pushing market expansion and development. It was

known as Bolsa Mexicana de Valores (BMV). Brokers in Mexico in 1894 were keen on having

a well-regularised institution to trade securities. This motive led to the formation of a National

Stock Exchange in 1894. Another set of brokers formed a competing establishment and named

it Stock Exchange of Mexico (Bolsa de Mexico). The two groups of brokers later reached

consensus and merged in 1895 retaining the name Bolsa de Mexico. Congress authorised this

Historical background of MINT stock exchange

markets

Theoretical underpinnings

Empirical evidences

institution in 1933 and the National Banking and Security Commission was established as a

supervisory body of the stock exchange market. As a result of growth in the commerce and

industry of Mexico’s economy, a second stock exchange was created (Maquiladoras) in the

northern part, and a third, named Occidental Stock Exchange, to serve the west. These two

newly formed stock exchange markets were performing poorly and were later compelled by

the Congress to merge and were named BMV (BMV, 2015). The summary of past events in

Mexico’s stock exchange is summarized in Table 2.

Table 2 Events in the Mexican Stock Market

Year Events

1850–1900 Mexico had the first main transaction, and

businesspeople and brokers were trading

behind closed doors. Borsa Mercantile de

Mexico is formed in 1886, and Borsa de

Mexico is established in 1895.

1901–1933 An economic crisis and a metal crisis caused

security inactivity after which Bolsa de

Valores de Mexico is inaugurated in 1908.

They operated in Uruguay until 1957 and, in

1933, new stock market activity started in

Mexico.

1934–1995 Guadalajara and Monterrey stock exchanges

were taken over and renamed Bolsa

Mexicana de Valores (BMV) in 1975, and

operations for fixed income market started.

1996–2002 Stocks were first to be traded electronically

in 1999, which resulted in the increase in the

number of deals in the stock market. A

service company was incorporated to hire,

administer and control the personnel in

Source: Group BMV website. www.bmv.com.mx

BMV has since then become fully operational. In 2001, foreign companies also began trading

in the exchange. By 2003, the global market had been made available, which allowed national

investors to have access to foreign securities. In 2006, the market was opened to foreign

investors enabling them to trade from any part of the world. BMV is the leading stock exchange

in Latin America regarding exchange traded funds. BMV with its expansion is also the biggest

market concerning market capitalization (WFE, 2010) in Latin America. Table 6 shows the

market capitalization, stock traded volume as well as the number of listed companies on the

Mexican stock exchange. The market capitalization has risen from US$ 243,054 billion in 2008

at the time of global recession to US$ 525,056 billion in 2012, which makes the stock exchange

the fifth largest stock market in America (UNCTAD, 2014).

2.2.2 Overview of the Indonesia Stock Exchange Market

The stock exchange in Indonesia was founded as far back as 1912 during the Dutch colony

and, due to the effects of World Wars I and II, there was several closures of the stock market.

The stock market was later re-opened in 1977 and supervised by the then Ministry of Finance.

In 1922, the exchange was privatised leaving Indonesia with two stock exchanges in 1995,

namely Jakarta and Surabaya stock exchange markets. The two later merged and were named

Indonesia Stock Exchange, which is also known as ‘Bursa Efek Indonesia’ (The Jakarta Post,

2012). The summary of event in the capital market is summarized in Table 3.

Table 3 Events in the Indonesia stock market

Year Events

1912 – 1939 The first stock exchange was built in Batavia

(Jakarta stock exchange, JSX) and was

closed between 1914 and 1918 due to World

War I. Batavia was re-opened in 1925 and

two new stock exchanges were established

namely Semarang and Subaraya. These two

later closed in 1939.

1940 – 1989 JSX also closed in 1942 during the World

War II; the stock market was re-activated in

1956 but was a bit dull with only 24

companies listed on it. In 1988, the

government deregulated banking and capital

market, and businesses were given ways to

go public. Subaraya also started operating at

the time.

1990 – 1995 Privatisation and the introduction of

automated trading system. The government

issued more regulations on the capital

market.

1996 – 2009 The introduction of scripless trading and the

implementation of a remote trading system in

JSX. Surabaya merged into JSX, and the

name later changed to Indonesia Stock

Exchange.

Source: Indonesia stock exchange website. www.IDX.co.id

The stock exchange has performed reasonably well, especially internationally as foreigners

owned up to 60% of its publicly traded stocks. As at 2012, the stock exchange had up to over

400 companies listed with a market capitalization of US$ 426.78 billion.

2.2.3 Overview of the Nigerian Stock Exchange (NSE)

The NSE was first established as the Lagos Stock Exchange in 1960 and kicked off operations

in 1961 with about 200 companies. In 1977, the stock exchange had its name changed to the

Nigerian Stock Exchange with a number of branches across the country. The Security and

Exchange Commission is the major regulator of the NSE. Its mandate is to regulate, make rules

and detect manipulations in trading practices (SEC, 2011). In 1999, NSE started its operations

through an automated trading system, which helped to connect dealers to a shared server. The

Nigerian government abolished the legislation that prevents a free flow of foreign capital into

the country thereby encouraging foreign investors into the system. It also allowed foreign

dealers to be listed on the NSE market (NSE, 2014). The events in the NSE since its inception

can be seen in Table 4.

Table 4 Events in the Nigerian Stock Market

Year Events

1960–1985 Lagos Stock Exchange (LSE) was formed,

and trading commenced in 1961. In 1975, in

an attempt to revive the financial system in

Nigeria, the name changed to Nigerian Stock

Exchange (NSE). Launched All-share index

and the opening of various branches across

the country.

1986–2000 Nigeria’s capital market was deregulated,

and Federal government internationalized the

market in 1975. The system puts in place

various controls and administrations for

foreign investors. In 1997, NSE transited to

an automated trading system, and cross-

border listings started.

2001–2008 Admission of the Federal government bond;

new equity sub-sectors were created, such as

mortgage, maritime and oil services;

companies were introduced as equity sub-

sector which revived foreign listings.

Telecommunication companies were also

listed which made the market exceed its

annual turnover at the time.

2009–2012 Regulatory body intervened in the

management of NSE and segmentation was

introduced to help reduce sectors. Also,

money making program commenced in the

market.

Source: The Nigerian Stock Exchange website. www.nse.com.ng

NSE witnessed swings in recent years but the market in the 1990s and early 2000s was said to

have performed extraordinarily. There was up to 900%-point increase between 1990 and 1999.

The NSE leapt from a record low in market capitalization of US$ 2,939 billion in late 1999 to

US$ 84,894.6 billion in 2007 just before the global financial crisis. The recession, however,

led to the sharp downturn from US$ 84,894.6 billion in 2007 to US$ 48,062.3 billion in 2008

(WFE, 2010) and this continued to deepen in 2009.

2.2.4 Overview of the Stock Market in Turkey

The history of the organized security market in Turkey has its root from the establishment of

Dersaadet Security Exchange in 1866 after the Crimean war. The market attracted mostly

European investors who were on the lookout for higher returns in the Ottoman market. The

stock exchange adopted a new name (Istanbul Securities and Foreign Exchange Bourse) as a

result of the newly created Turkish Republic enacted in 1929. The success of the market had

its challenges due to the 1929 Great Depression and World War II at the time. By the early

1980s, the Turkish capital market experienced improvements, which led to the need for a

regulatory body, ‘Capital Market Board’, in 1981. The single stock exchange is the Borsa

Istanbul stock market, which was founded in 1986 with 320 national companies. Combining

the derivatives exchange of Turkey, the Istanbul gold as well as the Istanbul Stock Exchange

formed the market. The major shareholder in the stock market is the government of Turkey

with 49% (Holley, 2013). Table 5 shows the events of the stock market in Turkey.

Table 5 Events in the Turkish Stock Exchange Market

Year Events

1985 – 1992 Borsa Istanbul Stock Exchange commenced

trading in 1986, and foreign investors were

allowed to purchase and sell securities in

1987. Bill and bonds market were initiated in

1991 when calculations of indices started. In

1992, the stock market changed into an

independent company.

1993–1995 Repo market was introduced; computerized

trading also started at the time. Federation of

Euro-Asian Stock Exchanges was

established in 1995 and Istanbul Menkul

Kiymetler Borsasi (IMKB) was recognised

as a relevant foreign investment market.

1996–2001 Initiating banking services and the

introduction of new stock indices in 1997, a

new computerized trading system was

developed and a memorandum of co-

operation and understanding was signed

between the market, London Stock Exchange

and Korea Stock Exchange respectively.

2002–2012 The wider area of the network was covered;

IMKB and sub-markets were closed and re-

opened in 2009; and markets were offered for

qualified investors in 2012. Equity, repo and

derivative markets were launched.

Source: Boras Istanbul website. www.borsaistanbul.com

Table 6 MINT Stock Market Data

ry MEX MEX MEX MEX IDN IDN IDN IDN NGA NGA NGA NGA TUR TUR TUR TUR

Market

capitaliz

ation of

listed

companies (% of

Stocks

traded,

total value (%

of GDP)

companies

, total

Domesti

c Market

Capitaliz

('000 000)

Market

capitalizat

ion of

listed

companies (% of

Stocks

traded,

total value (%

of GDP)

Listed

companies,

Domestic

Market

Capitalization ('000

000) USD

Market

capitaliz

ation of

listed

companies (% of

Stocks

traded, total value (% of

Listed

domest

companies,

Domesti

c Market

Capitaliz

('000 000)

Market

capitalizat

ion of

listed

companies (% of

Stocks

traded,

total value (% of

Listed

companies,

Domestic

Market

Capitalization ('000 000)

1996 26.8089 10.8303 193 106770.5 40.02997 14.1365 253 90,857.4 10.1749 0.2057851 183 12714.5 16.54217 20.2953 228 59,937.5

1997 32.5863 10.9024 198 156595 13.49022 19.8967 282 29,050.0 10.178 0.3684851 182 12559.1 32.18064 31.13513 258 85,070.6

1998 18.2757 6.80544 194 91745.8 23.15875 11.117 288 22,077.9 9.02057 0.4999279 186 10321.7 12.49447 25.42231 277 114,085.9

1999 26.5840 6.21995 188 154043.9 45.77615 14.2166 277 64,044.7 8.19491 0.4036153 194 2939.58 45.1312 32.54334 285 114,086.2

2000 18.3140 6.63207 179 125203.8 16.26109 8.67204 290 26,812.5 9.13401 0.5665503 195 NA 26.13181 67.22843 315 163,269.5

2001 17.4220 5.52538 168 126258.4 14.33848 6.02497 316 22,997.9 12.2428 1.1238838 194 NA 24.0554 39.7628 310 143,905.3

2002 13.9080 3.73882 166 103941.2 15.32826 6.66554 331 30,067.0 9.70895 0.8039499 195 2373.94 14.60341 30.38975 288 49,489.9

2003 17.1785 3.2931 159 122533 23.28172 6.29305 333 54,659.1 14.0323 1.268804 200 NA 22.567 32.87423 284 61,291.1

2004 22.3221 5.56157 152 171402 28.5203 10.7311 331 73,250.6 16.4658 1.8964794 207 15865.9 25.06552 37.59282 296 60,368.3

2005 27.6018 6.08716 151 239128 28.48446 14.6571 335 81,428.1 17.2436 1.7259589 214 22244 33.446 41.67009 302 161,537.6

2006 36.0331 8.28511 131 348345.1 38.09588 13.394 344 138,886. 22.5671 2.4471119 202 32830.5 30.5893 42.87344 314 162,398.9

2007 38.1183 11.0808 125 397724.6 48.97843 26.1098 383 211,693. 51.8752 10.077542 212 84894.6 44.28182 46.72788 319 286,571.7

2008 21.1615 9.84482 125 234054.9 19.35554 21.6911 396 98,760.6 23.9362 9.5878662 213 48062.3 16.14731 32.82227 317 118,328.7

2009 38.0386 8.6069 125 352045.4 33.02401 21.3702 398 214,941. 19.6629 2.6992478 214 32223.4 36.73156 39.62633 315 233,996.6

2010 43.2039 10.3202 130 454345.2 50.8168 18.2668 420 360388.1 13.7871 1.4304047 215 50546.4 41.94143 57.65974 337 307,052.0

2011 34.9283 9.57064 128 408689.8 46.11565 16.5046 440 390106.8 9.53747 1.0085311 196 39028.4 26.04921 53.39734 362 197074.46

2012 44.2540 9.9592 131 525056.7 45.25646 10.4577 459 428222.5 12.1797 0.9080588 192 NA 39.14171 44.17828 405 315197.53

Sources: World Federation of Exchanges members. http://www.world-exchanges.org/statistics/annual-query and the World Bank.

The Mexican stock market began operation in the early 1970s. the information in Table 6

shows the market capitalisation as a percentage of gross domestic product (GDP), the total

value of stocks traded in the stock market as a percentage of GDP, the total number of

domestic companies listed, and the domestic market capitalisation. The number of companies

listed reduced significantly after the Peso crisis. Even though, there was a bailout in 1995, the

capital flight experienced in the stock market was not reversed. There was an increase of

about 5 companies listed between 1996 and 1997, the number however dropped significantly

afterwards. This shows that the government’s effort to build investor’s confidence was futile.

The number of companies remained at 125 before and during the global financial crisis

(GFC), there was however an increase in 2010 shortly after the crisis. The number of stocks

traded as a percentage of GDP was at its barest minimum in 2002 and 2003. The Mexico

export market experienced contraction in 2002. This contraction was as a result of the

terrorist attacks of 11 September in the United States. This affected the confidence indicators

in Mexico for several months. The trade flow did not pick up as expected in the long – run

which is evident in the economy as a whole (OECD, 2002). Market capitalisation was also

low during these years. The market capitalisation was at its peak in 2010 and 2012.

The Indonesian stock market began operation in 1988. The number of companies listed

increased significantly from 1996 to 2012. There was a decrease in number between 1998

and 1999. This can be attributed to the Asian Financial Crisis (AFC) at the time. The number

of companies, which was 290 in the year 2000, rose to 459 in 2012. The total value of stocks

traded followed an increasing trend from 2002 to 2007, there was a decrease in 2007/2008

which can also be linked with the GFC. Surprisingly, the number of listed companies did not

decrease as expected. The decrease in 2008 was evident in the market capitalisation of listed

domestic companies.

Trading commenced in the Nigerian stock exchange market in 1961. The number of listed

companies rose significantly from 1996 to 2010. There was a slight decrease between 2005

and 2006. Stocks traded total value as a percentage of GDP ranges from 0.2 to 2.4 between

1996 and 2006. The governor of Central Bank of Nigeria made effort to prevent the GFC

from having adverse effect on the Nigeria economy, the immediate impact was not evident in

the data as stocks traded increased from 9.6 to 10.1 percentage of GDP between 2007 and

2008, it however declined to approximately 2.70 in 2009. The prices of shares in the period

nose-dived and there was a huge loss recorded by investors (Ujunwa, et al., 2011).

Borsa Istanbul commenced trading in 1986, the number of companies and total market

capitalisation rose significantly from 1996 to 2007. The total stocks traded followed a rising

trend from 2002 to 2007 mostly due to the high amount of capital inflows. The domestic

capitalisation also exhibited the same trend. The total traded stocks reduced from 46.73 to

32.82 between 2007 and 2008 respectively. There was a slight increase from 2008 to 39.63 in

2009. The stocks traded hit 57.66 percent of the GDP shortly after the GFC in 2010. The

information also shows that the number of listed companies reduced by 2 during the GFC but

increased significantly afterwards. The market capitalisation percentage of GDP also shows a

similar trend.

The MINT countries show a similar trend in the range of the market capitalisation percentage

of GDP, Mexico ranges from 13.9 to 44.25 between 1996 and 2012, Indonesia ranges

between 13.49 and 50.82 during same period, Nigeria show a range of 8.19 to 51.88 and

Turkey shows a range between 12.49 and 45.13. The high range in Nigeria (51.88) featured

only once which was before the GFC (2007). This was as a result of the capital flight into

emerging markets shortly before the crisis. The percentage fell below 23.94 between 1996

and 2012.

2.3 Theories of Market Behaviour

Several theoretical perspectives explain the behaviour of the stock market. The stock market is

imperative and, considering the importance of the institution, there have been various

developments of theories that explain how the market functions. Most of the theories identified

have been tested empirically, which has resulted in the suggestion of a number of factors that

influence the stock market. Market behaviour is, however, paramount to investors, fund

managers and policymakers. It helps to identify when to invest, how to choose investments and

how to make policies that influence the stock market. One of the pressing issues in finance is

how to correctly identify factors that affect expected return on assets and how sensitive are

expected return to any identified factor. It is also important to know the reward attached to the

bearing of the sensitivity. Various models have been developed over the years, they try to give

an explanation on how to identify the best asset at a particular point in time.

Markowitz (1952) developed the portfolio theory and centres on modelling the modern

portfolio theory as a mathematical problem. The portfolio theory is a well-presented theory,

which assumes that investors have a certain amount of capital that they are willing to invest at

a particular point in time, and most of them face up to various options of investment

opportunities such as bonds, stocks, currencies and options. It, therefore, proposes to investors

to base their decisions on return and risk of assets and the entire portfolio. However, the theory

has never been tested in practice since its development – this is due to its immense amount of

data requirement (Witt & Dobbins, 1979). Some studies have shown realised returns to be

higher than the expected low-risk securities, which signifies a weak relationship between risk

and return expected; there is, therefore, a conclusion that no stable relationship exists between

risk and return of an asset (Murphy, 1977).The assumption of the theory stimulates the need

for a practicable model, the capital asset pricing model (CAPM).

Sharpe (1964) developed the CAPM. The CAPM is a one factor model which is beta, the model

explains why different securities have varying expected returns. The model asserts that since

expected returns of assets vary, assets are therefore expected to have different risk which is

identified as beta. The CAPM expects a linear relationship between beta and expected returns.

The theory is widely accepted because it is testable. However, the empirical record of the

argument was unsatisfactory thus warrants invalidation of its application (Fama & French,

2004).

The theory was also criticized by Fischer (1972) when he suggested that there is no such thing

as a risk-free asset. Therefore, he replaced the risk-free asset with a risky asset and found a

meaningful outcome. Examining the CAPM, one could notice that the theory is peculiar to the

beta, and without a reliable beta, an investor using CAPM cannot do better than another

investor who knows nothing about assets. Most of the critics of this theory received little

attention until Fama & French (1992), after examining 9,500 stocks, concluded that measuring

a stock’s risk using beta is not a reliable predictor of performance. The findings depict that

when the volatility of equity is known, it does not give an insight about the return.

The Efficient Maket Theory was developed by Fama (1970) and deviates from the previous

theories by describing the market to be occupied by prudent investors who try to maximize

profits by making predictions of the future value of shares, of which information is available

to all participants in the market. Various definitions sprung up for the theory, some of which

are presented in Table 7.

Table 7 Definitions of Efficient Capital Market

Names of authors and year Definition

Timmermann and Granger (2004) ‘The efficient market is a backbreaker for

forecasters. In its crudest form, it effectively

says that although we would very much like

to forecast, however, the returns from

speculative assets are unforecastable.’

Malkiel (2003) ‘The efficient market is associated with the

idea of a random walk, random walk is a

common term used in finance to describe

price series where all subsequent price

changes connote departures from previous

prices.’

Allen et al. (2011) ‘Market is efficient when it is impossible to

earn a higher return than the market.’

Eakins & Mishkin (2012) ‘An efficient market is a market where asset

prices fully reflect all information available.’

Empirical evidences by various researchers has made commendable efforts to present

evidences to support market efficiency; The study of Shamshir and Mustafa (2014)

investigated the existing empirical evidence of random walk movement and informational

efficiency in developed and emerging equity markets. The study emphasized on how

emerging market has received greater attention in the area of efficiency due to liberalization

of the financial markets of emerging economies and the huge capital inflow. The study

showed how the level of efficiency is greater in developed markets when compared to

emerging markets.

Lo & Mackinlay (1988); Fama & French (1988) presented evidence which contradicted

random walk hypothesis and concluded that stock returns are predictable to a considerable

extent. This became a debatable issue (see, Jensen, 1978; Malkiel, 2005; Gupta, 2006;

Agwuegbo et al. 2010). Lo (2008) pointed out that despite the large number of publications

on the issue of efficiency, there is no consensus reached among researchers on the efficiency

of financial markets.

Guidi et al. (2011) investigated random walk movement in Central and Eastern Europe (CEE)

stock markets from 1999 to 2009. Study used autocorrelation, variance ratio and runs analysis

to test the hypothesis of random walk movement. The study provided an evidence that CEE

markets do not follow random walk. Kapusuzoglu (2013) investigated the Istanbul stock

exchange market using the National 100 index data from 1996 to 2012. The result of the

study shows an evidence to reject the hypothesis of random walk movement.

Munir et al. (2012) examined efficiency of (Association of Southeast Asia Nations) ASEAN

equity markets, the study selected five stock markets which included Indonesia stock

exchange market. Data used ranges from 1990 to 2009. Using two regime threshold auto

regressive (TAR) approach, the result revealed non – linear stationary process in Indonesia

stock market which is inconsistent with the efficient market hypothesis.

A study by Emenike (2010) investigated random walk movement in the NSE from 1985 to

2007. Using non – parametric runs test, the study revealed a rejection of the randomness of

the stock return series. Ikeora et al. (2016), examined the presence of weak form efficiency in

the Nigerian stock market using All Share Index converted to stock returns. The study

employed runs ADF test to check for random walk movement, the result shows the whole

period from 1985 to 2014 presented an evidence that stock market do not have random walk

movement, thus an evidence of inefficiency is attributed to the market.

Lo & MacKinlay (1998) and Narayan (2008) rejected the efficiency of the market while

Ozdemir (2008) has empirically shown that markets are efficient. Zoran et al. (2012) arrive at

a conclusion that EMH is not a falsifiable theory and suggest to researchers to be cautious when

giving the interpretation to empirical evidence used in investigating market efficiency. The

1987 stock market crash has been the instance that increased the number of academic critics of

the EMH. Lee et al. (2010) examined stationarity of share prices in developed and developing

nations, and findings show that markets are not efficient. The efficient market hypothesis is

also rejected for the Indonesian stock market since the market index fails to exhibit a random

walk movement (Guidi & Gupta, 2013). Since EMH does not give a realistic picture of the

investment market, another theory was established by Ross in 1976.

The APT was developed by Ross (1976). The model assumes that asset returns are generated

by a factor model. The model proposes that assets or portfolios with same factor sensitivities

should offer same expected returns. It emphasizes a co variation of asset with a number of

factors. Equity returns are determined by various anticipated and unanticipated events, of

which we do not know their direction or magnitude; as a result, we seek to identify the

sensitivity of returns of an asset to these developments (Roll & Ross, 1995). The APT

recognizes that asset returns have connections with both systematic and unsystematic risk

factors. Systematic factors are those that affect the whole of the market and the economy while

unsystematic factors are those that influence a particular industry or firm and this thesis does

not regard the variable as an appropriate yardstick to measure sensitivity of the market;

Jecheche (2006) supported this theory and mentioned that the theory gives room for an asset to

have various measures of systemic risks and not just one, unlike the CAPM.

Chen et al. (1986) also proposed a set of relevant variables, which are industrial production,

term structure, inflation, oil prices, risk premium, consumption, as well as market indices. The

impact of these variables was investigated in the New York Stock Exchange market, and the

result confirms that systemic risk, especially economic news, is one of the factors that

determine equity returns. The goal of the APT is to model asset return as a function of

macroeconomic variables; however, there is a sensitive gap that no research has been able to

unravel, which is to identify the number of systematic state variables that should be included

when testing the APT model (Mohammed & Sulub, 2014). This gap has led to inconsistency

in the empirical results derived by researchers, thus a pressing need arises in finance to identify

the economic factors that most influence stock returns as well as stock prices (Muhammad,

2011). Both the CAPM and APT agree that assets are compensated according to the systematic

risk attached to it.

2.3.1 Interactions Between Stock Market and Exchange Rates (Micro Level)

At the micro level of the interaction between stock market and exchange rate, some debates

ensued within the framework of foreign exchange rate exposure. Most international investors

are interested in knowing whether exchange rate risk is already priced in their assets or not, as

well as the economic significance of the outcome. Theories in finance have laid a good

foundation that assumes a positive relationship between asset prices and their associated risk.

The arguments so far substantiate the assumption that there is a higher return for bearing a

higher risk; therefore, stock returns are believed to be determined mostly by the risk associated

with it.

There is a lingering question of whether domestic firms or multinational companies are exposed

to the so-called exchange rate risk. There is an assumption that the latter is possibly more

affected than the former; however, it is important to review this using an underlying economic

assumption. Cash flows of multinational firms are expected to be affected whenever there is an

alteration in the exchange rate. This is because their operations are across borders and are

usually sensitive to export and import activities. An increase in prices of imports translates into

a high cost of production, which later reflects in the selling prices of goods.

On the other hand, domestic firms are also affected when exchange rates change. This change

is in regard to changes in prices of imported input in the production process in a competitive

environment as this will put a downward pressure on the firms’ profit and the value of the asset.

2.3.2 Interactions Between Stock Market and Exchange Rates (Macro Level)

This section reviews the relationship between exchange rate and stock prices at a macro level.

The macro level is more concerned about the short- and long-term relationship between

exchange rate and stock prices. Cointegration is the most widely used technique to investigate

the long-term interactions between variables (see Geetha et al., 2011; Naik & Padhi, 2012;

Castillo-Ponce et al., 2015). Researchers are also conversant with the Granger causality tool

for examining the short-term interaction between stock prices and exchange rates (see Semra

& Ayhan, 2010; Kutty, 2010). Studies show that using Granger causality can produce one of

four outcomes: bi-directional relations (this is when causality runs from exchange rates to stock

prices and vice versa), uni-directional from stock prices to exchange rates, uni-directional from

exchange rates to stock market, and no relation whatsoever between variables.

The interaction between exchange rates and stock market have been studied under two main

approaches: the portfolio and traditional approach. The advocates of the portfolio approach use

the issue of the stock-oriented exchange rate model which assumes exchange rates to equate

the demand and supply of assets. This assumption supports that stock returns can affect

exchange rates. This approach views capital account as the major determinant of the exchange

rate. Gavin (1989) argues that innovations in the equity market impacts liquidity and wealth,

thus influencing demand for money and exchange rates. A practical example is where a

growing stock market increases the wealth of local investors, which increases demand for

money and higher interest rates. Subsequently, high interest rates attract capital inflows and

appreciation of domestic currency. On the other hand, the traditional approach is like the flow-

oriented exchange rate model, which stands on market goods hypothesis and interest rate parity

condition. Dornbusch & Fisher (1980), proposed this model and assumed that exchange rate is

determined by a country’s trade performance and current account. Advocates of this model

argue that causality runs from exchange rates to stock returns, unlike the portfolio approach.

They suggest that changes in exchange rates impacts international competitiveness and then

actual income. Since Discounted Cashflow technique assumes that stock price is viewed as the

present value of the future cash flows of the firm, exchange rates will influence stock prices.

An example is a currency depreciation in a particular country which makes the goods of the

country cheaper for foreign countries to import, thus an increase in demand for domestic goods

and sales. Moreover, due to transaction exposure, a firm’s future receivables and payables in

foreign currency can be influenced by exchange rate movement. There is solid theoretical

support for Granger causality running from exchange rates to stock returns or vice versa.

Even though we have seen above that there is analytical support for the interaction between

stock markets and exchange rates, some studies, however, cannot find any relation between the

variables. Some researchers found no evidence of interaction between exchange rates and stock

prices in Indonesia and Japan (Granger et al., 2000); in India (Mishra, 2005); and in Hungary,

Japan, France and Poland (Chen & Chen, 2012). In conclusion, we can see that literature

supports both the portfolio and traditional approaches as well as no relation between the

variables.

2.3.3 Interactions Between Stock Market and Interest Rates

The Fisher effect proposed by Irving Fisher in the 1930s is what is used to measure real interest

rates and also used to relate interest rates and stock market. The advocates of the Fisher effect

suggest a negative relationship between interest rates and stock market returns, the reason

behind the inverse relationship is that an increase in interest rate makes expected earnings (cash

flow stream) of the firm to decline because of the higher cost of financing expenditure (Peiro,

2016). A company’s cash flow is influenced by rates of interest through an alteration in their

cost of borrowing; this raises the debt service payment of firms. It also affects consumers who

are heavily indebted to the firm through reduction in their demand, which brings a downward

pressure on corporate profit; thus, a negative influence on the firm’s value and shares.

Korkeamaki (2011) suggests that businesses that are financed majorly by debts (loans) are most

interest-rate sensitive. Interest-sensitive stocks are the most affected when there is movement

in interest rates. For example, when there is an increase in interest rates, individuals are less

likely to buy/build houses which means less business for the real estate sector. This explanation

supports the Fisher effect as it means higher interest rates will cause interest-sensitive stocks

to decline.

2.3.4 Interactions Between Industrial Production and Stock Market

Industrial production is a fundamental measure of the performance of the economy; it is a proxy

for the economic activity of a nation. Some studies support the argument that industrial

production follows the pattern of how the economy evolves (Peiro, 2016); this means it

increases during an economic boom and decreases during economic downturn. During an

economic boom, there is higher productive capacity, which contributes to the chances of firms

to generate more cash; hence industrial production is expected to be beneficial to the

company’s cash flow generation. Studies suggest a strong and positive relationship between

industrial production and stock prices (see Maysami et al., 2004 and Naik & Padhi, 2012).

However, some studies suggest that industrial production does not have any significant effect

on stock returns (see Tursoy et al., 2009 and Kadir, 2008).

The relationship between macroeconomic variables and stock market returns is illustrated by

stock valuation models such as free cash flow model and dividend discount model. These

models are similar to DCF as they assume current prices of an asset equate to present value of

future cash flows, thus any macroeconomic variable that can be theoretically linked with cash

flows or rate of return, influences stock market.

2.3.5 Interactions Between Stock Market and Global Factors

The interaction between stock markets and global factors is an ongoing research in the field of

economics and finance. The idea ranges from some who believe in market segmentation (see

Hong & Daly, 2014) and others who are advocates of market integration (see Choudhry et al.,

2007; Okoli, 2012; Fabrian & Herwany, 2007). Some researchers even believe emerging

markets are partially integrated (see Carrieri et al., 2007; Tai, 2007). However, researchers are

trying to reach consensus on whether emerging markets are completely integrated or

segmented. Advocates of market integration suggest that global risk factors explain the

variations in stock returns rather than country factors. On the other hand, market segmentation

proponents lay emphasis on country macroeconomic factors explaining variations in stock

returns. Empirical evidence of some studies show that markets are neither perfectly segmented

nor perfectly integrated; this is because every attempt to use the two extreme circumstances

produces no certain outcome. Graham et al. (2016) argue that investors’ decision functions are

not limited to country macroeconomic variables; hence researchers in this field should consider

global factors alongside country macroeconomic variables in explaining stock returns in

emerging markets.

Goh et al. (2013) suggest that the Chinese stock market is segmented from the global market,

as this is substantiated with the findings of Huang et al. (2000) when no causal or cointegrating

relation is obtained between the American and Chinese stock markets. However, this was

invalidated when Johansson (2009) showed that the Chinese stock market exhibits increasing

levels of integration since their admission into the World Trade Organisation in 2001.

The IS–LM Keynesian model, which relates investment savings to liquidity preference money

supply, suggests that a decrease in interest rate increases demand for credit which has a

multiplier effect on consumption (Mankiw, 2009). An increase in the application for credit

increases opportunities for investing in the capital market. The Federal funds rate is the interest

banks charge other banks on overnight loans made from excess reserves (Hilton & Hrung,

2007). The Federal funds rate is a tool used to control the economy. During an expansionary

monetary policy, the Federal Reserve lowers the Federal funds rate to stimulate the economy

by making borrowing cheaper and therefore pushes prices and demand upward. To achieve a

contractionary monetary policy, the Federal funds rate is increased to reduce the amount of

money in circulation (Nautz & Schmidt, 2008).

Empirical studies show that the tightening of monetary policy in the US is associated with the

appreciation of the US Dollar, while expansionary monetary policy causes dollar depreciation

(see Faust et al., 2003). This evidence suggests that anytime there is a contractionary monetary

policy implementation in the USA, emerging countries with a high level of imports from this

destination are likely to be more affected than the ones with a lower level of imports. This is

because there will be an increase in the prices of goods imported from the US which increases

costs of input in the production process; this increase makes goods pricier. On the other hand,

during an expansionary monetary policy implementation, economies that rely on the export of

commodities will experience a decrease in revenue generated through exports. There is also a

likelihood of extra expenditure on the government as they pay more to offset their US Dollar-

denominated debts.

The Federal funds rate has been held near zero to support the economy during the worst

financial crisis. Many foreign investors take advantage of the lower interest rate by borrowing

money during this period and investing it in emerging markets. Since emerging markets are

characterized by high risk, investors stand the chance of earning higher returns because of the

high level of risk which is usually proportionate to the degree of return in the market. Higher

returns increase the participation of foreign investors in emerging markets. As a result, there

will be high capital mobility from mature economies into the emerging markets.

These instances suggest a negative relationship between factors like the Federal funds rate and

emerging stock markets.

2.4 Empirical Evidence of the Relationship Between Macroeconomic Variables and Stock

Market

Empirical evidence on the linkage between macroeconomic factors and stock markets in

emerging economies have been numerous, especially since emerging markets have been

identified as being segmented from the global stock market. The segmentation indicates that

local risk factors rather than world risk factors are the major source of variation in equity returns

(Maysami et al., 2004). The evidence provided by various researchers had not followed a

particular pattern and, as a matter of fact, yielded conflicting results which could lead to

misleading policy recommendation and implications; this has given rise to researchers seeking

the factors responsible for differences in the findings. Godfrey (2013) suggests that capital

allocation variation and institutional differences within and between countries have made

results in a particular emerging country not to be generalized – thus the increasing need for

country-specific studies. Researchers embrace this idea by avoiding generalizing results,

despite the efforts made by researchers to stick to country-specific investigation of this

phenomenon. Some researchers (see Okpara & Odionye, 2012; Osamwonyi & Osagie, 2012)

who have used the same countries produce conflicting results, which is problematic.

Contradictory results, however, could serve as a tool of confusion for investors and

policymakers. It is therefore of great importance to do a bit of analysis on sample size, data

source, choice, data frequency, methods and structural breaks.

Table 8 Analysis Guideline

Issues to be considered in the literature Brief analysis

Sample size Time series analysis is used to predict future

values based on previously observed values.

It uses the natural way of ordering. It is

important to know the basic standard

required for regression analysis before a

study like this can be widely acceptable.

Green (1991) suggests that the number of

observation must be greater than when 50 is

added to 8 multiplied by the number of

predictors ( 𝑁 > 50 + 8𝑘 ) where N is the

number of observation and k is the number of

predictors. These criteria will be used as a

yardstick to ascertain the number of

observations expected in reviewed literature.

Data source and choice The source which sometimes explains

whether data is collected raw, mined, refined

or hand-calculated is very vital to research.

Discrepancies in data influence the outcome

of tests conducted, which eventually impacts

policy recommendation. Hence the reason

for near accuracy in data. The unit of

measurement of variables is equally

important, especially how a proxy is

selected. An example is the use of treasury

bills or discount rates as a proxy for the

interest rate.

Data frequency Do variances in frequency choice lead to a

different outcome or is frequency chosen

based on research aim and hypothesis? Stock

market analysis is better off studied monthly

since this helps to avoid distortions that are

common in the weekly and daily data which

arise from non-synchronous and non-trading

days (Marashdeh & Shrestha, 2010). Examples

are the New Year, national holidays and

Easter when markets are closed.

Methods The type of method that is employed in a

particular research must align with the aim

and objectives that the research is set to

investigate. It is important to identify the

purpose for which an idea is communicated

to have a full understanding of why they have

adopted a particular method of analysis.

Structural breaks Structural breaks can be observed in data,

and they occur when a real-world event

affects variables. It is vital to note if

structural breaks are accounted for in past

literature and to review how the problem of

structural breaks can be resolved.

Evidence from literature shows a number of statistical methods employed by researchers, of

which some are: regression analysis, cointegration analysis, vector error correction model

(VECM), vector autoregressive model (VAR), ordinary least square method (OLS), Granger

causality, impulse response function (IRF), variance decomposition (VDC) and generalized

autoregressive conditional heteroskedasticity (GARCH). These methods are used by

researchers to find a way of studying the behavioural pattern of stock prices and

macroeconomic variables. The statistical methods are explained in Chapter 4. In a research

such as this, it is ideal to look into the literature on the methodology alongside that of the topic.

This task helps to find the gap in the literature and establish the original contribution of this

study.

Researchers who are interested in forecasting stock market and macroeconomic variable

volatility (Sharma & Vipul, 2016; Okoli, 2012; and Caporale et al., 2016) have employed the

GARCH model to predict and forecast the movement of stock market indices in various

countries. Multiple regression is also commonly used by researchers (Sopipan et al., 2012 and

Kadir, 2008) for forecasting and investigating the interaction between variables. However,

researchers such as Alam & Uddin (2009); Tursoy et al. (2009); as well as Buyuksalvarci &

Abdioglu (2010) and Ahmed & Hasan (2010) have employed OLS and Granger causality to

examine the relationship between economic and financial variables. The author of this paper

noticed that researchers pick a particular method that gives a soothing relief to their quest and

ignore the fact that the use of different methods gives a different outcome in this type of

research. To guard against this, the author uses cointegration, VECM, Granger causality and

some other graphically based tests like IRF.

2.4.1 Empirical Evidence of the Relationship Between Stock Market and Commodity

Prices

The literature on the relationship between macroeconomic variables and the stock market is

voluminous, and most of the results derived from the research are either inconclusive or

contradictory. The conflicting outcomes have prompted most researchers to include variables

outside a nation or economy that possibly influence stock market return movements in their

models. Oil is a strategic commodity that is identified as a determinant of growth for all

economies (Pradhan et al., 2015); however, looking at the rate at which researchers (see

Sadorsky, 1999; Sahu et al., 2014) have focused mostly on oil prices than other commodity

prices, this calls for a lot of concern and questions.

Countries that are experiencing rapid growth, especially the ones enjoying modernization and

urbanization, are the ones that increase their demand for commodities such as oil. Fang & You

(2014) observed that developing countries use twice as much crude oil to produce a single

economic unit when compared to OECD countries. The researchers mentioned above are part

of those who followed the likes of Sadorsky (1999) in examining the link between fuel oil

prices and stock returns. They, however, used the same idea to investigate the relationship

between the variables in newly industrialized economies like China, India and Russia. Some

researchers who have tried to venture into a similar research (Huang et al., 1996) found no

interaction between stock returns and oil prices. Fang & You (2014), however, suggested that

the reason for this type of result depends solely on the level of dependence of countries on oil

exports or imports.

Ayhan (2011) also explored the long-term relationship between three stock market indices and

international Brent oil prices in the Istanbul Stock Exchange using the same econometric

techniques for the country. His findings show a long-term relationship between the variables;

however, the Granger causality revealed that there is a one-way causality which runs from

stock market return to oil prices. The evidence in the research shows that stock market return

in Turkey influence oil prices and not vice versa.

2.4.2 Empirical Evidence of the Relationship Between the Global Factors and Emerging

Stock Markets Returns

The argument that ensued in the literature on the stock market and global factors, such as

Federal funds rate, is that markets are completely integrated which explains why global risk

factors can explain variability in the prices. On the other hand, some researchers are of the

opinion that there is complete segmentation of the market. Edwards (2010) investigates how

changes in Federal Reserve interest rates affect interest rate differentials in four Latin American

countries (including Mexico) and four Asian countries (including Indonesia). The result

suggests that the extent of transmission into countries with high capital mobility (like the Asian

countries) is stronger and faster than countries with low capital mobility. Tai (2007) shows that

markets can only be partially integrated and this was evident in the Asian crisis in 1997.

However, Hong & Daly (2014) suggest that the Chinese market is not integrated, although their

finding shows that the global financial crisis is the only significant systemic risk influencing

asset pricing.

Eichengreen & Gupta (2015) found that countries that attract large volumes of capital flows

and those that allow current account deficit to widen in the period when it was easily financed

were the most affected by the tapering talk. Their research shows that countries like Turkey

and Indonesia saw stock markets decline up to 10% during the tapering period.

Okoli (2012) mentioned that if markets are integrated, then global factors should be the leading

factors used to predict the return of assets. The researcher is also of the opinion that market

segmentation advocates suggest that country macroeconomic variables should be the

determinants of market volatility. He, therefore, evaluated the impact of domestic and global

factors in explaining the movement of returns in the Nigerian stock market by employing the

GARCH and VAR models. The findings in this study show that global factors and domestic

factors play a significant role in explaining stock returns and volatility. The results confirm that

there is increased integration during the crisis period. This study is different from the one that

this research is set to do as the researcher concentrates mainly on investigating whether global

factors, such as Federal funds rate and London Interbank Offered Rate (LIBOR), and domestic

factors help in determining volatility in the stock market. However, this research is set to

examine the short- and long-term impact of changes in global factors and country

macroeconomic factors on stock returns.

Research conducted by Ulku and Demirci (2012), aimed to analyse the joint dynamics of

foreign exchange rate markets and emerging stock returns. The model estimated eight

countries, which included Turkey, were exchange rates, MSCI Europe index, MSCI emerging

markets index, commodity index and European – emerging stock returns. Employing

structural vector autoregressive (SVAR) model, structural VECM, IRF and examining both

daily and monthly data from 2003m1 to 2010 m10, the results derived show an evidence of

co – movement of stock market returns and exchange rate. The result show that the

interactions depend on the depth of the market. The result shows the relationship between the

currency and stock markets in Turkey is the strongest of the 8 countries examined. The result

also suggests that home currency returns are positively related to global stock index returns.

The lagged effect of the currency value on the National stock market is negative to some

extent in Turkey.

A research by Wong (2017), investigated the relationships between real exchange rate and

real stock returns in Asia and European economies. The study employed the constant

conditional correlation (CCC), dynamic condition correlation (DCC), multivariate GARCH

and the granger causality models for the following countries’ stock market; Malaysia,

Philippines, Singapore, Korea, Japan, United Kingdom and Germany. The researcher

included a dummy variable in the model to capture the influence of the AFC and GFC. The

result derived show that exchange rate and the stock market are significantly correlated. The

result specifically shows a negative relationship. The Granger causality result supports the

portfolio balance approach for 2 of the countries. The result in the work of Wong (2017)

shows that there is a possibility of the relationship between a macroeconomic variables and

stock returns to vary. Graham et al. 2016 suggests that changes in global economic activity

should have a varying impact on country equity returns and it should depend on the level of

country’s exposure to the global factor. Even though results are interpreted as either positive

or negative, it is important to note whether the relationship is a lead-lag or contemporaneous.

Dincergok (2016) conducted a research on the relationship between world equity index,

industrial production, exchange rate, oil prices and interest rates on four main sectors return

indices in Turkey stock exchange market. The sample period covers 2000m8 to 2008m11.

The result derived shows a negative effect of exchange rates and interest rates on all the

sector returns and a positive impact of world equity return index on all sectors except

technology sector. Oil prices however show no significant impact on sector return indices.

Graham et al. (2016), evaluates the interaction between global economic activity (measured

using commodity index and maritime index) and equity returns in emerging markets. The

researchers included global equity index proxied by the MSCI Global equity index. Sample

period extends from 1997m11 to 2013m9 and employing correlation and OLS, the result

show that global economic activity has positive and statistically significant coefficients in

explaining emerging markets.

Khan et al. (2015) investigated the impact of 17 macroeconomic variables which includes six

local variables (consumer price index, Industrial production index, trade balance, nominal

exchange rate, money supply and interest rates), six regional factors and five global economic

variables (US treasury bill rate, world stock market return, world GDP, world inflation rate

and world oil prices) on Southeast Asian stock returns. The study employed vector

autoregressive and multiple regression method to examine data for 15-year period from

1998m1 to 2012 m12. Countries selected were India, Sri – Lanka, |Bangladesh and Pakistan.

The regression output show that the information about exchange rates, interest rates and trade

explain stock returns in Southeast Asia but economic activity does not. The result indicates

that previous stock returns in other South Asian countries and world economic activity

explain stock returns in India but not in other countries.

A research conducted by Mensi et al. (2014) on the impact of global stock market on the

BRICS stock market returns, employing Quantile regression and using daily data from

1997m9 to 2013m9, the result shows a positive and significant impact of Standard and Poor’s

500 index on the BRICS stock returns before and since the GFC. The impact of economic

policy uncertainty in the US is insignificant on the BRICS stock returns.

Variables in Countries Other Than the MINT

The arbitrage pricing theory (APT) identifies that stock market returns are affected by various

macroeconomic factors but the theory has its shortcomings by not being able to specify these

macroeconomic factors. Subsequently, researchers have added and deducted from the variables

proposed by Chen et al. (1986) because some of these variables are not available for emerging

or pre-emerging countries; some of the empirical evidence from these researchers is presented

Geetha et al. (2011) examined the relationship between stock returns and inflation in Malaysia,

the United States of America and China. Inflation was expressed using two different variables,

which are expected and unexpected inflation. They used monthly data of treasury bill rates,

bank rates for China, inflation (measured using consumer price index), exchange rate, gross

domestic product (industrial production as a proxy for GDP) and share prices from 2000 to

2009. They employed cointegration and the error correction model technique, the study reveals,

through cointegration, an existence of a long-run relationship between the three countries but

no short-run for two of the countries, which are Malaysia and the US. The vector error

correction result shows a short-run relationship between inflation rate and the Chinese stock

market returns.

A study finds evidence of interactions between stock market returns and foreign exchange rate

in the BRIC countries. Sample period spans from 1997 month 3 to 2013 month 2. Weekly data

collected yielded a total of 832 observations. Findings show an inverse relationship between

stock market and exchange rate for all countries except South Africa (Chkili & Nguyen, 2014).

Narayan et al. (2014) checked the relevance of institutional and macroeconomic factors in the

predictability of stock market returns in 18 developing countries. Various macro factors were

included in the model, which included GDP growth, exchange rate and inflation rate. The result

of the study shows that in 15 out of the 18 countries selected, there is an evidence of the use of

institutional, macroeconomic or both as factors that can predict stock market return. The result

also shows that only 9 out of the 18 show macroeconomic factors as determinant of the stock

return.

Omondi & Tobias (2011) investigated the effect of foreign exchange rate, interest rate, and

inflation on stock return volatility on the Nairobi Stock Exchange. Monthly time series data

from 2001 to 2010 was analysed using exponential generalized autoregressive conditional

heteroskedasticity (EGARCH) and threshold generalised autoregressive conditional

heteroskedasticity (TGARCH). The findings show that interest rate, inflation, and exchange

rate impact the stock market volatility in Kenya. The impact of foreign exchange rates on stocks

was significant but relatively low.

Variables in the MINT Countries

Since this research focuses on the MINT countries, the empirical evidence that is country

specific would help build one’s expectations. Therefore, the review should assist in the area of

macroeconomic variables selection for the model estimation, starting with Mexico.

2.4.4.1 Mexico

Kutty (2010) examines the relationship between exchange rate and stock prices in Mexico by

using weekly frequency data between 1989 and 2006 of Bolsa Mexico’s equity index; market

capital weighted index of the leading 35 – 40 stocks and Mexico peso per US dollar rate. 849

observations were collected; the Granger causality test confirms that stock prices Granger-

cause exchange rate, which indicates a uni-directional causality. The causality, however, is

confined to only one-time lag which means that the impact can only be instantaneous but would

diminish in the long-run. The result, therefore, rules out any evidence of a long-run relationship

between the variables.

Alam & Uddin (2009) investigate the relationship between stock market indices and interest

rate in 15 developed and developing countries which included Mexico. The ordinary least

square method was employed to monthly time series data from 1998 to 2003. Results show a

significant negative relationship between stock price and interest rate in Mexico. The

suggestion of having a controlled interest rate was made for Mexico as it will benefit the stock

exchange through demand pull way by attracting more investors.

Castillo-Ponce et al. (2015) evaluate the association between the stock market and the economy

in the short and long-run. Using cointegration and common cycle tests to investigate Mexico

IPC index, industrial production and gross domestic product data from 1993 to the first quarter

of 2011, the outcome of the research indicates that industrial production, and stock prices move

together in the long-run. It also revealed that industrial production exhibits a significantly

positive relationship between the variables.

Hsing et al.(2013) analyse the effect of selected macroeconomic and global variables on the

stock market in Mexico. Using quarterly data of gross domestic product (GDP), money market

rate, exchange rate, ratio of M3 to GDP, US stock market index and expected inflation from

1985 to 2011. Employing exponential GARCH, the result derived shows a positive relationship

between exchange rate and stock market and a negative relationship run from inflation and

interest rate to stock market. The implication of this is that the depreciation of Mexico Peso

lowers interest rate and the inflation rate which would increase the stock exchange prices.

Assefa et al. (2017) investigated whether macroeconomic variables such as interest rates,

exchange rates and GDP explains some variations in stock return. They selected 21

developed and 19 developing countries, which includes Mexico, Indonesia and Turkey. The

result derived using fixed effect models identified very small negative impact of interest rate

on stock returns in developing countries. The MSCI variable included in the model showed a

positive and significant impact on developing countries’ stock returns.

2.4.4.2 Indonesia

Bhayu & Rider (2012) explain that policymakers are of the opinion that Indonesia’s economy

is vulnerable to capital flight; therefore it is important to understand the impact of domestic as

well as foreign shocks on the stock market. The study as a result of this investigates the short

and long-run relationships between domestic and foreign shocks on the stock market. Monthly

data ranging from 1997 to 2011 of domestic variables such as; industrial production, exchange

rate and money supply; with the inclusion of five regional stock prices of countries like

Malaysia, Thailand, Singapore, Phillippines and two developed countries namely Japan and

the US. Stock indexes are examined using cointegration as a tool to analyse the long-run

relationship, Granger causality to identify the short-run forecastability and the error correction

model to indicate the convergence of the short-term movement between variables in the long-

The cointegration results derived by Bhayu & Rider (2012) show an evidence of long-run

relationship among variables selected while the Ganger causality output indicates that

industrial production and exchange rate Granger-cause Jakarta composite index. However,

money supply shows no substantial evidence of bi-directional causality to the stock market.

The impulse response function shows a negative effect of industrial production and exchange

rate on the stock market. The authors suggest that money supply is not a good variable to be

used as economic surveillance because of its lack of impact on both stock market and economic

output. This result suggests that better knowledge of variables such as exchange rate will aid

future budget planning process, the bi-directional causality between exchange rate and stock

market explains the need to include monitoring of exchange rate movement in the economic

surveillance process of Indonesia.

Miseman et al. (2013) investigate the impact of macroeconomic forces such as interest rate,

broad money supply, domestic output and inflation rate on five ASEAN stock markets which

included Indonesia. Employing generalized least square regression, the results show the

substantial significance of all the macroeconomic forces chosen except domestic output which

shows no significant impact on the stock markets. The results also show that interest rate is the

strongest determinant of stock market performance. The authors suggest that the results derived

in the study should be a wake-up call for investors who rely on the forecasts of economic

growth to formulate investment strategy. ASEAN countries’ central banks are admonished to

formulate reliable and competitive policies that would enhance growth in the economy as well

as encourage more inflow of capital into respective countries’ capital market.

Surbakti et al. (2016) examined the impact of macroeconomic factors on Jakarta Composite

index (JCI) stock return volatility before and after the global financial crisis, selecting global

macroeconomic variables such as the Dow Jones index (DJI) and the gold price while exchange

rate, interest rate and inflation are domestic macroeconomic factors. Using the ARCH–

GARCH model, the results indicate that variables such as exchange rate and DJI show a

significant positive impact on JCI while all other variables show no significant effect on JCI’s

return volatility. The outcome of this study emphasizes that volatility in JCI is not only caused

by domestic macroeconomic variables but is also affected by international macroeconomic

factors.

Prima et al. (2013) examined the long and short – run relationship between selected

macroeconomic variables and Jakarta stock market Islamic index. Using time series

techniques of VAR and cointegration with data ranging between 2000 and 2010. The

macroeconomic variables selected were exchange rate, inflation rate, money supply and

industrial production. The result derived shows an evidence of long – run relationship

between stock market index and macroeconomic variables.

Karim et al. (2014) investigated the interaction between macroeconomic volatility and stock

market volatility. Using monthly data from 1986 to 2013 of macroeconomic variables such as

industrial production, exchange rate, inflation and money supply, and employing GARCH (1,1)

and Granger causality, the results show that macroeconomic variables have no significant

impact on stock market volatility. However, a uni-directional causality relationship runs from

the stock market to exchange rate, based on the outcome of the study; the authors suggest that

policymakers should take stock market volatility into account when making any policy relating

to exchange rate.

Jamaludin et al. (2017) investigated the impact of macroeconomic variables such as money

supply, exchange rate and inflation on both conventional and Islamic stock market returns in

Indonesia, Singapore and Malaysia. Data analysed spans from 2005m1 to 2015m12 using

panel least square regression technique. The result derived shows that there is a similar

pattern in the effect of macroeconomic variables on both stock returns. It also indicates a

negative impact of inflation on stock returns with a greater effect when compared to

exchange rate and money supply. There is a positive relationship between exchange rate and

stock returns and it is significant but the positive interaction shown between money supply

and stock returns is not significant.

The study conducted by Najafzadeh et al. (2016), examined the impact of exchange rate

volatility on stock exchange returns of D8 countries. The data used in the study was monthly

sample from 2008m1 to 2015m6. Researchers use panel GARCH model to investigate the

effect of exchange rate on the stock market returns. The 8 countries selected included

Indonesia and the result show a significant impact of exchange rate on stock returns. Real

exchange rate and inflation rate show negative effects, but oil has a positive effect on stock

returns. Interest rate show no significant impact on stock returns.

Alam 2013 examined the impact of macroeconomic variables and features of firm in

determining stock market return in Southeast Asian countries namely Malaysia, Indonesia,

Singapore and Thailand using monthly time series data from 2003 month 7 to 2011 month 11.

Macroeconomic variables included in the model were; growth rate of industrial production,

exchange rate, long and short – term interest rates, change in term structure, changes in

money supply, consumer price index and growth rate of crude oil. The result shows that the

significance relationship between macroeconomic variables and portfolio stock returns were

not reliable.

2.4.4.3 Nigeria

Olukayode & Atanda (2010) used VECM and cointegration methods to examine long- and

short-run shocks exerted by macroeconomic indicators on the Nigerian Stock Exchange.

Treasury bill rate, real output growth, exchange rate, money supply rate and consumer price

index were examined and the findings show a short- and long-run impact of these variables on

the Nigerian Stock Exchange (NSE), but exchange rate, inflation, money supply and real output

growth was recommended as the leading variables influencing NSE.

Izedonmi & Abdullahi (2011) adopted the ordinary least square method to examine the effect

of macroeconomic factors on Nigerian stock returns. They employed monthly frequency data

from 2000 to 2004 of three macroeconomic variables such as inflation, exchange rate and the

market capitalization of 20 sectors of the Nigerian Stock Exchange. The results show that all

variables have the probability greater than 0.05, which means that they are insignificant in

predicting different industry returns; however, inflation has the highest probability value which

makes the variable have more of a level of non-influence on Nigeria stock returns. The result

also suggests that each macroeconomic factor may affect different sectors in a different manner

but might not be statistically significant. Based on the result, the author concludes that other

local economic variables, rather than the ones selected, influence stock market returns in

Nigeria.

Maku & Atanda (2009) explored the short and long-run effect of five macroeconomic variables

on the Nigeria all share index. Variables investigated are consumer price index, treasury bill

rates, real GDP and exchange rate. Annual frequency of the data from 1984 to 2007 was

examined using cointegration and error correction model. The results indicate that there exist

a long-run relationship among all variables except consumer price index. Consumer price index

exerts a positive impact on share prices, which was not in tandem with the researchers’

expectation. The findings of the study signify that all variables except treasury bill rate should

be considered by foreign and domestic investors in decision making in Nigeria.

Adaramola (2011) used a unique method known as panel model to investigate the impact of

six macroeconomic factors (interest rate, exchange rate, money supply, inflation, oil price and

gross domestic product) on stock prices in Nigeria. Using pooled or panel model to examine

quarterly data between 1985 and 2009 (100 observations), so as to be able to combine both

cross-sectional and time series data, findings of the research show that macroeconomic factors

have varying significant impact on stock prices of individual firms in Nigeria. Also, all

variables except money supply and inflation have significant impact on stock prices. Interest

rate and exchange rate show adverse impact while gross domestic product and oil prices exert

significant positive impact on share prices in Nigeria. The results derived indicate that domestic

macroeconomic variables have significant but a varying impact on the Nigerian stock market,

which is useful for diversification of portfolios by investors as better risk-return trade-off can

be achieved. The results also suggest that studying the different significance of the variables

helps improve the performances of portfolios. In all, the study concludes that policymakers

should be mindful of the impact of macroeconomic variables such as interest rates, exchange

rates and oil prices on the stock market when formulating monetary policies.

Okpara & Odionye (2012) examined the causal relationship between stock prices and exchange

rate in Nigeria using pairwise Granger causality within VECM and multivariate cointegration

model. They employed these models to analyse quarterly data of exchange rate, inflation,

interest rate and three indicators of the Nigerian Stock exchange market performance which

are; all-share index, stock market capitalization and values of shares traded from 1990 to 2009.

Inflation in the model is classified as a control variable, and it is included alongside interest

rate to avoid the problem of omitted variable bias in the VAR model. The nominal exchange

rate was used as a proxy for exchange rate; real interest rate was also used as a proxy for the

interest rate. The cointegration result shows that exchange rate and the entire stock price

indicators have a long-run relationship. The results also suggest a stable negative relationship

between exchange rate and stock market prices, a significant positive impact of interest rate

and a negative impact of inflation on stock prices. The positive influence of interest rate was

justified by its growing attraction of foreign investors who would use the opportunity to

diversify their portfolio which increases the level of capital inflow thereby, increasing stock

prices. The pairwise causality indicates that stock market prices Granger-cause exchange rate

without feedback, which implies a uni-directional causality running from stock market prices

to exchange rate. This explains how changes in stock prices impact capital flight which in turn

influences the movement of the exchange rate. Exchange rate and all-share index accounted

for the larger forecast error in the stock market. The result suggests that policymakers should

heed the implementation of stock market policies and regulations because of its long-term

impact on exchange rate.

Olorunleke (2014) used the ordinary least square method, Granger causality and the

cointegration method to examine the impact of output growth, inflation and the interest rate on

stock returns in Nigeria. Taking annual time series data from 1986 to 2012, the findings show

a negative relationship between interest rate and the NSE All-share index return, and a positive

relationship between macroeconomic variables such as GDP (a measure of output growth) and

inflation. The results revealed that all variables move together in the long run. It also indicates

that the minimum rediscount money rate that is used as a proxy for the interest rate is an

alternative investment opportunity; hence investors would invest less in stocks whenever

interest rates rise. The pairwise Granger results indicate that the NSE All-share index has a

feedback effect on GDP and inflation; the results also confirm the Fisher effect in the Nigerian

economy with causality running from interest rate to inflation. The result also confirms that all

the variables move together in the long run. The author concludes by suggesting that

policymakers should keep interest rates stable and, if possible, low so as to give investors the

opportunity to raise investment funds from the capital market.

Osisanwo and Atanda 2012 investigated the determinants of Nigeria stock returns by

employing the Ordinary Least Square method using annual data between 1984 – 2010.

Macroeconomic variables included in the model were exchange rate, real capital income,

interest rate, broad money and consumer price index. The findings show that previous stock

return levels, interest rate, exchange rate and money supply are major determinants of stock

returns in Nigeria.

Nkoro et al. (2013) conducted a research to examine the effect of local macroeconomic

variables on the stock returns in Nigeria. The variables included in the model are;

government expenditure, inflation rate, exchange rate, index of manufacturing output,

minimum rediscount rate and broad money supply. Data examines spans from 1985 to 2007.

The result shows a negative and significant impact of interest rate on stock return. Money

supply and exchange rate show no significant impact on the stock returns.

2.4.4.4 Turkey

Tursoy et al. (2009) investigated the performance of arbitrage pricing theory in the Istanbul

Stock Exchange (ISE) by employing the ordinary least square method to examine the effect of

macroeconomic factors on stock returns. They used monthly frequency data for the period from

2001 to 2005 for variables such as inflation, interest rate, risk premium and money supply,

exchange rate and unemployment. The ordinary least square method findings show that

inflation is significant in explaining seven portfolios: term structure of interest rate in one

portfolio, risk premium in three portfolios and money supply in two portfolios. However,

unemployment and exchange rate show no significance in explaining all portfolios. Interest

rate and inflation exact positive effects on the stock returns of some portfolios. Exchange rate,

however, exhibits a positive effect on two portfolios and a negative effect on six portfolios. In

all, a significant pricing relationship exists between stock returns and macroeconomic factors

but a weak explanatory power was noticed. The weak explanatory power suggests that there

are other macroeconomic factors affecting stock returns in the Istanbul Stock Exchange market

than the ones tested.

Buyuksalvarci & Abdioglu (2010) in their quest to identify the macroeconomic factors that are

more responsible for stock price movement, investigate the causal relationship between five

macroeconomic variables and the Istanbul stock exchange market. Data of variables such as

gold price, foreign exchange rate, industrial production index, consumer price index and broad

money supply were retrieved on a monthly basis from March 2001 to June 2010. Selected

variables were tested using unit root of which data collected was not integrated of the same

order, hence the reason for the authors to employ Granger non-causality test. Results derived

indicate a uni-directional long-run causality from stock prices to macroeconomic variables.

The result implies that stock market can be used to predict movement in macroeconomic

variables and not vice-versa, which means that some other macroeconomic variables influence

the stock market other than the ones selected in the study.

Kadir (2008) looked at the impact of macroeconomic variables on stock returns on the Turkish

stock market. The data range used in the study is from 1997 to 2005 and it includes variables

such as oil prices, interest rate, money supply, industrial production and exchange rate. Using

multiple regression, the interest rate was found to impact stock returns negatively, while

industrial production, money supply and oil prices show no significant influence. However,

exchange rate shows a positive influence on stock returns in Turkey.

Semra & Ayhan (2010) explored the short-term dynamics and long-term relationship between

stock price index and three macroeconomic factors (inflation, industrial output, and exchange

rate) in Turkey. Cointegration and Granger causality test were applied to monthly time series

data between 2003 and 2010. The outcome of the findings show a long-term relationship as

there exist a cointegrating relationship between the variables, while uni-directional and bi-

directional causality were also found in the short-run among the variables.

Ahmed & Hasan (2010) examine the relationship between macroeconomic factors and stock

prices in Turkey. Monthly data from 2001 to June 2010 of Borsa Istanbul 100 index, foreign

exchange rate, gold price, broad money supply, consumer price index and industrial production

were examined using long-run Granger non-causality test and the results revealed an evidence

of a relationship between the stock market and macroeconomic variables in Turkey.

Ozlen & Ergun (2012) investigated the effect of macroeconomic variables on stock returns in

Turkey. The authors selected variables such as exchange rate, inflation, unemployment rate,

interest rate, current account deficit and stock returns of 45 companies which have been chosen

randomly from 11 different sectors. Dollar rates were used as a proxy for exchange rates,

interbank rates as proxy for interest rates, consumer price index as a measure of inflation and

the current account deficit was measured by taking the difference between import and export.

All data was obtained from the ISE website and spans from the second month of 2005 to the

last month in 2012. The autoregressive distributed lag (ARDL) was employed to determine the

relationship between the variables chosen. The results indicate that two factors, which are

interest rate and exchange rate, are determinants of the stock returns of the companies selected

from different sectors. The result shows that changes in interest rate and exchange rate impact

the economy as a whole. The results also suggest that these two variables have a crucial role to

play in alleviating the hazardous effect of the economic recession and financial crisis. Finally,

interest rate and exchange rate movements can be used to forecast stock returns of companies

in Turkey.

Acikalin et al. 2008 examined the relationship between macro variables such as interest rate,

current account balance, exchange rate and gross domestic product, and stock returns in

Istanbul stock exchange. Employing time series quarterly data for all variables and applying

vector error correction and Johansen cointegration approach on the data, the result derived

show an evidence of long and stable relationship between macro – variables selected and

stock returns. The result also shows uni – directional causality between each of the macro

indicators and Istanbul stock exchange.

Table 9 Summary Table of Significant Empirical Findings in Emerging Countries

Name Aim Methodology Findings

Hassan and Refai

(2012)

Investigated the

effect of

macroeconomic

variables on equity

returns in Jordan

General to specific

methodology and

ARDL approach to

cointegration

Findings show that trade surplus, foreign exchange, money supply

and oil prices are important macroeconomic variables as they

show long – run impact on Jordanian stock exchange market.

Hooker (2004)

To test how

macroeconomic

figures prominently

in analysing

emerging markets.

Predicting power of

macroeconomic

factors for emerging

equity returns.

Bayesian model

specification

approach

Result derived provides strong evidence against all

macroeconomic factors except exchange rate changes; interest rate

and GDP forecast change.

Hasan and Javed

(2009)

Examine the long and

short – run causal

relationship between

macroeconomic

variables and equity

market returns in

Pakistan from 1998 to

Vector autoregressive

model estimation

Findings show evidence of long – term relationship. It also show

uni – directional causality between the following variables;

exchange rate, money supply, interest rate and equity returns. No

Granger causality between industrial production index, foreign

portfolio and equity returns.

Mazuruse (2014) To analyse the impact

of macroeconomic

variables on stock

returns for Zimbabwe

stock exchange.

Canonical correlation

analysis (CCA) was

employed on monthly

data from 1990 to

The results show that maximisation of stock returns at Zimbabwe

stock exchange is mostly influenced by money supply, exchange

rate, treasury bills and consumer price index.

Gupta and Reid

(2013)

To investigate the

sensitivity of industry

specific stock returns

to macroeconomic

news and monetary

policy in South

African Stock

exchange market.

Bayesian vector

autoregressive

analysis.

The findings show that monetary surprise was the only variable

that has a negative and consistent effect on stock returns. The

consumer price index and production price index also show

significant impact in the gold mining index.

Nishi (2015) To investigate the

linkage between

exchange rate and

stock returns.

Cointegration

approach employed

to analyse monthly

data from 2007m1 to

2015m3.

Findings show long – run relationship between exchange rate and

stock returns. The sign of the correlation is negative. There is an

evidence of bi – directional causality for stock return of each sector

except the media and pharmaceutical.

Pimentel and

Choudhry (2014)

To analyse the

interest rate and

inflation with stock

returns in Brazil

during both inflation

Vector autoregressive

and granger causality

models.

Findings show a bi – directional relationship between inflation and

stock returns. The result also show that shocks in interest rates

influence future stock returns.

and monetary

stability.

Sayilgan and Suslu

(2011)

To analyse the impact

of macroeconomic

factors on stock

returns

Ordinary least square

method

Findings show that stock returns are affected by the exchange rate,

S & P 500 and the inflation rate and not affected by money supply,

interest rate, oil prices and gross domestic product statistically

Kasman et al. (2011) To investigate the

effects of exchange

rate and interest rate

changes on bank’s

stock returns in

Turkey.

Ordinary least square

method and

Generalized

autoregressive

conditional

heteroscedasticity.

The result shows a negative and significant impact of both

exchange rate and interest rate on stock returns.

Williams (2011) To examine the

macroeconomic

variables and the

stock market in

Nigeria

Error correction

Findings show a significant negative short run between minimum

re-discounting rate and the stock market. Long-run exchange rate

stability impacts stock market positively. Treasury bills and

inflation rates are not significant.

Khan and Senhadji

(2000)

To examine the

impact of

macroeconomic

variables on stock

returns in Pakistan.

Cointegration and

vector error

correction model

Findings indicate that all variables except money supply have

significant impact on stock returns in Pakistan.

Iskenderoglu et al.

(2011)

To examine the effect

of industrial

production on the

stock market in

Turkey.

Johansen

cointegration and

error correction

Findings show that industrial production index uni-directionally

affects stock market in Turkey.

Osamwonyi and

Kasimu (2013)

To investigate the

causality effect of

economic growth on

stock markets in

Nigeria, Kenya and

Ghana.

Johansen

cointegration and

Granger causality

Findings show no causality between economic growth and stock

market development in Nigeria and Ghana, while bi-directional

causality was found between stock market development and

economic growth in Kenya.

Al – Mukit (2013) To investigate the

predictive power of

interest rate volatility

on stock market

returns in

Bangladesh.

Johansen approach of

cointegration and

Granger causality.

The result shows a long -b run relationship with negative and

significant coefficients on stock market returns. The Granger

causality suggest a uni – directional relationship.

Addo and Sunzuoye

(2013)

To examine the effect

of macro variables on

the stock market in

Ghana.

Johansen’s

multivariate

cointegration tests.

The result provided an evidence that supports a long – run

relationship between interest rate, treasury bills and stock market

return. The result highlights a weak negative relationship between

interest rate and stock market returns in Ghana.

Fang and Bessler

(2017)

To investigate

whether interest rates

explain the

predictable

movements of stock

returns in China.

Prequential analysis,

vector autoregressive

and autoregressive

model.

The result shows that the inclusion of interest rate in the model

improves the ability of realistic predictability of stock returns,

thereby suggests that interest rate helps in forecasting stock returns

in China.

2.5 Summary

This section is in three different parts; the first part reviewed the historical background of the

MINT countries’ stock exchange markets and how some basic policies had been formed to help

in the development of each of the stock markets, while the second part critically reviewed key

theoretical frameworks that describe market behaviour. Theories that point out the possible link

between stock markets and macroeconomic variables were elaborated. Looking at the various

theories, we identified the APT as the theory that explains the phenomenon this research is set

to investigate; although we discovered that the APT was unable to specify the set of

macroeconomic variables that influence the stock market or the restrictions that should be

followed in the inclusion or exclusion of variables whenever an empirical study of this nature

is carried out. However, numerous researches that have investigated how macroeconomic

factors influence stock market have followed the pattern of the proponent of complete market

segmentation. Investigating the impact of country macroeconomic variables on the stock

market will not give a whole perception if the impact of global factors such as commodity

prices and Federal funds rate are not considered alongside.

Researchers over the years could not agree on how many or what factors should be included

when this type of research is carried out, and this has given supporters of APT the freedom to

explore different variables which lead to data dredging (Jamal, 2014). It is also observed that

researchers prefer to attach names to factors as it will provide a linkage between economic

events and corporate strategies as suggested by (Muhammad, 2011). The third part has

extensively reviewed existing literature to know the various country and global macroeconomic

variables proposed by researchers and also to understand the trend of the impact of the

macroeconomic variables on the stock market and the availability of similar research in each

of the MINT countries.

The stock price index has been used interchangeably with stock returns in research. According

to Hubbard & O’Brien (2012), it is possible for stock prices to be on a downward trend when

the return on stock is high. This means stock returns and stock prices do not always follow the

same trend. Stock return is the yield that investors expect to obtain as a result of their

investments which will be over a specific period of time. Stock return is an index that both

government and investors study to make investment decisions and policy adjustments. As long

as stock returns are higher than costs of capital, investors of varying financial capacity will

always be attracted to the stock market.

Stock returns is what determines the efficiency and effectiveness of how stock markets allocate

equities and shares based on preferences and information availability in the market. The

demand and supply of stocks in the market is affected by increase or decrease in price which

invariably creates uncertainty for investors. This connotes that stock markets are sensitive to

price – shaping information relevant for market development and determining future trends

(Sirucek, 2013).

Higher profitability in firms is reflected in the stock returns and thus the overall prosperity of

an economy (Aliyu, 2012). Uncertainty in the return of stocks is seen as a vital aspect of the

economy. Stock return is the percentage gain or loss in the value of shares at a particular time.

The stock market index is one of the widely used measure of stock market performance. Enen

though a number of literature used stock prices to represent the performance of the stock

market, this research uses stock returns as investors are particular about returns than prices. It

is therefore important to compare the results of researchers who used stock return as the main

ingredient in their model.

Researchers who have tried to examine the interaction between the stock returns and

macroeconomic variables in Indonesia have done so and arrived at a contradictory conclusion.

Assefa et al. (2017) included the MSCI as a proxy for global equity index, the results show a

negative impact of interest rate and a positive impact of global equity index on stock returns.

Surbakti et al. (2016) included Dow Jones index as a proxy for global euity index and the result

also confirms a positive impact on stock returns in indonesia. However, the result on the impact

of exchange rate, which is an important variable on stock returns in Jakarta stock exchange

show that there are variations in the result of researchers. Jamaludin et al. (2017) suggest a

positive relationship while Najafzadeh et al. (2016) suggest a negative effect of exchange rate

on stock returns. Another researcher suggest no impact of exchange rate on stock return.

Surbakti et al. (2016) supports Jamaludin et al. (2017) by suggesting a positive impact of

exchange rate on stock returns in Indonesia. Bhayu & Rider (2012) has mentioned the

importance of studying the movement of exchange rate during economic surveillance process

in the nation, therefore, having a different report on the exchange rate impact on the stock

market could mislead policymakers which would result in little or no reliance on this type of

study. It is of great importance to further investigate the interaction between these variables

using more than a single statistical analysis which is what this research is set to do.

A number of researchers (see Olorunleke, 2014; Olukayode & Atanda, 2010) have

concentrated on how macroeconomic factors influence stock returns in the Nigerian Stock

Exchange market. In fact, the author noticed that Nigeria when compared to other countries in

the MINT group, received the greatest amount of attention from scholars who are interested in

investigating the interaction between macroeconomic variables and the stock market; of which

most researchers have found a long-run relationship between macroeconomic variables and the

stock market. However, there has been discrepancies in the outcome (especially in the area of

determining whether the relationship is positive, negative or not significant). The result of the

research conducted by Izedonmi and Abdullahi (2011) show no statistical significance

influence of the impact of exchange rate on stock returns, this is in support of the the findings

of Nkoro et al. (2013). However, Olukayode and Atanda (2010) presented evidence to support

exchange rate as one of the leading variables that determines stock returns in Nigeria. There is

a need to investigate the relationship between excahnge rate and stock returns, this study

includes more variables to understand how domestic and global macroeconomic variables

influences stock returns in Nigeria.

Researchers (e.g. Tursoy et al. 2009; Kadir, 2008) have sought to understand the interaction

between macroeconomic variables and the stock market in Turkey. Some of these researchers

use typical macroeconomic variables such as consumer price index, exchange rate, interest rate

and industrial production in their study. Tursoy et al. (2009) in their study suggest no

significance in the interaction between exchange rate and all portfolio returns in the Istanbul

stock exchange market, also interest rate show significance in explaining just one out of the

seven portfolio selected which means some other macroeconomic variables other than the two

can better explain portfolio returns.Acikalin et al. (2008) show a long and consistent

relationship between exchange rate, interest rate and stock returns, this supports the findings

of Ozlen & Ergun (2012) where they suggest that interest rate and exchange rate are major

determinants of the stock market. Kadir (2008) also, in his conclusions, confirms a positive

long-run equilibrium relationship between consumer price index, exchange rate and the stock

market, and a negative relationship between interest rate and the stock market. The findings

also show no significant impact of industrial production on stock returns. Since the study of

Tursoy et al. (2009) is not in support of the exchange rate as one of the macroeconomic variable

that influences major portfolios in the stock market, it is therefore of great interest to employ

various methodology to confirm or oppose this stance. The contradiction in the findings of

these researchers can only be clarified if a thorough investigation such as this is carried out to

ascertain whether exchange rate and some other macroeconomic variables influence the stock

market in Turkey or not.

The main gap in the literature is that most researchers have followed the proponents of market

segmentation and concentrated on domestic macroeconomic variables as the major

determinants of movement in stock prices. Although researchers such as Okoli (2012) try to

bridge this by including global factors in explaining the variation in the Nigerian stock market

using the GARCH model, the aim of the researcher was to examine the relationship between

macroeconomic variables and stock market volatilities. However, this particular piece seeks to

know the short- and long-run impact of these variables as well as the causal link between them.

This research helps to clarify whether the MINT countries’ stock markets are segmented,

integrated or partially integrated. The empirical evidence on the interaction between stock

market and commodity price index shows that most researchers concentrate on the main

commodities, such as oil and gold. In this day and age where oil prices have dropped

significantly and most oil-dependent nations are trying to shift their focus on other commodities

like agriculture and metals, it is important to examine the impact of changes in both fuel and

non-fuel price indices which are part of the task in this study.

To be able to substantiate some of the findings of these researchers, it is paramount to follow

necessary steps. Kryzonowski et al. (1994) posit that the main issue in factor selection is a

multicollinearity problem; therefore, this research takes the multicollinearity issue as an

important issue to be considered to have an unbiased model estimation so as to avoid

misleading results. Macroeconomic factors are also known to be vulnerable to measurement

problems when examined over a short period (Chen and Jordan, 1993). To eradicate the issue

of the measurement problem, this research is set to adopt the sample size measurement in Green

(1991). The author of this study has therefore proposed three national and two global

macroeconomic variables which can be used as a proxy for the extensive list of variables used

by past researchers, and this would be elaborated further in the subsequent chapter. The

findings in the literature have also given an idea that empirical evidences are more for some

countries than others. Countries like Nigeria, Turkey and Indonesia have more numbers when

compared with Mexico. There is a certain link between stock market and macroeconomic

variables, but it could vary as a result of the sensitivity of variables selected.

CHAPTER THREE

3.0 Conceptual Framework

3.1 Introduction

This chapter is set to outline the broad concept of how the theoretical assumptions is used to

develop the interaction between the stock market and macroeconomic variables. The stock

market is a very broad concept that has been looked at by various researchers over the past

years. Investors get wealthier through acquiring dividends and are viewed as participants in

companies whose shares they hold. In as much as investors make money through the market,

companies, however, benefit by raising capital to expand their businesses to make more profits.

The stock market has a group of buyers of stocks who put in a bidding price or the price they

are willing to pay for a share; meanwhile sellers put in an asking price or the price they are

willing to sell their shares. The stock market is an organized place that connects these groups

of people together by revealing a minimum asking price and a maximum buying price-- which

is the equilibrium price to investors. With the existence of various investment options available

to investors, it is worth asking why some investors prefer to purchase stocks rather than gold,

bonds or, better still, keep their funds as savings to attract little or no risks.

An increase in the value of assets, which in turn increases wealth or appreciation of stock

creates capital gains, is one of the reasons why investors stick to stocks. Companies generate

profits from providing goods and services while shareholders are compensated with dividends

paid from profits earned. Gold, bonds and savings do not generate much income like stocks in

the long run. The impact of inflation on savings causes holders to become poorer when they

compare the value of their cash to what it can purchase.

For some reasons, the measurement of a stock market has been a difficult task. However,

researchers have resulted in the use of stock prices (see Izedonmi & Abdullahi, 2011) and stock

returns (see Okpara & Odionye, 2012) as a proxy for the stock market. The stock price index

is the price a seller gets out of selling a stock and stock return is the dividend and other benefits

derived from holding a stock.

In the previous chapter, APT is silent on the number of macroeconomic variables that should

be included in factors that influence the stock market. The discounted cash flow technique,

however, gives a guide to calculating stock prices, stated cash flows and rate of return as the

main subjects used in the calculation. This opens the choices of researchers to factors that

directly or indirectly influence asset prices. Researchers have derived varying results in

examining the impact of macroeconomic factors on the stock market (see Bhayu & Rider,

2012). This chapter is set to identify and visualize by narrating and showing graphical

illustration, the key factors and the presumed relationship that exists between the variables.

3.2 Broad Model

This part of the thesis specifies how the concept of this research is arranged. To understand the

model, Figure 9 shows a part that connects macroeconomic factors to the stock market using a

direction arrow. Macroeconomic variables utilized in this research consist of the in-country

and global factors. The stock market in the illustration is the main variable. To measure the

performance of the stock market, one could either use stock returns or stock prices as a basic

measurement. There are two main factors that impact stock market – microeconomic and

macroeconomic variables. Although factors such as war, terrorism and political instability

contribute to the cause of volatility in the stock market (see Gunay, 2016; Essaddam &

Karagianis, 2014), it is almost impossible to quantitatively measure these factors, hence why

studies ensure that they investigate the impact of macroeconomic factors that can be predicted

on the stock market. The selection of global and country macroeconomic variables is made by

considering the kind of literature that covers proxies for macroeconomic factors. An explicit

review is carried out on each of these factors.

Variables are observed to understand their trend and movement, while a stationarity check is

performed to ascertain their movement using statistical measurement (Hill et al., 2008). Since

there is a way out when variables are not stationary, the first differenced data is used for our

estimation (Gujarati & Porter, 2009). Tests could reveal whether variables are cointegrated or

not (a way of checking the long-run relationship between variables). The unrestricted vector

autoregressive model is employed to estimate the set of variables that are not cointegrated,

while restricted vector autoregressive, which is known as vector error correction model, is used

to investigate variables that are cointegrated (Enders, 2004).

Figure 7 Broad flow chart

macroeconomic indicators

Graphical visualization of the variables and determination of stationarity (objective 2)

Not cointegrated?

Determine short-term relationship between macroeconomic indicators

and stock market (VAR model)

(objective 3)

Cointegrated?

Determine the short term relationship between macroeconomic indicators

and stock market index

(VECM)

(objective 3)

Determine causality between macroeconomic indicators and stock

market index

Check for stationarity of variables using unit root tests (objective 2)

Not Stationary? Transform variables into stationary form by taking data in

differenced state

check for Cointegration among variables

(Objective 3)

Global macroeconomic indicator: Selecting important global indicators (contribution:

introducing Federal funds rate).

Country macroeconomic indicators: selecting indicators that are most

important determinants of country growth and development

Unification of the concept of global and country macro indicators and stock market index with regards to chosen time period (1993 – 2014)

Stock market

microeconomic indicators

3.2 Stock Market

The stock market is an organised market where shares of companies are publicly traded. It is

one of the most vital components of a free market economy, as it gives companies access to

capital exchange for a slice of ownership in the enterprise; this means equity is the claim of

owners of a firm (Zvi et al., 2009). It gives room for people to become wealthy without

necessarily taking the risk of starting a business. The stock market is an avenue to raise capital

for expansion of companies through sales of shares to the investing public (Arcot et al., 2007).

It helps to mobilize savings for investment. Rationality in the allocation of funds is achieved,

as funds that have been kept idle or consumed are mobilized through banks and directed into

promoting business activities which are of benefit to several sectors. The stock market,

therefore, creates opportunities for small investors who require huge capital to start up their

businesses; however, investing in stocks is open to both small and large stocks which means a

person can buy the number of shares they can afford (Naik & Padhi, 2012). Investors, therefore,

make money through the dividend they get and also participate in the financial success of

companies whose shares they hold (Mishkin & Eakins, 2009).

The stock price of a company is the current market price of a single stock in that company.

Stocks are traded in the stock exchange market; buyers put in a bid price or the price at which

they are willing to buy a stock. Sellers, on the other hand, put in an asking price or the price at

which they are willing to sell a stock (Houthakker & Williamson, 1996). The stock market

connects deficit and surplus units. It is a regulated institution that helps to prevent unethical

and unfair trading practices and sets a minimum asking price and the maximum buying price

(equilibrium price) (see Saunders & Cornett, 2015). Positive or negative information about a

company increases or decreases the number of stocks traded over a period which is known as

market volume; this includes the quantity of shares that changed owners over a period (see Sun,

2003). The market capitalization represents the total number of issued shares of listed

companies; it is calculated as current stock price multiplied by the number of outstanding

shares the company has issued (World Federation of Exchanges, 2010).

People invest in stocks because stocks appreciate in price, creating capital gains or an increase

in the value of your assets, which grows wealth (Houthakker & Williamson, 1996). Companies

also generate profits/earnings from providing goods and services (income generation). If the

return on stock is high, but stock prices keep going down, holders lose money regardless

(Hubbard & O'Brien, 2012).

Stock market indices are used to help understand the level of price and trend movement of the

market; this is done by selecting some stocks that can be an accurate representation of the whole

market or a specified sector of the market. Investors use stock indices to gain some insight on

how stocks have performed (Mishkin & Eakins, 2009).

3.2.1 Exchange Rate

The exchange rate has been a major driver of some developing economies. There is, however,

two main types of the exchange rate, which are nominal and real exchange rates. There are also

various types of exchange rate regimes that countries practise which can be suppressed into

floating and fixed exchange rate regimes. The nominal exchange rate is the value of foreign

currency regarding a domestic currency, while the real exchange rate is the unit of international

goods or services the domestic currency can purchase (see Krugman & Wells, 2013). The

nominal exchange rate is recorded based on two different perspectives; the first being the unit

of the foreign currency per unit of domestic currency, while the second represents the unit of

domestic currency per unit of foreign currency. Looking at the two perspectives, it is obvious

the latter is the reciprocal of the former.

Most countries have their national currencies, except a number of EU countries which use the

Euro as their common currency, and Panama, which uses the US Dollar as its national currency

(Abel et al., 2008). The exchange rate could be expressed using either of the scenarios, but one

must be mindful when interpreting a rise or fall in the exchange rate as it would be different

depending on the definition used. Applying the second definition, an increase in the Mexican

Peso per Dollar exchange rate from, say, MXN 0.050/1 USD to MXN 0.080/1 USD means

more Mexican Pesos have to be given to obtain a dollar, which shows depreciation in the value

of the Peso. If the first definition is employed, a rise in the Dollar per Peso exchange rate from

1 USD/0.050 MXN to 1.20 USD /0.050 MXN would mean that more dollars are given to obtain

a Peso in the exchange rate market, saying the Peso has appreciated in value or the dollar has

depreciated. The movement in exchange rate plays a pivotal role in determining the behaviour

of foreign transactions, whether for trade or investment (Frankel, 1999).

There are two major types of exchange rate regimes: flexible and fixed exchange rate regimes.

A flexible exchange rate is also referred to as floating exchange rate regime. Under the flexible

exchange rate regime, the exchange rate is determined without the intervention of the

authorities. Rather, the government allows the currency to fluctuate due to supply and demand

of the currency (Parkin, 2014). This apparently means that the exchange rate is a market

determined by the forces of demand and supply and authorities do nothing to affect their

exchange rate directly. For more clarity, we examine the market in two countries’ currencies:

the US Dollar and Mexican Peso.

Whenever there is an increase in demand for dollars in Mexico, from D0 to D1, the equilibrium

at point A will move to point B where there will be more supply for dollars. This pushes the

exchange rate from ER0 to ER1 and equilibrates the supply and demand for dollars in the foreign

market (Mexico). This illustration shows that more Mexican Pesos are needed in exchange for

US Dollars. The value of the dollar against the Mexican Peso gives rise to prices of goods that

are imported from the US. An increase in demand, therefore, leads to an appreciation of dollars

against the Peso.

Increase in demand

Market for Dollars in Mexico

Quantity of Dollars supply

Figure 8 Market for Dollars in Mexico (Increase in Demand)

Figures 8 and Figure 9 show that demand for US Dollars in the Mexican market increases the

supply of Mexican Pesos from S1 to S2 which results in the depreciation of Peso from ER1 to

ER0. The shift in supply means that a lesser amount of dollars is needed to be exchanged for

Pesos in the foreign exchange market. This graph helps to understand an exchange rate regime

without any intervention. Friedman (1953) attributed more advantages to this type of exchange

rate regime. He argued that the flexible exchange rate is a better shock absorber for the

economy.

Fixed Exchange Rate

Unlike the flexible exchange rate, in the case of a fixed exchange rate regime, authorities are

allowed to intervene by adjusting the exchange rate around a certain level. For example, a

depreciation of a currency is undesirable since it leads to an increase in prices of imported

goods and sometimes endangers the central bank’s inflation target, while appreciation of a

currency could hit the economy’s export (Parkin, 2014).

The fixed exchange rate regime is vital for economic stability. However, Friedman stressed

that a fixed exchange rate is dangerous. It is the fixing of the price of a foreign currency

regarding a local currency, and for one reason or the other, prices do not correspond to the

equilibrium rate in the foreign exchange market (Froyen, 2013). In the case where the domestic

currency is undervalued, there is always a similar case of balance of payment surplus in the

country; this arises when countries accumulate foreign exchange (Krugman & Wells, 2013).

Increase in supply

Market for Peso in the US

Quantity of Peso supply

Figure 9 Market for Peso in the United States (Increase in Supply)

Alternatively, the government could restrict exports and encourage imports by promoting an

increase in prices of domestic goods (inflation). This is a way of taxing exports and subsidizing

imports. They could also impose increased taxes on exports or reduce taxes on imports.

However, in the case of an over-valued domestic currency, the country experiences a deficit in

the balance of payment which leads to borrowing or decreasing foreign reserves.

In most cases, the choices of authorities differ until disequilibrium in the currency market

reaches the crisis point; decisions made during a crisis period are unlikely to be carefully

reasoned out and could have a severe disruption in the economy. Frankel (1999) argued that

the choice of an appropriate exchange rate regime, especially in countries where they have few

restrictions on the capital financial account, serves as an important determinant of economic

stability.

Johnson (1969) argued that a flexible exchange rate regime is attributed to an acute monetary

system in the 1930s. Most people have the notion that, when introduced, a flexible rate system

would only display in an exaggerated fashion the worst features of the fixed exchange rate

system rather than remedy it. He further explained the uncertainties that occur in the prediction

of floating or flexible exchange rates. He added that rates have the capability to jump wildly

from day to day, this he reckons is as a result of changes in demand and supply which is not an

erratic movement. It also responds to changes that are induced by government policies which

would move at a slow pace and can hardly be predictable.

If movements in exchange rates are predictable, they help in providing compensation to

investors and traders. A flexible exchange rate regime gives room for an independent monetary

policy which may be abused because governments are not under compulsion to prevent

currency depreciation. This could tempt them into inflationary monetary policies (see Froyen,

2013). Flexible exchange rates, according to Johnson, can be unpredictable which may create

difficulties for businesses in pricing and planning, whereas, when the exchange rate is

predictable, there would be a possibility to hedge against uncertainties (Johnson, 1969).

Examining flexible exchange rates on the other hand, when demand and supply (market forces)

determine the rate, there is a possibility of efficient resource allocation. Domestic monetary

aggregates will not be affected by external flows, and countries would have the independence

of pursuing monetary policies that suit them (see Krugman & Wells, 2013).

The fixed rate regime provides businesses with the sure basis for pricing and planning and

thereby contributes to boosting investment and international trade. It also helps to put checks

or constraints on domestic monetary policy. These constraints are imposed by the country in

which their national currency is fixed whenever a local stance is not in line with theirs (see

Latter, 1996). This promotes discipline in the monetary policy decisions of countries. Fixed

rate, however, is prone to some dangers; it could be vulnerable to speculative attacks which

may result in damaging consequences (see Froyen, 2013). Since there is no sure way of

establishing whether a chosen threshold is sustainable, there may be a question of credibility

in the choice of the authority. This is because a government cannot know better than the

markets where the equilibrium lies (Abel et al., 2008). One of the requirements of this regime

is that government or the central bank must stand ready to intervene in the foreign exchange

market. Decisions are needed on how to keep up with the domestic monetary consequence, i.e.

sterilize or not, which doesn’t work in some cases.

Exchange rate regimes vary with the level of financial development of countries. The choice

of exchange rate regime, therefore, stands as the most controversial aspect of macroeconomic

policy (see Aghion et al., 2009). Since the exchange rate is one of the macroeconomic variables

looked at in this study, it is important to, therefore, understand the historical background of the

MINT countries’ currency and exchange rate policies.

Mexico

The Mexican Peso is the most traded emerging market currency (Cota, 2016). From 1988 to

1994, the economic policy in Mexico was subjected to the national agreement (Pacto) among

the government, businesses and workers. This agreement was reached to bring the level of

inflation down since it has been established that exchange rate is a determinant factor of

stability in the economy. Under the agreement, the monetary policy supports the exchange rate

regime (a crawling peg) although the policy brought down annual inflation from 159.2% to

7.1%. However, there was no reliable preparation of sustaining the stabilization as the

exchange rate was not flexible in the midst of political instability. Over-valuation of currency

and an increase in current account deficit eventually led to the sudden devaluation of thePeso

causing the most severe economic crisis in the history of Mexico. After the crisis, a flexible

exchange rate regime was adopted (Whitt, 1996).

Indonesia

The administration in Indonesia adopted a fixed exchange rate regime in 1971, with a local

currency of 415 Rupiah to 1 US Dollar. Despite this type of exchange rate regime, the economy

experienced inflation as high as 20% in 1972, which doubled in a couple of years after. Despite

the high inflation, the fixed exchange rate regime was maintained until 1973 (see Baillie &

McMahon, 1989). However, the combination of high inflation and decrease in foreign reserves

led to the devaluation of the Rupiah to Rp 625 to 1 US Dollar. The government had no choice

but to abandon the fixed exchange rate regime by altering the economic policy to form a

managed float type of exchange rate (see Rajan, 2010).

It is worthy of note that most financial crises, including the Mexico crisis 1994 and the Asian

financial crisis in 1997, were as a result of exchange rate regimes adopted by those countries

involved (see Whitt, 1996; Eichengreen & Hausmann, 1999). Thailand found it difficult to

maintain a pegged currency (25 Thai Baht to 1 US Dollar); this led to the abandonment of the

defence of the Thai Baht giving room for it to float freely (see Boyes & Melvin, 2012).

Indonesia’s response to widening the managed float/band from 8% to 12%, of which a similar

step was taken during the Mexican financial crisis from 2% to 3%, was the cause of the

meltdown. This led to a drop in investors’ confidence in the market; most of them had to move

their Rupiah investments to US Dollar-denominated investments, which caused a further

depreciation of the currency, thereby increasing demand for dollars in the foreign exchange

market in Indonesia.

Nigeria

The Naira (Nigerian currency) was first introduced in 1973 replacing the pound at a rate of 2

NGN to 1 pound. It is almost an impossible task to maintain a realistic exchange rate in Nigeria

given the structure of the economy. In an import-dependent country like Nigeria, the exchange

rate is used to measure current account performance, competitiveness and foreign reserves

(Adeoye & Saibu, 2014). Between 1980 and 1986, the monetary authority in Nigeria adopted

a fixed exchange rate system. Oil glut of the early 1980s made it clear that the Nigerian

economy is unable to sustain a fixed exchange rate regime because of the depletion of reserves

and accumulation of foreign debt (see Budina & Pang, 2007). During the fixed exchange rate

regime, the Naira was over-valued and foreign reserves fell drastically during the devaluation

year when authorities tried to save the currency in early 1986. The Nigerian monetary authority

adopted a floating regime in the first quarter of 1992 by merging market rates with the official

exchange rate; although there was an adverse reaction of foreign reserve to this, the shocks,

however, diminished afterwards by subsequent increases in the country’s reserve (see Adeoye

& Saibu, 2014).

Turkey

The Turkish Lira (TL) is the domestic currency of Turkey and was pegged with a band of 2–

8TL to 1 US Dollar in 1946 up until 1960. The value of the Lira began to fall as a hard peg

gradually became a soft peg. The currency was awarded the world’s least valuable currency in

1999 through to 2004 at 154,400,000 to 1 US Dollar. The Lira was revalued by removing the

six zeros, which made the new currency (also known as Lira) 1.54 new Turkish Lira to 1 US

Dollar – that is 1 new Lira is equivalent to 1,000,000 old Lira. The TL is the sixteenth most

traded currency in the world (BIS, 2013).

3.2.2 Interest Rates

Developed and developing economies are getting accustomed to relying on interest rates as a

primary monetary policy tool; therefore, they target interbank rates as an intermediate policy

target (Saborowski & Weber, 2013). The interest rate, according to the loanable funds theory,

is the price of market loanable funds. Loanable funds are cash that is made available for

investment purposes. The suppliers of loanable funds through deposits are households, and the

lenders are the loan demanders (corporations, governments and private organisations).

Borrowers, like governments, are also bond suppliers. This explanation simply shows that

interest rates are expected to be inversely related to the prices of bonds. It also shows that the

quantity of loanable funds supplied and interest rates are positively related. Figures 12 and 13

show the relationships between interest rates and bonds, as well as interest rates and loanable

funds.

Figure 10 The Relationship Between Interest Rates and Bonds

Loanable funds

Demand

Supply

The graphical illustrations show the inverse relationship between government bonds and

interest rates on money and the positive relationship between interest rates and loanable funds.

The first graph shows that as interest rate paid on money increases, there is an expectation of a

corresponding decrease in the sale of bonds as holders of wealth would prefer to hold their

money. The supply graph indicates that an increase in interest paid on money means more

loanable funds is supplied to meet such demands. This is as a result of the willingness of lenders

of funds to earn a higher interest on their money. Interest rate movements are pro-cyclical; this

means that interest rates increase during the economic boom and decreases in the time of

recession. During the economic boom, there is an increase in both demand and supply of

loanable funds; however, there is a tendency that demand would exceed the supply in the

market which shifts the equilibrium interest rate upward (Abel et al., 2008). This is illustrated

in the graph below;

The illustration above shows the shift in both demand and supply, and the new equilibrium

point. According to Fisher’s time preference theory, interest is said to be the price for time

preference. Keynes argues that interest is the reward for parting with liquidity. His theory does

not only explain why the rate of interest is paid but argues further to explain how they are

determined. Lending rate is the bank rate that meets both short and medium-term financing

needs of the private sector. It differs as it depends on the creditworthiness of borrowers as well

as the objectives of investment. Loan demand in economies with developed market is expected

to react more intensely to changes in interest rate because firms rely on alternative sources of

financing (equity) rather than loans from banks.

Interest Rate in Mexico

Quantity of loanable funds supplied

𝑄1 𝑄2

Figure 11 The Shift of Demand and Supply

Banxico (Central Bank of Mexico) is the authority that steers the wheels of the level of interest

rate in Mexico. This is possible with the help of monetary policy. Mexico’s interest rate is

known as the overnight interbank rate. The board of governors determines this; the overnight

interbank rate is the rate Banxico pursues interbank loans with a maturity date. The policy

interest rate determines the interest rate in the economy since it is the price at which private

agents, especially banks, obtain money from the central bank. The Central Bank of Mexico’s

board of governors meets up to eight times in a year to decide on the suitability of the monetary

policy for the economy (see Calafell, 2015). Since the switching of the exchange rate regime

to a free-floating type, Banxico adopted a monetary framework of inflation targeting.

Banxico’s most important instrument used in the monetary policy is targeting the overnight

interbank interest rate. It was formally announced in 2008 that Banxico adopted the operating

target for the overnight interest rate as a means to control liquidity in the market. This serves

as a signal for the market participant to know the direction that interest rate will follow in the

future (Banco De Mexico, 2008).

In 2009, Banxico reduced the overnight rate by between 7% and 8.25% in 2008 to 4.50%; this

was done to help improve credit conditions and to stimulate a slowing economy during the

global financial crisis. Banxico is committed to maintaining a low interest rate to support weak

economic growth in Mexico (see Focus Economics, 2015).

Interest Rate in Indonesia

The Central Bank of Indonesia, which is also known as Bank Indonesia (BI), is the leading

authority that determines the level of interest rates in the economy. Indonesia’s interest rate is

often referred to as the BI rate; it is used by the central bank to shape the monetary policy. The

rate is reflected in a decrease/increase of the interbank rate. When the BI rate changes, the

interbank rate responds to the changes. This is also a determinant factor of the movement of

interest rates that banks apply to savings, mortgage and loans (BI , 2013). The nominal interest

rate in Indonesia is the weighted average lending rate on loans to the private sector for working

capital (Lane et al., 1999).

Interest rate in Nigeria

There has been a direct control of interest rate in Nigeria before 1993; interest rate policy in

this period was driven by considerations of promoting overall investment and channelling

credit to priority sectors. The interest rate was very volatile and high to the extent that the

Central Bank of Nigeria adopted a monetary policy rate towards the end of 1992. The

government, however, decided to fix the monetary policy rate and treasury bill rate at 13.5%

and 12.5% respectively. Other savings and loan rates thereby reacted to the shock and declined

to between 13% and 14.27% from their high rate in 1993 (see Ahmed & Hulten, 2014). The

interest rate cap was lifted in 1996 and was left to be determined by the forces of demand and

supply of funds. The problem with this market-based approach is that it results in the high

volatility of interest rates which brings about questioning the overall desirability of the strategy

(Shittu & Onanuga, 2010). The persistent increase in interest rates reduces the return on

investment in the real sector and conserves trading in financial instruments (Wehnam & Jagero,

2013).

Interest Rate in Turkey

Turkey’s interest rate is referred to as the Central Bank of the Republic of Turkey (CBRT) repo

interest rate. The Turkish central bank makes use of this base rate for monetary policy. The

level of the repo interest rate has a direct impact on the level of interest rates for loans, savings

and mortgages (see Yilmaz, 2002). Turkey maintains a tight monetary policy by keeping the

interest rate on hold during inflationary periods. Interest rates in Turkey were at a record high

in the 1990s and were as high as 200% in 1994; it changed in the middle of the same year and

ranged between 60% and 100% afterwards. After the 2007/2008 global financial crisis, interest

rates averaged 6% (CBRT, 2015).

3.2.3 Mexico Economic Growth and Capital Movement

The Mexican economy is known to fluctuate, especially during the slowdowns in 1993 and

1994. However, the impact was evident in 1995, which resulted in a major financial crisis; the

economy recovered a year later and maintained a high growth rate until the late 1990s (Heston

et al., 2011). There was a notable crisis which lasted for about three years; however, the country

recovered in 2004 until the slowdown that was experienced during the global financial crisis

(GFC) in 2007/2008. The impact of the crisis was so much that the GDP dived to negative

figures but recovered in 2010 (Iniguez-montiel, 2014). Mexico’s macroeconomic reforms were

clearly proposed to stabilize inflation, adjust public finances and also to open the domestic

market to reduce state intervention in investment and production. This idea was to make export

the engine that drives the economic growth (Moreno-Brid & Ros, 2009). The reform was

successful and was evident when the country’s manufactured exports increased since the 1980s

from less than 20% of total exports to about 80%. However, a significant proportion of raw

materials used for export goods are imported, which means only a little local content of

intermediate input is in the production process of export produce. Hence many businesses in

the country are seen as assembling firms rather than manufacturing firms.

The slow growth in the economy in the 1980s is attributed to two main factors which are the

surge in the import propensity and the income elasticity of imports. According to the Keynesian

income multiplier, a decrease in national income causes reductions in investment and exports.

Import elasticity eradicated the pull effect that the increase in export had on the total GDP

growth rate (Moreno-Brid, 1999). These factors undermined the international competitiveness

of Mexico’s production sector and made it almost impossible for them to compete with the

foreign competitors that the country is open to, due to the liberalization of trade.

Foreign direct investment (FDI) inflow has become an important subject in developing

countries; this is because it is an alternative source of external financing for the economy.

Countries are, therefore, not focusing on import substitution but encouraging foreign

investment and trade (UNCTAD, 2006). FDI is not only beneficial because of the capital it

brings into the economy but contributes to economic growth by generating employment.

Between 1994 and 2014, FDI flows into Mexico have been on the increase. The US has been

the largest source of FDI (Waldkirch, 2010).

Source: Data from World Development Indicators, graphed by the author.

Figure 12 Mexico, Indonesia, Nigeria and Turkey Foreign Direct Investment Inflow

1985 1990 1995 2000 2005 2010

MEXFDI

-5.0E+09

0.0E+00

5.0E+09

1.0E+10

1.5E+10

2.0E+10

2.5E+10

3.0E+10

1985 1990 1995 2000 2005 2010

IDNFDI

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

9,000,000,000

1985 1990 1995 2000 2005 2010

NGAFDI

0.0E+00

4.0E+09

8.0E+09

1.2E+10

1.6E+10

2.0E+10

2.4E+10

1985 1990 1995 2000 2005 2010

TURFDI

3.2.4 Indonesia Economic Growth and Capital Movement

Economic growth in Indonesia has been healthy apart from the time of the 1997/1998 Asian

financial crisis. The discernible periods saw about 7%–8% growth during the Soeharto-era

boom in 1996. The Asian financial crisis began to show in the economy in the third quarter of

1997. Growth moved to a negative figure after which there was a negligible growth in 1999

(Basri & Hill, 2011). There was a stable growth in the economy from 2000 to 2003. The growth

picked up again in 2004 and was steady until 2008 during the GFC. The impact of the GFC

was mild in 2009, and Indonesia was ranked the third fastest growing economy in the G20

group. Bank Indonesia (the central bank) struggled to keep inflation in check, at least to make

it at par with the neighbouring countries and major trading partners. This caused an increase in

the exchange rate which combined with a hike in commodity prices over the past decades,

putting persistent pressure on tradable goods. Public debt in Indonesia has reduced from 100%

to about 30%; comparing this to other emerging economies, Indonesia is among the low-debt

economies (Chowdhury & Islam, 2011).

There is an ongoing competition among policymakers in developing countries in the area of

attracting FDI (Suyanto & Ruhul, 2010). The food processing industry in Indonesia is one of

the important industries in the economy; it is advantageous for its reliance on local raw

materials to produce outputs. The electrical machinery industry has been a major target of

foreign investments. In 1995, foreign contributions to the sector were up to 50% of total value

added. However, the industry relies heavily on imported materials that are up to 70% of the

total material used (Sjoholm & Lipsey, 2006). The FDI inflow in Indonesia was negative during

the Asian financial crisis and picked up in 2004, and has since maintained a stable and

progressive growth (see Figure 16 below).

3.2.5 Nigeria Economic Growth, Oil Production, and Capital Movement

Since the discovery of crude oil in Oloibiri in the Niger-Delta region of Nigeria in 1956, the

country has abandoned the sources of revenue of primary products like timber, cotton, cocoa

and palm products (Usman, 2007). Oil and gas accounted for more than 98% of export earnings

and 83% of government revenue in the country (Odulari, 2008). The development indicator of

Nigeria’s economy has not improved much despite the impressive export earnings. Oil

production in Nigeria increases the chances of enhancing the level of economic activities and

national income. Odulari (2008), using domestic consumption and crude oil exports as proxies

for oil production, shows empirically that oil production contributes positively to GDP in

Nigeria. His findings were confirmed by Mehrara (2009) where he demonstrated a positive

effect of oil revenue on economic growth of 13 countries which include Nigeria.

FDI is a critical source of long-term capital investment in infrastructure. Nigeria has received

the highest FDI inflow in West Africa (Anyanwu & Yameogo, 2015). Nigeria, unlike other

countries, received reasonable FDI inflows from 1989; however, there has been an increase in

FDI inflows in 2005 and afterwards, especially 2008/2009 (see Figure 12). Could this be as a

result of the contractionary monetary policy during the 2008/2009 GFC ? This question would

be addressed later in this research.

3.2.6 Turkey Economic Growth and Capital Movement

Turkey has recovered and bounced back from some economic problems in recent years.

Presently, they are maintaining a stable economic growth. Turkey’s economy grew by 8.5 %

in 2011, which made them the second biggest country regarding growth rate in the world after

China. Turkey has experienced a subtle growth pattern from the crisis experience in 2000/2001

which caused a sharp depreciation of the country’s currency to the contraction of the GDP by

5.7%. The economy, however, recovered between 2002 and 2007 before it downsized during

the GFC (Macovei, 2008).

FDI inflow in developing countries grew in the 1990s but maintained a stable growth. Turkey

was faced with the problems of national savings which is a factor that aids sustainable growth

in the economy. Turkey, like other emerging economies, focuses on FDI as an important tool

to providing external financing in the economy. Turkey’s FDI slowed down between 1990 and

2000 but picked up and surged in 2005 and onwards. It was also disrupted in 2009/2010 due to

the GFC (see Figure 18). European Union (EU) countries ranked at first place in the gradation

of FDI into Turkey, especially after the acceptance of Turkey as a candidate country for EU.

3.2.7 Federal Funds Rate

During the 2007/2008 GFC, a lot of attention was focused on monetary policy actions taken

by the European Central Bank and the Federal Reserve. The Fed took measures such as keeping

the interest rate low to stimulate the US economy (Goodwin et al., 2014). The Federal funds

rate is the interest rate that banks charge other banks on overnight loans made from their excess

reserves. Whenever the Federal Reserve lowers the Federal funds rate, they do so to ease

monetary policy, i.e. an expansionary monetary policy, and an increase in the Federal funds

rate is a way of introducing a contractionary monetary policy. Whenever there is crisis in the

economy, the Federal Reserve responds to it using three different strategies. Firstly by

providing liquidity through discount window lending and open market operation (buying of

bonds); secondly by lowering the Fed funds rate just as the author has mentioned previously;

and lastly by intervening through the foreign exchange with the US Treasury (Neely, 2004).

The GFC made the Federal Reserve take extraordinary steps. Some of the measures were to

buy treasury bonds and assets in a very large quantity; this resulted in pushing the interest rate

to near zero. Research shows that during this time, countries like India experienced a surge in

their stock market (the Nifty 50 index rose from about 3,041 to 8,368) with an annual rate of

17%. Countries like China also show up to 12% in their rate of return (Marwah et al., 2015).

According to Marwah et al. (2015), the lower interest rate did support the US economy at the

time but also created artificial liquidity in the economy. Most portfolio managers took

advantage of the higher rate of return in the emerging market at this stage; deposits shrank from

1.75% in 2008 to about 0.25% in 2013 in the US. This explains the surge in the capital inflow

into emerging markets from 2008–2010. The FDI illustrations above correspond with this by

showing a surge during this same period.

3.2.8 Global Commodity Price Index

The global commodity price index is a major factor. It consists of fuel and non-fuel commodity

prices. Oil is said to be the lifeblood of modern economies. There is a positive correlation

between urbanization/modernization and oil (Basher & Sadorsky, 2006). Although there has

been the focus on oil when the impact of commodity prices on the stock market is examined

on country-specific stock market (see Sadorsky, 1999; Ghosh & Kanjilal, 2014); however,

there is an urgent need to consider other commodities alongside the fuel products, especially

in this era of a nosediving oil price index. Agricultural and food commodities are also

important. Developing countries that are food importers have seen their import bills more than

double since 2000. There has also been a lingering question of how food exporters can turn an

increase in food prices into an opportunity to boost economic growth. This issue has not been

adequately investigated because of the impact of key inputs, such as fertilizers, which increase

along with product prices and thereby weaken the incentive to produce more (FAO, 2009).

Changes in agricultural prices also influence emerging economies that are dependent on

agricultural commodities for a larger share of their exports. The short-run effect depends on

whether a country is an exporter or importer of agricultural commodities and sometimes on

whose food imports are likely to impact their balance of payment (IMF, 2011). The response

of a country to changes in commodity prices is expected to vary, which means it depends on

whether they are major importers or exporters of commodity prices. This research is set out to

examine how the MINT countries respond to these changes.

3.3 Specific Model

Researchers such as Tursoy et al. (2009) and Kadir (2008) have used stock returns as a

measurement, while Semra & Ayhan (2010) and Asaolu & Ogunmuyiwa (2011) used stock

prices as an essential measurement of stock market performance. This research adopts the use

of stock prices as a proxy for the stock market and how macroeconomic factors such as interest

rates, exchange rates, Federal funds rate, commodity price index and economic growth

influence its movement. Researchers in the past have examined how a large number of

variables affect stock prices, but it will be ideal to have a narrowed down specific

macroeconomic factor to avoid contradictory findings, which has been experienced by past

researchers. Kadir (2008), in his findings, concludes that industrial production, money supply,

and oil prices show no significant influence on stock returns in Turkey. The findings contradict

those of Semra & Ayhan (2010) where industrial production shows short- and long-run

significant impact on the stock market in the same country. To have a levelled basis of

comparison in this type of research, the author proposes three basic country macroeconomic

variables (interest rates, exchange rates and economic growth) and two global factors (Federal

funds rate and commodity price index) of which some of the country variables have been used

in the literature.

Basic macroeconomic variables selected in this research are known as key indicators that

portray the current status of the economy of a nation. Announcements and events on these

factors are monitored by almost everyone in the financial market. After the publication of

alterations in these indicators, volatility is usually noticed in the market but the degree of the

volatility is dependent on how important the indicator is; that is why it is paramount to know

what each indicator represents and how important they are. It is noticeable that researchers like

Tursoy et al. (2009), Adaramola (2011) and Osamwonyi & Osagie (2012) did not include

variables like money supply which is also deliberately omitted from the choice of

macroeconomic variables selected to be tested in this research.

A direct relationship is expected to exist between the amount of money in circulation and the

inflation level. An increase in money supply also devalues purchasing power; as a result,

markets would be able to bear high prices while consumers would be unable to buy a product

at its earlier price because the purchasing power of the currency has weakened. Money supply

is used to control inflationary conditions by the central bank. In most cases, they lower lending

rates and increase borrowing rates to reduce inflation. The instance described shows that money

supply is tightly influenced by inflation, interest rates, and exchange rates which is the more

reason Tursoy et al. (2009) suggest that the result of weak explanatory power depicts that other

macroeconomic factors influence the market than the ones selected in their research.

Commodity prices, especially oil, are another common variable used. Oil is a major commodity

in every nation, and it is said to be used to predict the movement in the exchange rate. Giannone

et al. (2012) using an ‘out-of-sample fit’ test found that commodity prices and exchange rates

show a very robust short-term relationship. Oil-price impact varies from country to country

depending on whether a country is an oil exporter or importer. An example is the Nigerian

Naira which has been under intense pressure as a result of decreasing oil prices. Although the

central bank has been forced to step into the currency market, the impact of the fall of oil prices

still lingers on. This example shows that countries that are oil dependent would feel the impact

of the movement of oil prices or commodity prices on their currency value in the international

market. Commodity price index is included instead of oil prices. This helps to capture other

commodities. The illustration below shows the chart of a mini-model of this study.

Figure 13 Illustration Showing the Conceptual Framework of the Dependent and

Intervening Variables That Are Used in the Research

The illustration above shows five blocks of intervening variables (𝑋1, 𝑋2, 𝑋3 , 𝑋4, 𝑋5, 𝑋6) ,

which represent global and domestic macroeconomic variables and (𝑌) as the dependent

variable which represents stock prices. The model above suggests the equation below:

𝑌 = 𝑓(𝑋1, 𝑋2, 𝑋3 , 𝑋4, 𝑋5, 𝑋6)

Where 𝑌 represents each of the MINT countries’ stock market prices

𝑓 Denotes function of

𝑋1 Represents the interest rates of each of the MINT countries

𝑋2 Represents the US Federal funds rate

𝑋3 Represents the exchange rate of the domestic currencies of MINT countries per US Dollars

𝑋4 Represents the industrial production of each of the MINT countries

𝑋5 Represents the global commodity price index

𝑋6 Represents the Morgan Stanley Capital International (MSCI) equity index

Stock market returns

Interest rate

(𝑋1)

Federal funds rate

(𝑋2)

(global macro variable)

Exchange rate (local currency per US dollars)

(𝑋3)

Economic growth( industrial

and oil price production)

(𝑋4) Morgan Stanley Capital

International equity index

Global commodity price

(𝑋5)

As explained earlier in Chapter 2, there are philosophical views that support both positive and

negative impacts of these intervening variables on the stock market. Some are of the opinion

that there is no relationship between these macroeconomic variables and the MINT countries’

stock markets. However, this research has, therefore, prepared research hypothesis that would

give suggestions on the relationship between these variables.

𝐻01: Macroeconomic variables do not have a significant long-run relationship with the stock

market in MINT countries

𝐻𝐴1: Macroeconomic variables have a significant long-run relationship with the stock market

in MINT countries

𝐻02: Macroeconomic variables do not have a significant short-run relationship with the stock

market in MINT countries

𝐻𝐴2: Macroeconomic variables have a significant short-run relationship with the stock market

in MINT countries

𝐻03: Global factors have a significant influence on stock markets in the MINT countries

𝐻𝐴3: Global factors do not have a significant influence on stock markets in the MINT countries

3.4 Summary

This chapter has shown how variables in this research are selected and conceptualized.

Illustrations showing the pattern and linkage between variables are also clearly seen. Some

variables which influence stock prices are briefly mentioned, and the reason for maintaining a

particular model is highlighted. The next chapter will begin with the methodology, followed

by the justification of variables, expected relationships and also the description of methods that

are used to examine the variables would be specified.

CHAPTER FOUR

4.0. Research Methodology

4.1 Introduction

This chapter is divided into two main parts. The first part gives a description of the research

design which encompasses the philosophy adopted in the research, the research approach,

choice of method, time horizon and techniques employed in the research. The second part

elaborates the methods and procedures, which in simple terms means the data collection and

analysis that are required for this type of research. Various kinds of econometric tests that are

used to ascertain the relationship between country and global macroeconomic factors and stock

markets in the MINT countries are also explained. As mentioned earlier in Chapters 2 and 3,

the APT framework is silent on the selection of basic macroeconomic variables that influence

stock prices. However, the Discounted Cash flow gives a direction by suggesting that asset

prices are a function of cash flows and discount rates, which sheds more light on the selection

of macroeconomic factors that can likely influence stock prices directly or indirectly. This

chapter will define and give the rationale for the selection of five major macroeconomic

variables and their possible linkages to stock prices. Tests that are undertaken are outlined, and

basic decision rules for the tests are clearly spelled out.

4.2 Quantitative method

Newman (1998) asserts that scientific methods that rely on deductive logic and combines

observation and experimentation in the empirical world is the quantitative method. Neuman

suggests that the quantitative method is developed to affirm probabilistic causal laws which

can help generalise about nature or an event. The scientific method gives room for

quantification, allowing observations to be transformed into numerical data. The quantitative

choice is the type that is concerned with a wide range of data. There are some factors that

validate the usage, which are the number of observations and acceptable statistical standards.

These are required to establish whether a result is statistically significant or not. The

quantitative method is usually used when research employs a statistical method. In a

quantitative research, assumption and theories are used to formulate hypothesis; which is

subject to testing and imposing a sample frame.

The research strategy used in this study is the archival type, as this is conducted from existing

data (Flick, 2011). This method uses the positivist paradigm and this is explained below.

4.2.1 Positivist Paradigm

This paradigm is based on a philosophical idea of a French philosopher, Auguste Comte, in the

nineteenth century. Comte emphasized his reason and observation as a means of understanding

human behaviour (Murzi, 2010). He asserted that scientific knowledge is the only means to

reveal the truth about reality. It relies entirely on the senses by distrusting logic and theory as

a way of interpreting sensory experiences (Glenn, 1986). This was established formally in the

twentieth century and was a dominant scientific method at the time. This approach leveraged

on empiricism as adopted by David Hume. The theory suggests that empiricism is a way that

the mind emphasizes the part played by reasoning in knowledge against that played by

experience (Voss, 1993). This means that the truth is acquired through observations and

experiences. White (1991) suggests that knowledge and truth are not purely mental operations

but are products of sensory experience.

Positivism also adopts Rene Descarte’s theory of knowledge. This theory only believes that

reason is the best way to generate knowledge about reality (Voss, 1993). The deductive method

developed by him implies that events are connected to one another and ordered, which means

that reality can be ordered or deduced. Positivism philosophy also assumes that the only facts

that are derived from observations and measurements can be trusted. It claims that the role of

a researcher is limited to the collection and interpretation of data through an objective approach,

and the findings derived are usually quantifiable and observable (Hill & Hoecker-Drysdale,

2001). Newman (1998) suggests that positivism assumes the existence of reality in research,

which means that there is consistency in the meaning of phenomenon between subjects.

The principles of positivism depend on quantifiable observations which often require statistical

analysis. Positivists assert that real events can be explained with logical analysis after empirical

observation. Positivists are effectively empiricists who assume that knowledge is derived from

human experience. Positivists view researchers as an independent group of people with their

studies not influenced by human interests. Independence in this context means that minimal

interaction is had with participants when the research is being carried out.

Without more ado, the description of the positivist paradigm fits the intended purpose of this

research. It is, therefore, important to mention the shortcomings in the adoption of this

philosophy.

The positivist paradigm is criticized because research findings derived from this

philosophy are descriptive, thus they cannot be applied to issues that require in-

depth research.

It lacks regard for the subjective state of individuals.

It relies on experience as a source of knowledge; however, some vital and essential

concepts such as time, space and cause are not based on experience.

In all, positivists are trying to prove that it is possible to study the social world in the way the

natural world is being studied, and they have identified methods that can be used to study the

social world – the value is free and the explanation of causal nature can be provided. Crowther

& Lancaster (2008) suggested that a positivist adopts a deductive approach. This research

concentrates on facts that are derived from data collection that would undergo statistical

analysis, hence the reason for the adoption of this philosophy in this research.

4.2.2 Research Approach, Strategy, Time horizon and Data Type

This research, as mentioned earlier, adopts a deductive approach. This approach develops

hypothesis/questions based on existing theory and formulates a plan to test it (Silverman,

2013). This type of approach suits a research thesis that is concerned with investigating whether

an observed phenomenon fits with expectations when compared to previous research on the

same subject. This approach is the best for positivism philosophy as it allows the statistical

testing and formulation of hypothesis to an acceptable level of probability. Kothari (2004)

describes this approach as the development of a general to a specific which could bring about

knowledge-based theory and the specific knowledge gained in the research process is put to

the test. The following are the characteristics of deductive approach:

The research seeks to explain the relationship between variables

Independence of the researcher in observation

Facts are measured in a quantitative manner

The following are the five stages suggested by Robson (2002):

Figure 14 Quantitative Research Stages

A longitudinal time horizon is used in data collection. This is because the data is repeatedly

collected over an extended period and used to examine the changes over a period. This helps

to study the development and changes in variables. The type of data that is required for this

research is secondary. Secondary data is a type of data that is derived from other people’s

opinion or archives (Newman, 1998). Secondary data is not collected directly by the user but

can be derived from organisational records, government archives, company progress reports

and internet searches, just to mention a few. The type of secondary data that is used in this

research helps to save time as data required for the research is infeasible for the author to gather

directly from each of the countries and government offices. A secondary source of data also

provides a large database, especially financial and economic data. This is what the framework

of this research looks like in a tabular form:

Table 10 Research Design

Research Framework Type Adopted in the Research

Research Philosophy Positivism

Research Approach Deductive

Choice Quantitative

Research Strategy Archival

Time Horizon Longitudinal

Data Type Secondary

Techniques and Procedures Data Collection and Analysis

Deduce testable questions/hypotheses about the relationships between variables (this has been done in the first chapter)

Express how variables can be measured (this has been fufilled in Chapter 3)

Test the variables using one or more techniques

Examine the specific outcome of the inquiry to confirm theory or to suggest modification

Modification of theory and verification by going over the cycle again

4.3 Data Collection

In this research work, data selection is based on the methodology that is explained above;

therefore, it is done by studying part of a population which is known as a sample. The whole

population is also essential, even though a quantitative research of this type predominantly

assumes a positivist point of view. The type of sampling used in this research is the purposive

sampling because the researcher has deliberately selected the sample period. The selection is

based on the time the stock markets started full operations. Stock markets in most emerging

and developing economies began to gain international recognition in the 1990s, and this

attracted foreign investors to their markets (see Tables 2, 3, 4 and 5).

This study employs secondary data as mentioned earlier. This is because financial and

economic variables can only be accessed through secondary sources. Data is therefore sourced

for stock prices from Yahoo Finance, websites of the respective central banks and the Federal

Reserve Economic Data (FRED). Data collected from these sources is for 22 years (1993–

2014), which gives a total number of 264 observations per variable. Based on the fact that stock

prices change on a daily basis, it would have been ideal to use daily data, but most

macroeconomic data is recorded on a monthly, quarterly and yearly basis. It is, therefore,

admissible to use the monthly frequency as stock prices would not have deviated too much

within the space of one month. Moreover, Green (1991) suggested the use of the formula (𝑁 >

50 + 8𝑘) to calculate the ideal number of observations; therefore N, which is the number of

observation is 264. k, the number of predictor is equal to 5. So, therefore, we have (264 > 50

+ (8 5) ) which is, (264 > 90). The outcome shows clearly that the number of observation is

greater than the stated guideline, which allows the author to proceed. Data used in this research

is studied over time; therefore, a time series analysis is utilised.

Variables such as economic growth, exchange rate and interest rates are the selected domestic

macroeconomic factors that are examined. After giving a close look at the pattern in which

researchers have used some of the variables over time, a sampling table is used to examine the

most frequently used country macroeconomic variables chosen by researchers in the past. The

table below shows the names of authors and the period. It also shows the number of times a

variable is selected at random.

Table 11 Sampling Table

IP-Industrial Production INF- Inflation rate IR- Interest rate FR-Foreign reserve

GDP- Gross Domestic Product MS- Money supply RP- Risk premium FD-Fiscal deposit

CPI- Consumer Price Index ER-Exchange rate TB-Treasury bills

Name and year Variables

Kadir (2008) IP CPI MS ER IR

Tursoy et al. (2008)

INF MS ER IR RP

Daferighe and Aye (2009) GDP INF

Osuagwu (2009)

CPI MS ER IR TB

Kutty (2010)

Semar and Ayhan (2010) IP INF

Olukayode and Atanda (2010)

INF MS ER IR

Adaramola (2011) GDP INF MS ER IR

Khan and Senhadji (2010)

INF MS ER IR

Iskenderoglu et al. (2011) IP

Naik and Padhi (2012) IP INF MS ER IR

Akbar et al. (2012)

INF MS

Osamwonyi and Osagie (2012) GDP INF MS

Olorunleke (2014) GDP INF

Muazu and Musah (2014)

INF MS ER

No. of times used 8 13 10 10 11 1-each

The most commonly used variables based on this sampling table are inflation/consumer price

index, exchange rates, interest rates, gross domestic product/industrial production (a measure

of economic growth) and money supply. This research is set to give an equal level of

measurement to variables; therefore, the fast daily changes of stock price is being considered,

and the researcher has decided to examine variables that are available on a monthly basis since

quarterly or annual frequency variables may not capture the actual variations in the movement

of stock prices. Gross domestic product of countries like Nigeria and Indonesia is not available

in monthly frequencies; therefore, industrial production is used as a proxy for the variables.

Money supply, which is the measure of the total amount of currency in circulation, is also a

variable that researchers in this field are interested in because of its impact on inflation, the

value of domestic currency and business cycle. The quantity theory of money suggests that

money supply has a direct relationship with the general price level. This simply implies that an

increase in money supply causes prices of goods to go up, which translates into inflation. Fisher

expresses the relationship between interest rate and expected inflation in an equation: 𝑖𝑡 = 𝑟𝑡 +

𝐸𝑡𝜋𝑡+1 ; the equation shows that the nominal interest rate equals the real rate plus expected

inflation. This means both variables contain a steady state in which higher interest rates

correspond to higher inflation (Fama, 1975). This depicts that interest rate is a good predictor

of inflation rate in a country. Therefore, a proxy for the nominal interest rate will likely

correlate with a proxy for the inflation rate. Kryzanowski et al. (1994) mentioned that financial

economists should be careful in variable selection so as to avoid the problem of

multicollinearity. To guard against this sort of problem, the researcher has, therefore, boycotted

money supply as consumer price index instead shows the growth or decline of the money in

circulation. Also, the demand for money in a nation is a function of interest rate, which is also

one of the variables selected. Since interest rate can be used to predict inflation, there is no

point having both variables in the same model hence the exclusion of the variable inflation.

Table 12 Research Variables

Countries Variables Description Source

Mexico Mexican stock exchange

Economic growth

Exchange rates

Interest rates

Mexican IPC index

Industrial production index

Mexican Peso/US Dollar

Discount rate/borrowing rate

Yahoo Finance

Federal Bank of St. Louis

Commodity prices Commodity price index Federal Bank of St. Louis

Federal funds rate Effective Federal funds rate Federal Bank of St. Louis

Indonesia Indonesian stock market

Economic growth

Exchange rates

Interest rates

Jakarta Composite Index

Indonesian Rupiah/US Dollar

Yahoo Finance

Commodity Prices Commodity price index Federal Bank of St. Louis

Nigeria Nigerian stock exchange

Economic growth

Exchange rates

Interest rates

Nigeria All-Share Index

Crude oil production index

Nigerian Naira/US Dollar

CBN official website

Turkey Turkish stock exchange

Economic growth

Exchange rates

Interest rates

Borsa Istanbul 100 Index

Turkish Lira/US Dollar

Yahoo Finance

Morgan Stanley Capital International

(MSCI)

MSCI world equity index MSCI Official website

4.4 Justification of Choice of Variables

4.4.1 Mexican IPC Index

This is the major stock market index in the Mexican stock exchange. It is the index that tracks

the performance of leading companies that are listed on the Mexican Stock Exchange. It

comprises the selection of shares that represent all the shares listed on the exchange from

various sectors of the economy. The Mexican IPC index comprises the 35 most liquid

companies in Mexico. The IPC index started growing in the 1990s and experienced a major

decline in 1994 during the Peso crisis, and it has since experienced a steady growth ranging

between 2,000 and 10,000 index points as of 2005. A major increase was experienced again

shortly after a sharp fall during the 2008/2009 global economic meltdown (20,000 to 10,000

index points). The index recovered swiftly from the crisis and had been performing above

25,000 index point and ranges from 30,000 to 40,000 at present.

4.4.2 Jakarta Composite Index (JCI)

This is the major stock market index in Indonesia and it accounts for the performance of all

companies listed on the Indonesian Stock Exchange. It is a modified capitalization-weighted

index with a base value of 100. JCI is a general indicator of all stocks listed on Jakarta’s stock

exchange market. It comprises more than 400 companies that are listed on the stock exchange.

JCI grew more than seven times of its original level in the early 2000s and the index growth

was influenced by the Asian financial crisis in 1997. JCI has experienced a stable growth just

after the global meltdown in the late 2000s. A significant growth of up to 19% was experienced

in 2011 with 4,978 index points and has, since 2012 to date, index points ranging from 4,000

to 6,000 index points.

4.4.3 Borsa Istanbul National 100 Index (XU100)

Index XU100 is a major stock market index at the Istanbul Stock Exchange (ISE). It tracks the

performance of 100 companies selected from the national market, real estate investment trusts

and venture capital investment trusts listed on the ISE. It is a capitalization-weighted index

with a base value of 1 as of January 1986. This index tracks the performance of 100 companies

that are selected for the national market. The XU100 index experienced a slow development in

the 1990s, especially during the political instability that affected the country in 1997. Although

a significant growth was experienced in the year 2000, the 2001 economic crisis in the country

caused a slowdown for a while. It has since 2002 onwards played above 20,000 index points.

The index was also affected by the economic crisis in 2008/2009 and recovered as the points

inched up between 60,000 and 85,000 from 2012 to 2014.

4.4.4 Nigeria All-share Index (NASI)

The Nigeria All-Share Index represents the movement of all shares listed on the stock exchange

market. It only includes shares in the computation of the index. It is with a base value of 100.

The All-share index comprises of all shares that are listed on the Nigerian stock exchange

market. NASI started in 1984 and grew in a slow but steady state from that time to the mid-

2000s with index points between 1,000 and 20,000. A major upward trend was experienced

between early 2007 and early 2008, which saw the index points climbing from 40,000 to

62,000. However, there was a sharp downturn in the market movement during the 2008/2009

global recession which influenced the index points as it reduced from 62,000 to 20,000. The

index has since then been in a recovery state. It has risen to 40,000 which was its former state

before the recession. The market is expected to keep going upward if there are no external

influences.

4.4.5 Interest Rate (Discount/Borrowing Rate) IR

The interest rate, according to Alam & Uddin (2009), is the cost of capital which has two

definitions from a lender’s and a borrower’s point of view. To a borrower, IR is the fee that is

paid for using money over a period (borrowing rate) and to a lender, IR is the amount that is

charged for using money over a period (lending rate). They also checked the relationship

between interest rate and stock prices and found a negative one. Interest rates increase the

interest firms pay on loans which eventually influences profits made by firms. This, in turn,

impacts shareholders’ wealth. Investors consider the growth and level of interest rates

obtainable in the various sectors of the economy and assess their possible impact on the

profitability and performance of companies (Osamwonyi & Osagie, 2012).The high capital

requirement that is needed in setting up businesses leads to companies taking the option of debt

financing which gives them the opportunity to purchase inventories and equipment. An

increase in the borrowing rate, therefore, results in a higher cost of borrowing which negatively

influences the future expected return of the firm. On the other hand, a reduction in the

borrowing rate decreases the costs of borrowing which serve as incentives for companies to

expand and also increase their profitability, thus a positive effect is thereby expected on the

firm’s output.

Interest rates in a financial sector that is regulated are benchmarked against the policy rates set

by the monetary policymakers; however, in an unregulated financial sector, interest rates are

purely determined by the supply and demand of funds in the market. This implies that when

the demand for funds increases, interest rates rise and vice versa.

Investors borrow money to purchase stocks in some cases and an increase in the borrowing rate

causes stock transactions to be more expensive; hence investors require a higher rate of return

on stocks before investing. This leads to a decrease in demand and the price of stocks.

Central banks use interest rates as a tool to steer the economy. This means that interest rates

determine the level of money in circulation. Monetary policy implementation can be achieved

through monetary expansion (by decreasing interest rates so that more firms would be able to

have access to cheaper loans) and monetary contraction (by increasing lending rates to make

loans costlier for firms). This study uses discount/ borrowing rates; therefore, interest rate is

expected to have a negative impact on the stock market.

4.4.6 Commodity Price Index (CPI)

Emerging markets face instability whenever there is a swing in global commodity prices. It is

usually expected that an increase in commodity price index could lead to an appreciation of the

local currency which reduces competitiveness; a decrease in commodity price index also causes

capital outflows and puts a downward pressure on the balance of payment of a nation (Hegerty,

2015). Commodity exports are known to exhibit strong linkages to foreign exchange; therefore,

there is the need to know the extent to which it could impact the entire nation through the stock

market. Since some emerging markets’ government expenditure (Nigeria being a prime

example) is funded by the proceeds from commodities especially oil, a reduction in commodity

price index could, therefore, reduce the gross domestic product. Findings show that both oil

and non-oil volatility reduces economic growth and strengthens what is known as ‘resource

curse’5 (Ploeg & Poelhekke, 2009).

Many studies have looked at the relationship between oil price changes and emerging stock

markets, as a result of oil being the lifeblood of modern economies. Countries that are

experiencing rapid growth are the ones that increase their demand for commodities such as oil

(Basher & Sadorsky, 2006). Increases in oil prices, according to the authors mentioned, impact

the discount rate that is used in the equity pricing formula. It also leads to inflation – which

central banks seek to control as part of their mandate by using interest rates. Higher interest

rates make bonds more attractive than stocks; hence share prices could fall in this regard. Since

the number of researchers interested in finding the impact of changes in commodity prices on

emerging stock market is few, it is of great importance to look beyond oil prices and include

non-oil prices as they are equally important.

4.4.7 Exchange Rate (ER)

The exchange rate is the rate at which one currency is exchanged for another. It is also the

value of a country’s currency in terms of another. The exchange rate is a product of a nation’s

external trade and it is also directly related to the balance of payments of the country. The

balance of payments and outer trade influence the exchange rate. Depreciation of a nation’s

currency leads to an increase in demand for the country’s export products which increases cash

5 Is a paradox for plenty, it occurs when a country focuses all its energy on a single industry.

inflow into the country. Firms of a particular nation pay attention to the variation in the

currency to be able to strategize on foreign transactions or international trade.

Yuko & Ito (2004) explained the relationship between exchange rate and stock prices by

suggesting that the relationship between the two variables contributed to the spread of the Asian

financial crisis in 1997. The depreciation of the Thai Baht had a great impact on the nation and

its environs which led to a stock market crash at the time (Leightner, 2007). Alternatively, if a

nation’s currency is expected to appreciate, investors will be willing to invest there, with the

pick-up in investment which leads to an increase in stock market returns. This is suggestive of

a positive impact of exchange rate on stock prices. Maku & Atanda (2009) show a positive

relationship between the depreciating Nigerian Naira and stock prices. Moreover, exchange

rate impact may equally depend largely on the level of trade balance and international trade;

therefore, the effect of the variable on the MINT country stock prices is determined by the

dominance of import and export of the economy.

4.4.8 Economic Growth (Industrial Production (IP)

Industrial production is used as a proxy for economic growth. It is used to measure changes in

the price-adjusted output of industry. It also measures the real production output of industries

such as mining, manufacturing and utilities. It measures production output and highlights

structural developments in the economy. It is used to explore production variation in the short-

term period, and it is used to calculate macroeconomic indicators like gross domestic product.

Industrial production (IP) is a measure of the real economic activity of countries studied in this

research. It measures the output of industries such as manufacturing and mining. IP is well

known for its response to the state of the economy. It rises during an economic boom and

declines during a recession which shows its pro-cyclical nature. IP is a reflection of growth in

relevant industries of the economy. Firm growth is usually influenced by environmental

factors, especially growth in the economy. When industries in a nation are doing well, firms

generate more cash flow as a result, and there will be an increase in productivity and

profitability which would trigger share prices to go up. IP plays a pivotal role in the overall

economy. Industrial production index data for countries such as Mexico, Indonesia and Turkey

are available; however, that of Nigeria is not.

The industrial sector in Nigeria includes manufacturing, mining and utilities, which contribute

only a small portion of the gross domestic product (Ihejirika, 2012). The industrial sector

accounts for about 6% of the economic activities. The oil and gas industry, however, is the

primary driver of the economy and accounts for over 95% of export earnings and 85% of

government revenue between 2011 and 2012 (Chete et al., 2013). This research, therefore,

includes oil production index as a proxy for economic growth in Nigeria and industrial

production index as a proxy for economic growth in Mexico, Indonesia and Turkey. The

researcher expects a positive impact of the variables on the MINT countries’ stock markets.

4.4.9 Federal Funds Rate (FFR)

A hike in short-term US interest rates would bring to an end the near-zero borrowing costs in

the developed nations. There has been an enormous borrowing in the developed market that is

fuelled by the US-impaired easy money; up to $9 trillion flowed to emerging markets since

early 2005 which gave rise to inflating domestic debt levels to an unprecedented 160% of GDP

(Marwah et al., 2015). Tightening of the US policy will foist a credit crunch onto emerging

markets. Emerging markets will have to pay more to service debt e.g. the impact will be felt

more in countries that have a huge current account deficit, while other sources of finance are

dwindling (Trivedi, 2015). This instance will make the surge of capital that flowed into

emerging markets over the past decade to start reversing. This means that emerging markets

will suffer a net capital outflow. The chart below shows the process of the impact of the hike

in the US interest rate in emerging markets.

Figure 15 Federal Funds Rate Impact Process

Interest rate hike in the US will most likely be bad news for emerging economies as investors

are most likely to take up dollar-dominated investments in the US. As the US tighten their

screws, Central Banks in emerging markets could be counted on to do the same. However,

these are two equally unattractive extremes, which is either they allow low interest rates to

continue and risk capital outflows as investors move to dollar-dominated assets or raise

high debt slows the economy

exchange rate falls

bond yields rises

stock prices drops

domestic interest rates to keep pace with the Fed and possibly sending their economies into

recession (World Economic and Financial Surveys, 2015).

4.5 Model Specification

Following the research question that is coined out of the objectives that this research is aimed

to meet, it can be observed that the research seeks to investigate how the MINT countries’ stock

markets react to changes in the country and global macroeconomic variables in the short and

long runs. A number of statistical methods are mentioned in Chapter 2; some of these methods

are referred to in this chapter and explained. Regression analysis is an important tool used by

economists to understand the relationship that exists among two or more variables. It is very

useful when there are several variables and the interactions between these variables are

complex. There is a simple regression analysis that is used for examining the interaction

between two variables which are the dependent variable and the explanatory variable. There is

also multiple regression which was developed to analyse economic research involving many

variables. The main difference between the two is that multiple regression is a case of having

more than one explanatory variable in a model while simple regression is a regression with a

single dependent and independent variable (Koop, 2005).

Ordinary least square (OLS) method is the most common estimator for the regression model.

After examining stock market predictability literature, the use of OLS to forecast simple and

multiple regression models has been criticized by Kuo (2016); the researcher’s argument

coming from the work of Phan et al. (2015), is that OLS estimation ignores three major

statistical problems, which are predictor endogeneity, persistency and heteroskedasticity. His

findings show that different results are produced when OLS, VECM and VAR are used to

examine the same model. GARCH has been used to study the volatility of stock indexes which

is a deviation from the objective of this study. It is, therefore, advantageous to explain the

limitations of some of the methods used by researchers to investigate the relationship between

economic and financial variables.

If the development of regression analysis has suited the purpose for which it was created, i.e.

if it had addressed the problems in economics and finance, there wouldn’t be a need to develop

new methods that can serve the same purpose by statisticians and econometricians. According

to Koop (2005), there are a few cases where OLS could be the second best option to estimate

a model, and there are cases where OLS would be the wrong method to use. When the

dependent variable in a model is censored or when the dependent variable measures a duration,

the OLS is not the best estimator in these instances. The VAR model is a multiple equation

model which assumes the dependent variable to be dependent on its lag as well as lags of other

chosen variables. It does not stop there but also has a second equation that takes the explanatory

variables as dependent variables, and, therefore, examines it by considering its lags and the lag

of the previous dependent variable. It is a method that can show a lead lag when examining

interactions among variables (Keshin, 2013). VAR is useful when there is no cointegrating

equation among variables in a model; this draws the attention of researchers to the use of

VECM. VECM, according to Kuo (2016), performs better than VAR because it produces

smaller errors. The VECM is known to be more efficient by improving VAR forecast accuracy,

especially the ones with longer time horizons. He concluded that VECM is superior in

forecasting performance when compared to OLS and VAR. VECM produces an accurate

forecasts of variables but fails to account for the downturn which occurs as a result of

unexpected shocks in variables (Anderson et al., 2002).

Methods used in this research begin with a process of a multiple regression analysis and the

following equations show a representation of the dependent and intervening/explanatory

variables.

𝑆𝑅𝑡 = 𝛽0 + 𝛽1𝐿𝐶𝑃𝑡 + 𝛽2𝐿𝐼𝑃𝑡 + 𝛽3𝐿𝐸𝑅𝑡 + 𝛽4𝐼𝑅𝑡 + 𝛽5𝐹𝑅𝑅𝑡 + 𝛽6𝑀𝑆𝐶𝐼𝑡 + 휀𝑡 ............. (4.1)

In the equations above, SR represents the stock returns of the MINT stock exchange market,

𝛽0 represents the intercept, which is usually a constant and is the expected mean value of the

dependent variable when all intervening variables are equal to zero. 𝛽1 − 𝛽6 represents the

sensitivity of each of the chosen macroeconomic variables in relation to stock returns in

Mexico, Indonesia, Nigeria and Turkey respectively. 휀 is the error term which stands for all

other yet-to-be-captured factors that influence stock prices, while L indicates that variables are

in their natural logarithm form. CP represents commodity price index, IP is the industrial

production index, ER stands for the exchange rate, IR represents the interest rate, in the case of

Nigeria, OP stands for the oil production index, and FRR connotes the Federal funds rate. The

base t shows that variables are studied over time. Overnight borrowing rates are in percentages;

therefore, there is no need to take them in natural logarithm form. To further study the data, it

is important to begin with descriptive statistics.

Equations 4.1 is strictly time series – it is used to develop simple models that can be used for

forecasting, predicting and testing hypotheses. Methodology helps to decompose a series into

a trend, and trend components represent the long-term behaviour of a series (Enders, 2010).

Datasets come in various forms; it is, therefore, expedient to say a bit about some important

data structures that exist in applied econometrics. Each will be described in the next paragraph.

The first is cross-sectional data, which is a study of a sample of individuals, cities, regions or

any unit at a particular point in time. Cross-sectional data allows different time periods; this

means it is possible to ignore time differences in the collection of data. It is usually associated

with applied microeconomics. The second is panel data which consists of the time series of

each cross-sectional member of a data set. For example, it is possible to consider the sales and

number of employees for 40 banks over a 10-year period. It is also possible to collect panel

data on a geographical basis. For example, one could have industrial production index of 10

countries and for a 30-year period. Both cross-sectional and panel data are useful and can be

used to suit the purposes set by researchers. However, in this case, the time series employed

consists of observations of more than one variable over time and are usually arranged in

chronological order which have different frequencies such as hourly, daily, weekly, monthly,

quarterly or annually. Time series is relevant in econometrics as past events are said to

influence those in the future and lags in behaviour are prevalent in social sciences; this makes

time a very crucial dimension in time series data sets (Asteriou & Hall, 2011).

4.5.1 Descriptive Statistics

Descriptive statistics give an idea of the fundamental element of the data being studied. To

understand the data movement, variables are plotted in a graph using their level form and a

table is drawn in the next chapter to show the results of the sample mean, skewness, kurtosis,

standard deviation, p-value and Jarque–Bera (JB) statistics. The mean provides an estimate of

the population from which the sample data is selected, and the result indicates the centre point

of the distribution. Standard deviation gives an estimation of the variability and the dispersion

in the distribution of the population data. It shows the variability of data around their respective

mean, and zero indicates no variability. The JB test is used to determine normal probability

distribution in variables; it is a large sample test that computes the kurtosis as well as the

skewness measure. The formula for JB is 𝐽𝐵 =𝑛

6[𝑆2 +

(𝑘−3)2

4], where n is the sample size, S

is the sample skewness coefficient and k is the sample kurtosis coefficient. For the normal

distribution of a variable to occur, S = 0 and k = 3. This explains why JB is a joint hypothesis

test of S and k which are 0 and 3 respectively. In the case of k = 3, the distribution is said to be

normal, where k is less than 3. It is known as platykurtic distribution, which means that a

variable distribution on a bell-shaped curve is flat. When k is greater than 3, it is called

leptokurtic variable distribution on a bell-shaped curve which is thin.

The result derived from these will be explained in the analysis chapter. The standard deviation

result would indicate the level of variability of each of the MINT countries’ stock markets and

the normal distribution of the intervening variables would be checked using the p-value at 1%,

5% and 10% levels of significance.

Most macroeconomic variables are close substitutes; therefore, there is a tendency that

correlations of high magnitude can exist among the intervening variables selected in this work.

This has prompted the need to do a multicollinearity test to detect the correlation, as results

derived from a model with multicollinearity problems are said to be biased.

4.5.2 Diagnostic Tests

Diagnostic tests are performed to determine the presence of unusual patterns or trends in data

or the estimated model. It is important to conduct the test that would be mentioned in this part

of this research to avoid spurious or biased outcome in our analysis. There are a number of

tests that can be classified as tests to clean data used for analysis or to detect abnormal

movements in data set, and some of these tests are considered in this study to ascertain the

reliability of the findings that would be presented at the end of the thesis. The first type of test

is the outlier detection, the second is the test for structural break points and, lastly,

multicollinearity tests.

4.5.2.1 Outlier Detection, Structural breaks and the inclusion of dummy variables

Outliers are defined as patterns in data set that deviate from or do not conform to normal

behaviour; outliers can be detected when the residual compared to fitted values (Singh &

Upadhyaya, 2012). The existence of outliers is not a new issue in statistics (Cliff, 1993).

Hawkins (1980) was one of the notable scholars who suggested the identification of

observation that appeared not to be consistent with the remainder of data set; the observation

is said to raise suspicion whether data is generated by another mechanism. Outliers can be

detected in either univariate or multivariate methods (Ben-Gal, 2005). Outliers can lead to

biased parameter estimation, incorrect results and model misspecification; it is, therefore, of

great importance for outliers to be identified prior to analysis and modelling (Liu et al., 2004).

The graphical representation obtained from the statistical software (EViews 9) helps to identify

observations with high leverage; this means observations that deviate from other observations

in the dataset are identified. Although the graphical illustration helps to identify outliers, there

is another test known as the Chow test that can be used to check if the period identified is a

breakpoint or not. Peradventure, the test shows that the outlier identified is a breakpoint, and

there will be a need to conduct structural break test.

When one assumes that real-world events could be the reason for an outlier, there is a

possibility of avoiding a structural break by splitting the dataset into two segments. The Chow

breakpoint test was developed by Chow (1960), who tested for a change of regime at known

dates using the F-test. The Chow test is a test that allows the researcher to identify breakpoints

one after the other and also help specify the dates identified in the graphical illustration derived

in the outlier detection and input them to check if they are actually breakpoints or not

(Petterson, 2000). In the case where the structural break points exceed one, there will be an

evidence of multiple breakpoints in the data. The multiple structural break test proposed by Bai

and Perron (2003) will be used in this instance.

Whenever data plotted in a graph exhibits structural breaks especially in periods where there

are specific significant deviations from a trend, a dummy variable is constructed to account

for the breaks, it takes a value of one for the observations and zero elsewhere. When the

break is not just at a single point in time, which means it has changed the trend and level

which evolves several periods, the type of break is known as innovational outlier. The Eviews

9 software helps take this into account. The intercept, linear trend and the break dummy

variable are therefore stated as fixed regressors to avoid their lags being included in the

model. When lags of the dummy variable are included in the model, there is a tendency of a

problem of multicollinearity.

4.5.2.3 Multicollinearity Test

As mentioned earlier, this test is necessary to be sure that multiple correlations of sufficient

magnitude do not exist among intervening variables as this would have an effect on regression

estimates. Multicollinearity problems lead to the following:

Non-significant results

Predictor beta moving in a non-sensible direction

Large standard errors

To identify this issue, the variance inflation factor (VIF) is employed using the intervening

variables. VIF values of 9 or less are often the criteria to validate that intervening variables do

not have multiple correlations, while values of 10 or more show correlations among the

intervening variables. This test is of great importance due to the suggestion made by

Kryzanowski (1994), who emphasizes the multicollinearity problem in the issue of factor

selection. There are ways to correct the problem of multicollinearity; firstly, by excluding the

redundant variable in the model; secondly by increasing the sample size so as to add more

stability to the data; thirdly, the first differenced transformation can be used to alleviate

multicollinearity problem and lastly, by checking if two variables are duplicates to replace one

with another variable.

4.5.3 Unit Root Test

After the multicollinearity problem is verified, the study proceeds to check for stationarity of

the variables employed. There is ‘a consensus that economic and financial data are not

stationary’ (Rothman, 1999, p. 49) and non-stationary data cannot be used to estimate

parameters or run cointegration tests as it could lead to arriving at misleading results which are

also known as spurious regression. Granger & Newbold (1974) warn of spurious regression in

the level form of economic time series data; they recommend that researchers should either

take variables in their first differences or include a lagged dependent variable in their equations

so as to avoid misspecification. A spurious result is a form of type II error which could be in

the form of high R-squared, inflated t-ratios and small standard errors of the models estimated.

Stationarity in time series occurs when the mean and variance of the data are constant over

time. In an equation like this 𝑌𝑡 = 𝛼 + 𝛽𝑋𝑡 + 휀𝑡 applying differencing operation gives results

of observations such as:

X level 𝑋𝑡

X (1st differenced state) 𝑋𝑡 − 𝑋𝑡−1

X (2nd differenced state) 𝑋𝑡 − 𝑋𝑡−2

There are two basic types of unit root tests that are common to researchers, which are:

Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP).

4.5.4 Augmented Dickey–Fuller (ADF) Test

This test was developed by Dickey and Fuller (1979), where they examined data using trend

and intercepts. ADF regression decides whether or not to include constant, trend, drift or lag

lengths for the differences that augment the regular Dickey–Fuller regression. If time series is

to be differenced for it to become stationary, it is said to be integrated of order d, I (d), where

d denotes the number of lags the data is differenced. According to Gujarati & Porter (2009),

they modelled unit root and expressed time series variables as follows;

∆𝑌𝑡 = 𝛼0 + 𝛼𝑖𝑌𝑡−1 + ∑ 𝛿𝑖𝑝𝑖=1 ∆𝑌𝑡−1 + 휀𝑖 ......................................................... (4.2)

𝑌 𝑖𝑠 𝑡ℎ𝑒 𝑐ℎ𝑜𝑖𝑐𝑒 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒

𝛼0 𝑖𝑠 𝑡ℎ𝑒 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡

∆ 𝑖𝑠 𝑡ℎ𝑒 𝑓𝑖𝑟𝑠𝑡 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑜𝑝𝑒𝑟𝑎𝑡𝑜𝑟

𝛼𝑖𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡𝑠 𝑖 = 1 𝑎𝑛𝑑 2

휀𝑖𝑖𝑠 𝑡ℎ𝑒 𝑠𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑟𝑦 𝑠𝑡𝑜𝑐ℎ𝑎𝑠𝑡𝑖𝑐 𝑝𝑟𝑜𝑐𝑒𝑠𝑠

𝑃 𝑖𝑠 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑎𝑔𝑔𝑒𝑑 𝑡𝑒𝑟𝑚𝑠

The decision rule for the ADF test statistics is based on a null and alternative hypothesis.

𝐻𝑜: 𝛼𝑖 = 0 𝑤ℎ𝑖𝑐ℎ 𝑑𝑒𝑛𝑜𝑡𝑒𝑠 𝑝𝑟𝑒𝑠𝑒𝑛𝑐𝑒 𝑜𝑓 𝑢𝑛𝑖𝑡 𝑟𝑜𝑜𝑡 𝑜𝑟 𝑛𝑜𝑛 − 𝑠𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑟𝑖𝑡𝑦 𝑜𝑓 𝑑𝑎𝑡𝑎

𝐻𝐴: 𝛼𝑖 ≠ 0 𝑤ℎ𝑖𝑐ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑒𝑠 𝑎𝑏𝑠𝑒𝑛𝑐𝑒 𝑜𝑓 𝑢𝑛𝑖𝑡 𝑟𝑜𝑜𝑡 𝑜𝑟 𝑠𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑟𝑖𝑡𝑦 𝑜𝑓 𝑑𝑎𝑡𝑎

The output of the test gives McKinnon’s critical values and ADF value. The null hypothesis of

the presence of unit root is rejected when the ADF value is greater than McKinnon’s critical

value, and when McKinnon’s critical value is greater than ADF value, one should fail to reject

the null of hypothesis of the presence of a unit root. Stationarity of time series data confirms

the suitability of the data for model estimation. ADF is a test that helps to determine whether

economic time series is trend stationary or difference stationary.

4.5.5 Phillips–Perron (PP) Test

This test was developed by Phillips and Perron (1988), and it differs from the ADF test. Unlike

the ADF test that includes serial correlation of error term with lagged difference, the PP ignores

serial correlation and replaces it with a non-parametric statistical method with an exclusion of

lagged difference terms. PP takes variables in their first differenced log form to eliminate the

possibility of non-stationarity of data as financial and economic variables are likely to be

stationary in their first differenced form.

The issue of structural break is identified as a weakness for both ADF and PP unit root tests as

the tests structural breaks as evidence of non-stationarity (see Perron & Vogelsang, 1992). This

means that series that are found to be stationary after first differencing may be stationary I (0)

around structural break(s) but are classified as I (1). Perron (1989) shows that when models fail

to allow for the existing break, it leads to a bias that increases the ability to reject the null

hypothesis of the presence of a unit root. Since the Chow and multiple structural breakpoint

tests identify the number of structural breakpoints, the author proposes allowing a known break

in the unit root test to overcome the issue identified by Perron. The author therefore will

consider the use of unit root with breakpoints for models that have structural break(s).

4.5.6 Lag Selection Criteria

Appropriate lag length is very important as it helps to avoid inconsistency in the vector

autoregressive (VAR) model. The two ways to approach this are through information criteria

restriction or cross-equation restrictions. This study uses the information criteria restriction;

the method focuses on the residual sum of squares. The aim of this is to choose an appropriate

number of lags that can reduce information criterion values. Lag lengths would be justified

using three tests, which are Akaike’s information criteria (AIC), Schwarz’s information criteria

and the Hannan–Quinn information criteria. Although there have been instances where

information criteria underestimate or overestimate lag lengths, Khim-Sen (2004), however,

suggests that AIC information criteria are to be upheld in the case of a different result.

4.5.7 Cointegration Test

Cointegration is the concept of Granger (1981) and was fully developed by Johansen & Juselius

(1990). It is useful in determining the long-run relationship among variables. As an example,

let’s assume 𝑌𝑡 𝑎𝑛𝑑 𝑋𝑡 are non-stationary after first differencing I (1), then the linear

combination 휀𝑡 = 𝑌𝑡 − 𝛼 − 𝐵𝑋𝑡 will also be non-stationary of the same order. Cointegration

means that variables move in the same direction, which indicates that they share common

stochastic trend. To test for cointegration in a model, the stationarity test is first performed on

the error term 휀𝑡, and this is observed using the least square residuals of error term (∆휀�̂�). If the

residuals are stationary based on the test conducted, then Y and X are cointegrated. Existing

literature has employed the cointegration test developed by Johansen (1988) to investigate

cointegration among variables.

There are two different forms of Johansen cointegration tests, which are bivariate and

multivariate cointegration tests. The bivariate analysis is useful when the relationship between

stock prices and a particular macroeconomic variable is examined, while the multivariate

cointegration test considers the relationship between all macroeconomic variables selected as

one entity on the dependent variable which is stock prices. The Johansen test, therefore,

assumes a null hypothesis of no cointegration among variables at 5% level of significance. In

examining this, trace and maximum Eigen statistics methods are applied. The equations for

these tests are as follows:

𝜆𝑡𝑟𝑎𝑐𝑒(𝑟) = −𝑇 ∑ ln (1 − 𝜆𝑖𝑛𝑖=𝑟+𝑡 ) ........................................................................ (4.3)

𝜆𝑚𝑎𝑥(𝑟, 𝑟 + 1) = −𝑇 ln (1 − 𝜆𝑟+1) ...................................................................... (4.4)

𝜆 in equation (4.3) is the ordered Eigenvalues; r is the cointegrating vectors and n is the number

of variables. The output of the results derived from this test is reported in the next chapter using

a table which specifies the values for (r =0, 1, 2, 3...n-1) and the decision rule is that for a null

hypothesis of no cointegration to be rejected, critical values must be compared to trace

statistics. The null and alternative hypothesis are as follows:

𝐻0: 𝑟 = 0 𝑤ℎ𝑖𝑐ℎ 𝑑𝑒𝑛𝑜𝑡𝑒𝑠 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 𝑎𝑟𝑒 𝑛𝑜𝑡 𝑐𝑜𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑

𝐻𝐴: 𝑟 ≠ 0 𝑤ℎ𝑖𝑐ℎ 𝑑𝑒𝑛𝑜𝑡𝑒𝑠 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 𝑎𝑟𝑒 𝑐𝑜𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑

When trace value is greater than the critical value in absolute terms, then one should reject the

null hypothesis of no cointegration, and when trace value is less than the critical value, then

one should fail to reject the null hypothesis of no cointegration and assume no long-run

relationships among the variables. Equation (4.4) is the formula representing the maximum

Eigen method of cointegration. It assumes a given r under the null hypothesis against the

alternative of r+1 cointegrating equations. The hypothesis for the maximum Eigen test is:

𝐻0 = 𝑟 𝑐𝑜𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑛𝑔 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠

𝐻𝐴 = 𝑟 + 1 𝑐𝑜𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 𝑓𝑜𝑟 𝑟 = 1,2,3 … 𝑛

This test considers critical values at 5% level of significance when making decisions. When

maximum statistics are greater than critical value, then null hypothesis is rejected and when

maximum statistics are less than critical value in absolute terms, then one should fail to reject

null hypothesis. Alexander (2001) suggests that the trace statistics should be upheld in case of

conflicting outcome. Some advantages of the cointegration tests are that it transforms the linear

combination of two non-stationary time series into a stationary one, and also improves

forecasting performance in the long horizon.

The Johansen cointegration test explained above accommodates variables that are of the same

order 1(1). In a case where a model has both variables that are order 1(0) and I (1), the

autoregressive distributed lag (ARDL) approach to cointegration introduced by Pesaran et al.

(2001) is the appropriate technique for model estimation. The ARDL result gives F-statistic

value and upper and lower bound value. If the F-statistic value is higher than both the upper

and lower bound value, there is cointegration among the set of variables which indicates a

long-run relationship. On the other hand, if the F-statistic is less than any of the bound critical

values, there is no cointegration among the set of variables which indicates no long-run

relationship among variables. The ARDL cointegration technique is preferred over the

conventional cointegration method for the following reasons;

The traditional cointegration method is sensitive to sample sizes while ARDL technique

provides consistent and robust results for sample sizes. Secondly, the ARDL makes it

possible to determine the lags for each variable in the model which makes it more flexible,

whereas the conventional cointegration does not. Third, The ARDL approach integrates the

short – run impact with a long – run equilibrium using an error correction term without the

elimination of the long – run information. Lastly, ARDL does not need adjustment to the data

to calculate the long – run relationships.

Figure 16 Cointegration Decision

The illustration above shows that the result derived confirms whether there is a cointegrating

relationship among the variables or not. The VECM is applied to confirm the short-run dynamic

relationship in the estimated model; however, in a case where no cointegrating equation is

reported, the VAR model helps by showing the interaction between each of the variables in the

model and the variable of interest.

4.5.8 Vector Autoregressive (VAR) Model

The VAR model as mentioned earlier is a model used for estimation when there is no

cointegrating relationship among the variables. This model was developed by Sims in 1980.

He proposed that if there is actual concurrence among sets of variables, they are to be treated

equally in a model; this means there shouldn’t be a distinction between exogenous and

endogenous variables. The term ‘vector’ is the fact that the model deals with two or more

variables and the term autoregressive is due to the appearance of the dependent variable on the

right-hand side (Sims, 1980).

In VAR, there is no need to worry about which variable should be exogenous or endogenous.

The estimation in VAR is simple, and the interpretation of OLS can be applied to each equation

separately. The simulation result presented by Chan & Chung (1995) show that VAR model

can reveal an underlying process better than a simultaneous-equation model. Although VAR

model has got various advantages; however, there are various problems attributed to the use of

Employ VECM

Co-integrated?

Employ unrestricted

VAR NO

the model. Some of these are: the problem of the choice of lag length; the emphasis on

forecasting makes it less suitable for policy analysis; and the use of first differenced data may

be unsatisfactory especially when data contains the mix of some that are stationary in level

form I(0) and the other that are stationary after first differencing I(1).This makes transforming

data a little bit complicated. Finally, the individual coefficients in the estimated VAR model

are often difficult to interpret; hence practitioners of this technique use the impulse response

function to understand the VAR model result (Runkle, 1987).

Having pointed out the various problems one faces when using the VAR model, the first issue

has been dealt with in econometrics since there is an availability of information criteria that is

used to determine the lag length that is suitable for model estimation. Secondly, the principal

use of VAR model in this research is not for forecasting but to investigate the relationship that

exists among variables. The author is confident that the second problem mentioned is avoided,

and thirdly, there is an aim which is to make sure that all variables are of the same stationarity

level, i.e. they are either I(0) or I(1). A mixture of these would suggest a different model

estimation technique other than the VAR. In all, this study intends to use impulse response

function to interpret the VAR model result derived.

4.5.9 Vector Error Correction Model (VECM)

After the cointegration test is performed and there is confirmation that the model has

cointegrating equations, variables can use the VECM model. The VECM is a restricted vector

autoregressive (VAR) where the dependent variables are not covariance stationary in its level

form but first differenced form. According to Granger’s representation theorem, VECM is just

a representation of cointegrated VAR. The VECM helps to investigate the short-run

relationship among the variables. Short-run dynamics are measured by the speed it takes the

dependent variable to deviate and go back to its point of equilibrium. The VECM explains the

time it takes the dependent variable to change as a result of influences from intervening

variables. The VECM structure is specified in the equation below:

∆𝑌𝑡 = 𝛼 + 𝛽∆𝑋𝑡−1 − 𝛽𝐸𝐶𝑡−1 + 휀𝑡 ................................................................................. (4.5)

EC in the equation above is the value attached to the speed at which deviations from

equilibrium is corrected. The error correction in the equation for all X variables (intervening

variables) would help determine the short-run effect of intervening variables on the dependent

variable. The VECM is a VAR model transformed into a VEC model because it is not

vulnerable to simultaneous bias.

Some advantages of the VECM are:

It offers an easy means of explaining, forecasting and predicting the values of

economic variables at any time.

It can test for weak exogeneity and parameter restrictions.

It assumes no prior direction of causality among variables.

The resulting VAR from VECM representation has a more efficient coefficient

estimate.

It restricts the long-run behaviour of the endogenous variables to converge to their

cointegrating relationship while allowing a broad range of short-run dynamics, and

it makes the concept of cointegration useful for modelling and inference for

macroeconomic time series.

4.5.10 Impulse Response Function (IRF)

IRF is a tool used for interpreting VAR. The IRF identifies the reaction of the dependent

variable in the VAR model to shocks in the error term. An example of the application of IRF

in an equation is expressed below:

𝑌1𝑡 = 𝛼 + ∑ 𝛽𝑗𝑌𝑡−𝑗𝑘𝑗=1 + ∑ 𝛾𝑗

𝑘𝑗=1 𝑋𝑡−𝑗 + 휀1𝑡 …………………………………………… (4.6)

𝑋𝑡 = 𝛼′ + ∑ 𝜃𝑗𝑌𝑡−𝑗𝑘𝑗=1 + ∑ 𝛾𝑗

𝑘𝑗=1 𝑋𝑡−𝑗 + 휀2𝑡 ……………………………………….…. (4.7)

The error terms in the equation above are 휀1 and휀2. Suppose 휀1 in the equation increases by a

value of one standard deviation, such shock or change will impact 𝑌1 in the current as well as

in the future. The same applies to equation 4.7. The IRF traces out the impact of such shocks

for several periods in the future. Although the utility of IRF has been questioned, it is still the

centre of attention of VAR analysis (McNees, 1986). IRF is also used to measure the reaction

of variables to shocks emanating from another variable. It shows the vanishing rate, size and

magnitude of the effect of shocks. IRF from the stationary VAR model is said to die out over

time, while IRF from VECM does not usually die out over time. This is because the stationary

VAR model is time-variant variance which makes it possible for the effect of shocks to die out

to enable variables to go back to its mean. Moreover, variables that are stationary in their

differenced form in VECM are not mean-reverting which supports the assumption of the effect

of shocks not being able to die out over time. The terminology given to shocks that die over

time is transitory shocks, while a shock that does not die out over time is referred to as a

permanent shock. The result of the test is presented as a graph, and the interpretation of the

graphs would be given based on the guidelines stipulated.

4.5.11 Variance Decomposition (VDC)

VDC makes the inferences on causal relationship beyond the sample period. The test analyses

the error in the evaluation process that comes from other variables in the VECM (Guneratne,

2006). The test indicates the amount of interaction each variable contributes to another variable.

It also determines how much of the error variance of each variable is explained by exogenous

shock to another variable. VDC makes one of the variables a dependent variable and gives the

proportion of the response of each intervening variable to variation in the selected dependent

variable. The result of the test is presented in a tabular form and helps identify which of the

selected macroeconomic variables have the most proportion in explaining the variation in the

stock market.

4.5.12 Granger Causality

Granger causality is used to make inferences about the causal relationship between variables;

causality is predictability in simple terms. Granger (1969) developed this technique and started

by pointing out how difficult it is to determine the direction of causality in time series because

of the integrated correlation of the variables. This test examines whether changes in a variable

cause another variable to change and vice versa. This method is used to determine whether

variables are temporarily related and whether an inclusion of a variable in a particular model

reduces the forecasting variance in the information set. Granger causality is as close to causality

as is possible with statistical information. The method relies on time concept and, therefore,

believes that the future cannot cause the past and test whether lagged values of a variable (X)

play a significant role in explaining another variable (Y) in a model with several lagged values

of the latter (Y) variable on the right side. If so, then variable X is said to Granger cause Y.

Granger causality offers various directions of causality. For example, say x and y are two

variables that we seek to investigate the direction of causality; one is expected to determine the

possibility of causality using these scenarios; causality of x to y (xy); y to x (xy), both of

these scenarios are known as unilateral causality as it indicates that one of the variables cause

the other to change and not vice versa. However, when x causes y and y causes x (xy), it is

called bilateral causality as, in this case, variable x causes y to change and vice versa.

There are three cases of causality. Firstly, when a variable x causes variable y to deviate and

there is no reverse causation, we assume uni-directional causality. Secondly, when variable x

causes y and there is reverse causation, we assume a bi-directional causality. Lastly, when

variable x does not Granger cause variable y and vice versa, we assume independence between

variables x and y. Although the concept of Granger causality has been widely accepted, it is

not free from problems. Some of these problems include the issue of lag length specification;

this is said to be critical to the outcome of the test. Also with small samples, the choice of

bivariate or multivariate will affect the result. The first problem has been mentioned earlier as

part of the shortcomings of VAR and has been resolved with the development of information

criteria as a good way to select appropriate lag length. The other problem, however, is only

applicable when we are working with a small sample size; this research is looking at up to 264

observations per variable which is not small according to the stipulated rule (Green, 1991).

4.6 Summary

This chapter has described the research design that is adopted in this research. It has also shown

how this research fits perfectly to the assumption of the positivist paradigm. The first part of

this chapter has therefore covered the methodological background, framework as well as the

limitations in the structure chosen. The second part explains the source, collection method and

description of the data that is used in the research work. Various countries and global

macroeconomic factors that are chosen are justified, and possible linkages between the

variables and stock prices are suggested based on past literature. The chapter goes further to

specify the model that is estimated for each of the MINT countries in equation 4.1.

A brief description of the data collected is necessary so as to identify omitted variables and to

understand significant variability in the data. The data validity test that is conducted is stated

and explained accordingly. After data must have gone through validity tests and certified for

estimation, the main tests – which are cointegration, vector autoregressive, vector error

correction and impulse response function tests – would help determine the results of our

research questions. These tests were carried out using EViews 9, which is a type of statistical

software used for econometric analysis. The next chapter presents the analysis according to

how the tests have been outlined in this chapter.

CHAPTER FIVE

5.0 Analysis

5.1 Introduction

The fundamental objective of this chapter is to use data that is collected via secondary sources,

as explained in the previous chapter, to perform the step-by-step statistical test that is outlined

in the same chapter. Historical data of the major stock market indices of the MINT countries

as well as country and global macroeconomic variables such as exchange rate, interest rate,

commodity price index, Federal funds rate and economic growth (which are measured using

industrial production and oil production index) are examined. Due to the lack of availability of

the range of industrial production index data needed for Nigeria, there is a need to search for a

variable that can be used in place of industrial production index in the country. Although gross

domestic product (GDP) could have been an ideal proxy, GDP data in Nigeria is recorded

quarterly in the Central Bank of Nigeria archive; hence the need to consider another alternative

measure. Nigeria is a country that depends on primary product export (especially oil) and the

sector accounts for about 35% of gross domestic product; petroleum exports revenue represents

over 90% of total exports revenue (OPEC, 2015). The oil production index gives the account

of how much oil is produced; the trend of this helps to understand how it contributes to the

advancement of the economy as a whole since it is the major revenue source for the Federal

government.

This chapter, is therefore, set to give a detailed description of data used in the model estimation.

This is achievable by observing the data, both visually and through the running of statistical

tests, to give empirical content to the main aim of the research, which is to investigate how

chosen country and global macroeconomic variables affect the stock market in each of the

MINT countries.

5.2 Presentation of Results

The output of the statistical tests performed are mostly tabulated which are copied directly from

the software (EViews 9), and every one of the table has its interpretation. The researcher has

also created some equations using the variables as most of the variables are now converted to

numerical forms and can be analysed in a more scientific manner. There is a need to understand

how variables are coded for easier interpretation. The exchange rate is coded as ER, interest

rate as IR, industrial production as IP, MSCI as Morgan Stanley Capital International World

Equity Index, Federal funds rate as FFR and commodity prices as CP. All of these variables

except for the one in percentages are taken in their natural logarithm form to be able to give a

proportional (%) interpretation to our result.

5.2.1 Descriptive Statistics

Descriptive statistics are a way of quantitatively explaining the patterns and trends of the

dataset and giving a summary of the data in numerical value. Tables 13 and 14 present the

overview of all the variables (Mexico, Indonesia, Nigeria and Turkey). Using 264 observations,

first we examine the measure of variability exhibited by each of the variables, variability

indicates how spread out the data is, and the standard deviation provides an index of variability

in the distribution.

Table 13 Descriptive Statistics Result for Mexico and Indonesia

Mexico RIPC MSCI LIP LER LCP IR FFR

Indonesia RJCI LIP LER IR

Mean 0.0149 6.9672 4.5167 2.2393 4.4969 11.3644 2.9230 0.0144 4.4665 8.8310 12.5249

Median 0.0160 7.0248 4.5332 2.3630 4.2576 5.7700 3.0200 0.0183 4.4529 9.1103 9.5950

Maximum 0.1931 7.4666 4.7620 2.6838 5.3931 70.2600 6.5400 0.2842 4.8483 9.5441 70.8100

Minimum -0.2951 6.2102 4.1863 1.1305 3.7369 2.1500 0.0700 -0.3151 4.0452 7.6327 5.7500

Std. Dev. 0.0701 0.2995 0.1455 0.3880 0.5045 11.0962 2.2640 0.0797 0.1625 0.6009 10.1429

Skewness -0.4628 -0.4931 -0.7040 -1.7661 0.3241 2.0175 -0.0074 -0.4991 0.1543 -1.2273 3.7650

Kurtosis 4.6711 2.4281 2.6500 5.4596 1.5259 8.0085 1.3897 5.6373 2.5603 2.7236 18.8327

Jarque-Bera 40.1458 14.2958 23.1583 203.80 28.523 455.048 28.525 87.473 3.1743 67.118 3381.173

Probability 0.0000 0.0007 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2045 0.0000 0.0000

Sum 3.9375 1839.34 1192.43 591.20 1187.19 3000.22 771.68 3.8034 1179.17 2331.38 3306.59 Sum Sq. Dev. 1.2939 23.600 5.5685 39.602 66.946 32382.1 1348.1 1.6718 6.9457 94.971 27057.45

Observations 264 264 264 264 264 264 264 264 264 264 264

L means the logarithm form of the variables, RIPC- Indice de Precios y Cotizaciones returns, RJCI – Jakarta Composite Index returns, IP- Industrial production,

ER- Exchange rate, CP- Commodity price index, IR- Interest rate, MSCI – Morgan Stanley Capital International and FFR-Federal funds rate.

Table 13 shows that the mean values of ER, IP, MSCI, RIPC and FFR are less than the median

values, which suggests that the data falls close to the tail on the left side of a probability density

in a bell-shaped curve; while the mean values of CP and IR are greater than the median values

which suggest that data falls to the right tail of a probability density curve. The maximum value

of RIPC is 0.19 while the minimum price is -0.30, which indicates that RIPC data is less

dispersed; this means there is less variability in the data. IR data is more dispersed. However,

IP, FFR and ER show that data is moderately close together. In all, there is an evidence of

minimal variability in MSCI, ER and CP indices. FFR exhibits a moderate variability while IR

exhibits a very high variability.

The Jarque–Bera test determines whether a series is normally distributed or not. It measures

the difference between the skewness and the kurtosis of the series and compares it with those

from normal distribution. The null hypothesis of the normal distribution of data is not rejected

when the probability of the table above is used. When the probability is less than 0.05, the null

hypothesis is rejected. Looking at the variables in Table 13 above, they show a probability that

is less than 0.05, and the output suggests that all variables used in the analysis for Mexico are

not normally distributed. Kurtosis is a statistical test to measure the flatness or peakedness of

the distribution of the series. IP, FFR, MSCI and CP are platykurtic, which means that their

distributions are flat in proportion to normal; this is as a result of the value of the kurtosis which

is less than 3. RIPC, ER and IR are leptokurtic, which implies that their distribution is peaked

when compared to the normal; this result is based on the value of kurtosis which is greater than

The negative skewness observed in ER and FFR indicates that ER, MSCI and FFR data falls

close to the tail on the left of the probability density in a bell-shaped curve, while the positive

skewness observed in IR, IP and CP indicates that the data of these variables fall to the right

side of the curve. RJCI, IP and CP exhibit nearly normal distribution which is in line with the

suggestion of the measure of central tendency (mean and median). When mean is greater than

the median, there is a positive skewness in place and when the mean is less than median, there

is a negative skewness in place. The maximum and minimum give the range of the data, and a

small range suggests that data is close together, but if they are large, it means data is more

dispersed.

The mean of RJCI is 0.01 while the maximum price is 0.28. The standard deviation is 0.08

which indicates a moderate variability in the Jakarta Composite Index. RJCI, MSCI, IP, ER,

and CP exhibit minimal variability. There is evidence of high variability in IR.

The Jarque–Bera test in Table 13 shows that all variables except IP are not normally distributed

as the probability of all variables is less than 0.05, hence the reason for the rejection of the null

hypothesis of the normal distribution of series for all variables except IP. The kurtosis test

output reveals that all variables except IR and RJCI are platykurtic which means the

distribution of the series are flat relative to the normal, while IR and RJCI is leptokurtic, that

is, the distribution of the variable peaked when compared to the standard.

Table 14 Descriptive Statistics Result for Nigeria and Turkey

Nigeria RNASI LOP LER IR

Turkey RXU100 LIP LER IR

Mean 0.0162 7.7173 4.7504 13.6439 0.0382 4.3708 -0.5649 42.0281

Median 0.0093 7.7097 4.9020 13.5000 0.0276 4.3496 0.2945 25.9850

Maximum 0.3827 7.8991 5.2388 26.0000 0.7978 4.8589 0.8306 435.9900

Minimum -0.3064 7.3426 3.2132 6.0000 -0.3903 3.8670 -4.7444 1.5000

Std. Dev. 0.0792 0.0919 0.3820 4.3011 0.1419 0.2612 1.5338 48.8131

Skewness 0.2673 -0.1316 -1.5963 0.6115 1.1858 0.0764 -1.2920 4.0676

Kurtosis 6.8375 2.9074 5.5417 3.8271 7.4803 1.7624 3.3804 28.5772

Jarque-Bera 165.143 0.8572 183.195 23.982 282.68 17.103 75.043 7924.19

Probability 0.0000 0.6513 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000

Sum 4.27 2037.37 1254.10 3602.00 10.091 1153.90 -149.15 11095.44

Sum Sq. Dev. 1.6522 2.2229 38.3888 4865.40 5.2968 17.9467 618.73 626657.3

Observations 264 264 264 264 264 264 264 264

RNASI – Returns of Nigeria All – Shares Index, RXU100 – Returns of borsa Instanbul National 100.

Table 14 above shows that variables such as ER and FFR, when represented by a density curve,

have long left tails; however, RNASI, OP, CP and IR data falls close to the right tail of the

probability density in a bell-shaped curve. OP exhibits nearly normal distribution which is in

line with the suggestion of the measure of central tendency (mean and median), and the mean

and median of the series are quite close when compared to other series. The maximum value

of RNASI is 0.38 while the minimum price is 0.31 which indicates that RNASI data is more

dispersed when compared to Mexico and Indonesia; this means there is moderate variability in

the data. IR and FFR data are more dispersed. However, MSCI, OP, CP and ER show that data

is moderately dispersed. In all, there is an evidence of minimal variability in RNASI, MSCI,

ER, OP and CP indices. FFR and IR exhibit high but moderate variability.

The Jarque–Bera test in Table 14 shows that all variables except OP are not normally

distributed as the probability of all variables except OP is less than 0.05, hence the reason for

the rejection of the null hypothesis of a normal distribution of series. The probability value for

OP, however, suggests that we should fail to reject the null hypothesis of a normal distribution

of the series. With the kurtosis value greater than 3 for RNASI, ER and IR, the series are said

to be leptokurtic. The distribution peaked when compared to the normal; however, CP, MSCI,

FFR and OP are platykurtic with their values less than 3, which signifies that distribution is flat

when compared to the standard. The result shows that all variables except OP are not normally

distributed in the case of Nigeria.

Table 14 shows CP, IP, RXU100 and IR, when plotted on a density curve, exhibit a possibility

of a long right tail, but for ER, MSCI and FFR, which exhibit negative skewness, the

distribution of the series have long left tails. The mean of MSCI and IP are very close to its

median which shows that series is nearly normally distributed when compared to all other

variables. The maximum value of RXU100 is 0.80 while the minimum value is -0.39 which

indicates that RXU100 data is the most dispersed when compared to the other three countries;

this means there is high variability in the data. IR also shows a very high variability, and FFR

is moderately dispersed. However, IP, CP, MSCI and ER show that data is moderately

dispersed. In all, there is evidence of high but moderate variability in RXU100, and moderate

variability in FFR, ER, MSCI, IP and CP indices. Whereas, IR exhibits very high variability.

The Jarque–Bera test in Table 14 shows that all variables are not normally distributed, as the

probability of all variables is less than 0.05; hence the reason for the rejection of the null

hypothesis of a normal distribution of series. The value of the kurtosis for all variables (which

is less than 3), indicates that all variables except RXU100, IR and ER are platykurtic; this

means that the distribution of these series is flat when compared to that of a normal distribution.

However, IR and ER distribution is peaked when compared to the normal which indicates that

the series is leptokurtic. The result shows that all variables that are selected for analysis are not

normally distributed in the case of Turkey.

Figure 17 Graphical Illustration to detect outliers

-.3 -.2 -.1 .0 .1 .2

-.12 -.08 -.04 .00 .04 .08

-.6 -.4 -.2 .0 .2 .4 .6

-.8 -.4 .0 .4 .8

-20 -10 0 10 20 30 40

-3 -2 -1 0 1 2 3 4

RIPC vs Variables (Partialled on Regressors)

-.6 -.4 -.2 .0 .2 .4

-.4 -.3 -.2 -.1 .0 .1 .2

-0.8 -0.4 0.0 0.4 0.8 1.2

-.8 -.6 -.4 -.2 .0 .2 .4 .6

-20 -10 0 10 20 30 40 50

-3 -2 -1 0 1 2 3

RJCI vs Variables (Partialled on Regressors)

-.3 -.2 -.1 .0 .1 .2

-.4 -.2 .0 .2 .4 .6

-.8 -.6 -.4 -.2 .0 .2 .4 .6

-8 -4 0 4 8

-4 -2 0 2 4

RNASI vs Variables (Partialled on Regressors)

-.3 -.2 -.1 .0 .1 .2 .3 .4

-.2 -.1 .0 .1 .2

-2 -1 0 1 2

-.6 -.4 -.2 .0 .2 .4 .6

-100 0 100 200 300 400

-3 -2 -1 0 1 2 3

RXU100 vs Variables (Partialled on Regressors)

5.2.2 Diagnostic Test Results

Figure 17 show the graphical illustration output of the regression analysis with each variable

with their relationship with the dependent variable stock returns (LIPC, LJCI, RNASI and

RXU100). The model for Mexico highlights 1995:2, which means the second month of the

year 1995 as a major break point; it also shows 2009:5. The model for Indonesia shows 1998:9

and 2008:2. The model for Nigeria shows 1994:11 and 2008:2, while the model for Turkey

shows 2000:3 and 2001:2 as breakpoints. As mentioned earlier in the previous chapter, the

graphical evidence of an outlier does not necessarily mean the outliers could be a structural

break. The Chow test result will clarify whether the outliers identified are actual breakpoints.

The Chow test with a null hypothesis of no breaks at specified breakpoints gives the following

outcome: for the estimated model for Mexico, 1995:4, 1995:2 and 2009:5 show F-statistics

10.98, 11.34 and 9.97 respectively with a probability less than 0.05. Since the probability is

less than 0.05, we reject the null hypothesis of no breaks at specified points and assume that

identified outliers are actual breakpoints. The F-statistics for the dates identified in the

estimated model for Indonesia, Nigeria and Turkey are 4.51, 5.37, 2.53, 54.7, 21.31 and 19.11

respectively. The F-statistics come with a probability value that is less than 0.05 which

indicates a rejection of the null hypothesis of no breaks at the above specified dates.

5.2.2.1 Structural Break

The Chow test identifies the models estimated for the four countries and the outcome shows

that each model has more than one breakpoint. This brings about the need to use a multiple

structural break test to identify how many structural breakpoints are in each model. Bai and

Perron (2003) proposed a multiple structural break test of L versus L+1 breaks. This implies

that the null hypothesis of no structural change is represented with L sequentially determined

break with an alternative of L+1 structural breakpoints. The Bai – Perron test allows

heterogenous error distribution across breaks and tests breaks using L + 1 versus L sequential

to determine the number of multiple breaks that exist in a model. The result was tested at 0.05

level of significance. The estimated model for Mexico shows no rejection of 0 versus 1 break

in the model, which connotes one break point for the model selected. The model estimated for

Indonesia provides an evidence against the rejection of null of 0 versus 1 but rejects the 1 versus

2 structural breaks, the break dates for both sequential and repartition was 1998:9. The model

estimated for Nigeria also show 1 sequential F – statistic determined break. The model

estimated for Turkey provided evidence of 1 sequentially F – statistic determined breaks as it

was significant at break test of 0 versus 1.

5.2.2.2 Multicollinearity Test Result

The VIF is a quantifiable measure of how much the variance is inflated in the model we are

about to estimate. The variance in this concept means standard errors. To calculate VIF,

𝑉𝐼𝐹𝑖 = 11 − 𝑅𝑖

Where 𝑅𝑖2 represents the proportion of the variance of independent variable i that is related to

the other independent variable in the estimated model. VIF measures how much variance of

the estimated regression coefficient is inflated as compared to when the predictor variables are

not linearly related.

Table 15 Variance Inflation Factor (VIF) Output for the MINT Countries

Variance Inflation Factor (VIF) Output for the MINT Countries

Mexico Coefficient Centred Indonesia Centred Nigeria Centred Turkey Centred

Variables Variance VIF Coefficient

Variable

VIF Coefficient

Variable

VIF Coefficient

Variable

C 0.163728 NA 0.060121 NA 0.298177 NA 0.161665 NA

LIP 0.021537 9.85094 0.004627 5.127304 0.006525 2.348573 0.021038 7.41309

LER 0.000527 4.500094 0.000336 5.092395 0.000610 3.792745 0.000137 4.581408

IR 6.58E-07 4.596090 4.11E-07 1.773733 3.11E-06 2.447481 5.41E-08 1.833884

LCP 0.000236 3.400034 0.00059 6.30115 0.000533 5.777355 0.002604 9.424234

FFR 1.38E-05 4.002833 1.87E-05 4.025859 1.36E-05 2.973380 5.02E-05 3.656001

MSCI 0.002496 9.69996 0.001797 6.765091 0.001082 4.135384 0.005767 7.357968

VIF 5.24 4.84 3.58 5.70

VIF – Variance Inflation factor, > 10 shows evidence of multicollinearity

Tables 15 show the variance inflation factors for the explanatory variables that the author

includes in the model estimation. Mexico, Indonesia, Nigeria and Turkey show VIF values of

approximately 5.24, 4.84, 3.58 and 5.70 respectively, which implies that the standard errors are

larger by a factor of the values derived from the output table. A value of 10 according to (Hair

et al., 1995) and (Kennedy, 1992) is recommended as the maximum level accepted. With the

result derived, we can be certain that the explanatory variables selected in this research study

are not highly correlated. When variables are not highly correlated it means they do not

essentially measure the same phenomenon.

Figure 18 Graphical Illustration of the Variables in their Level Form

94 96 98 00 02 04 06 08 10 12 14

RXU100

94 96 98 00 02 04 06 08 10 12 14

5.2.3 Graphical Illustration of Each of the Variables in Their Level Form

Taking data in their level form; the graphs in Figure 18 above especially the country

macroeconomic variables (LER), (LIP), (IR) and the stock return data depict some major

deviations from the mean. These changes occur commonly in all the illustrations mentioned in

1994. During this period, the Mexican government devalued the domestic currency (Peso)

against the US Dollar. The devaluation of the currency was announced by the president in the

last month of 1994; devaluation of the Peso led to investors’ doubts about policymaking, and

the fear of further devaluation gave rise to investors’ quest for foreign investment which ignited

capital flight out of the country. This explains the high variability (a sharp increase and decrease

in interest rate and industrial production respectively), the return data also show a major

deviation in 1994, 1998 and 2008 respectively. Even though, the United States organized

(through the International Monetary Fund, G7 and Bank of International Settlement) a bailout

for the nation to help boost investor’s confidence and discourage them from investing in other

emerging economies other than Mexico. The effort made did not produce the desired outcome,

instead the value of Peso deteriorated more and growth in the economy was only restored in

the late 1990s. This is noticed in the graph as data maintains minimal variability afterwards

(Cheol & Resnik, 2011).

In addition, the global and monetary policy variables, i.e. (LCP), (MSCI) and (FFR) exhibit

major deviations from the mean shortly before 2008 and 2009. The GFC happened at this time

and could be the reason behind the variability. It is also important to point out that both

commodity price index and Federal funds rate moved downwards during this time which

connotes a decrease in these variables during the time. FFR and MSCI in the panel show a

deviation from a trend in 2002 up until 2004, also in 2008.

In Figure 18 in the second panel at the top right, the variation noticed in the domestic country

variables, i.e. (RJCI), (LER), (LIP) and (IR), can be attributed to the Asian financial crisis.

The variables mentioned show noticeable movements shortly before and after 1998. In 1997

the Asian financial crisis, which originated as a result of the devaluation of Thailand currency

(Thai Baht), spread to neighbouring countries and even had an impact on the global economy

due to financial contagion. The three most affected countries were Thailand, Indonesia and

South Korea. This effect lingered on up until 1999 when southeast Asian countries raised their

interest rates (evidence in the sharp interest rate from less than 20% to above 60%) to attract

investors that were in search of a high rate of return. This attracted almost half of the total

capital flight in developing nations (Berg, 1999). Employment situation was affected adversely

by the slowdown in employment rate and retrenchment of those that were in active service

increased it also changed the level of savings and income in the economy. Shortages of food

and unemployment led to social unrest, and the resignation of the president contributed

immensely to this unrest as the country struggled with both social and political instability

(Klinken, 1999). The significant variations in the stock market and country macroeconomic

variables in the graphs are as a result of the shock from political and social instability in the

economy, most of which were obvious between 1997 and 1999. The impact of the GFC in

2007/2008 is evident in stock price index, and a minimal variance is also noticed in the

exchange rate. Surprisingly, IP exhibits great variation as it shows instability even in times that

the country seems to be in a state of rest.

Figure 18 at the bottom left panel show illustrations for the domestic variables, i.e. (RNASI),

(LER), (LOP) and (IR), and the variables show deviations during the same period. In the early

1990s, as seen on the graph of the variables mentioned, Nigeria was faced with a conflict that

arose in the oil-producing part of the country. The Niger-Delta region is the part of the country

that has the major territory in which crude oil is extracted; this region has an ethnic minority

group which is called the people of Niger-Delta. This group raised the issue of exploitation by

the government and decided to prevent extraction of oil from the area. This issue generated

political as well as ethnic unrest in the region. The ruling military government at the time

increased the presence of armed forces in the area which restored peace. As soon as the military

government handed over to the democratically elected government in 1999, the Niger-Delta

people sought for a dialogue with the administration, and both parties reached consensus

through deliberations. This is evident in the stability of variables between 2000 and 2008

(Oviasuyi & Uwadiae, 2010). The country also experienced a banking crisis in 2008/2009 as a

result of undercapitalization of some top banks in the economy; this was triggered by the sub-

prime crisis that emanated from the United States. The apex bank (Central Bank of Nigeria)

stepped in by injecting funds into the banking sector to safeguard stability; they also introduced

a guarantee on individual and interbank deposits (IMF, 2013). These facts explain the variation

in variables between 2008 and 2009.

Figure 18 in the bottom right panel, shows the deviations of both domestic and global variables

from the mean. Domestic variables, i.e. (RXU100), (LIP), (LER) and (IR), exhibit deviations

at different times; it is therefore expedient to go through some events within the country. In the

late 1980s, Turkey completed the second phase of financial liberalization; this removed

restrictions on capital movement and gave room to economic openness. There was a massive

short-term capital inflow in the economy, and the threat of capital reversal became a cause for

concern in policymaking; hence firms were committed to high interest rates. Commercial banks

leverage on this opportunity by borrowing at the world interest rate to lend at a higher national

rate. This led to the depreciation of the Turkish Lira. The currency crisis which began fully in

1994 lingered on for the whole year as commercial banks were busy servicing the foreign debt

accumulated (Feridun, 2008). Another economic crisis began in 2000 when the country

abandoned the stabilized rate in 2001; this happened as a result of the central bank deviating

from its quasi-currency board rule by injecting massive funds into the system to save some

banks that had liquidity problems in the economy. Most of the extra cash was used by the banks

to service foreign debt which put more downward pressure on the currency. This, however,

discouraged investors and the ones left in the market demanded a higher rate of return on their

investments which increased the country’s risk premium. The government resulted to floating

the Lira which brought the exchange rate stabilisation program to an end (Bibbee, 2001).

Table 16 Lag Length Selection Output

Mexico Indonesia

*indicates lag order selected by the criterion, LR: sequential modified LR test statistics (each test at 5% level), FPE: final prediction error, AIC: Akaike information criterion, SC: Schwarz information criteria and HQ: Hannan – Quinn

information criterion.

Lag LogL LR FPE AIC SC HQ LogL LR FPE AIC SC HQ

0 -566.6716 NA 2.08e-07 4.481810 4.578748 4.520798 -963.7106 NA 4.64e-06 7.583677 7.680615 7.622665

1 2128.975 5222.816 2.18e-16 -16.19512 -15.41961 -15.88321 1495.612 4764.937 3.08e-14 -11.24697 -10.47146 -10.93506

2 2336.930 391.5391 6.31e-17 -17.43695 -15.98287* -16.85213* 1708.409 400.6578 8.57e-15 -12.52663 -11.07256* -11.94181*

3 2386.841 91.24460 6.28e-17 -17.44407 -15.31143 -16.58633 1758.364 91.32298 8.52e-15 -12.53409* -10.40145 -11.67635

4 2459.692 129.1970 5.23e-17* -17.63041* -14.81919 -16.49975 1811.850 94.85430 8.26e-15* -12.56914 -9.757924 -11.43848

5 2497.458 64.90986* 5.75e-17 -17.54264 -14.05286 -16.13906 1873.194 105.4358* 7.55e-15 -12.66558 -9.175796 -11.26200

Nigeria

0 -519.3324 NA 1.44e-07 4.111972 4.208911 4.150961

Turkey

-1671.283 NA 0.001167 13.11159 13.20852 13.15057

1 1951.312 4786.873 8.75e-16 -14.80712 -14.03161 -14.49522 812.1048 4811.564 6.42e-12 -5.907069 -5.131561 -5.595162

2 2092.679 266.1675 4.26e-16* -15.52874 -14.07466* -14.94391* 992.1490 338.9896 2.31e-12 -6.930852 -5.476775* -6.346027*

3 2135.125 77.59656 4.49e-16 -15.47754* -13.34489 -14.61979 1062.964 129.4590 1.95e-12* -7.101283* -4.968637 -6.243540

4 2174.014 68.96766 4.88e-16 -15.39855 -12.58733 -14.26789 1091.880 51.27961 2.29e-12 -6.944371 -4.133157 -5.813711

5 2205.165 53.54032* 5.64e-16 -15.25910 -11.76931 -13.85552 1129.177 64.10436* 2.52e-12 -6.852942 -3.363158 -5.449364

5.2.4 Lag Length Selection Result

The tables below show the number of lag lengths suggested by AIC, SC and HQ.

Table 16 show the lag length suggested by three different information criteria, which are: AIC,

SC and HQ. For Mexico, the information criterion AIC suggests a lag length of 4, HQ and SC

recommends lag lengths of 2. For Indonesia, AIC information criterion suggests a lag length

of 3 while SC and HQ information criteria suggest lag lengths 2 and 3 respectively. For Nigeria,

AIC information criterion suggests a lag length of 3 while SC and HQ suggest lag lengths of

2. For Turkey, AIC suggests a lag length of 3, while SC and HQ suggest a lag length of 2 and

3 respectively. The lag length selection result shows that information criteria have chosen

different lag lengths; however, Khim-Sen (2004) suggests that the AIC information criterion

produces the least underestimation among all criteria and also affirms that the problem of

overestimation is negligible in all cases. Given this, the author would stick to the AIC

information criterion, which means that a lag length of 4 is chosen for the Mexico model and

lag lengths 3 for other countries. We can now proceed to the unit root test to ascertain the non-

stationarity of data.

5.2.5 Unit Root Test Result

The time series variables considered in the study are financial (stock market index) and

economic (macroeconomic factors) data which are known to be non-stationary in their level

form (Hill et al., 2008). This issue necessitates the use of unit root with structural break to

identify whether variables are of the same integrated order or not. Before investigating the

short- and long-run equilibrium relationship between the variables, econometric methodology

requires each of the time series data to be stationary. Tables 17 show the output result of

variables in level as well as their first difference state.

Table 17 Unit Root with Structural Break Output

* indicates that variables are significant at 5% critical level

Panel A Dickey-Fuller Test statistics

Dickey-Fuller Test statistics

Mexico (H0: Unit root /Non-stationary)

Indonesia (H0: Unit root /Non-stationary)

Variables Level First Difference Order of Variable Variables Level First Difference

Order of Variable

RIPC -16.98* ------ I(0) RJCI -14.49* ------

LIP -3.76 -18.95* I(1)

LIP -4.09 -5.49* I(1)

LER -6.40* ------ I(0) LER -4.83* ------ I(0)

IR -5.76* ------ I(0) IR -5.06* ------ I(0)

LCP -2.20 -5.61* I(1) LCP -2.20 -5.61* I(1)

FFR -2.98 -4.50* I(1) FFR -2.98 -4.50* I(1)

MSCI -3.09 -15.53* I(1) MSCI -3.09 -15.53* I(1)

Panel B

Nigeria

Turkey

Variables Level First Difference Order of Variable

RNASI -16.94* ------ I(0) RXU100 -18.40* ------ I(1)

LOP -3.58 -6.37* I(1) LIP -3.17 -5.95* I(1)

LER -4.45 -6.20* I(0) LER -5.65 ------ I(0)

IR -3.63 -10.83* I(0) IR -7.79 ------ I(0)

LCP -2.20 -5.61* I(1) LCP -2.20 -5.61* I(1)

FFR -2.98 -4.50* I(1) FFR -2.98 -4.50* I(1)

MSCI -3.09 -15.53* I(1) MSCI -3.09 -15.53* I(1)

5% Critical values -4.52 10% Critical values -4.26

The Dickey–Fuller unit root test with structural break shows that variables such as LIPC, LIP,

LCP and FFR are integrated of order 1, which implies that they are differenced stationary.

However, IR, RIPC and LER show stationarity in their level form, which means that they are

I (0). The t-statistics are used as a guide line to choose whether to reject or not to reject the null

hypothesis of the presence of unit root. The result in Table 17 suggests a rejection of null

hypothesis when LIP, LCP, MSCI and FFR are I (1), also a rejection of null hypothesis of unit

root when IR and LER are I (0). This result indicates that variables in the model are not of the

same order.

There is a suggestion of rejection of the null hypothesis of unit root when MSCI, LIP, LCP

and FFR are integrated of order 1, also there is rejection of null hypothesis when RJCI, LER

and IR are integrated of order 0. This indicates that variables are not also of the same order.

Panel B above shows that all variables except RNASI exhibit non-stationarity in level but

stationary after first differencing. This depicts that all variables except RNASI are integrated

of the same order I (1). RNASI is stationary in level form which indicates integration of order

I(0). The null hypothesis of unit root is not rejected for all variables except RNASI in level

form but rejected for all variables in first differenced form which indicates that variables are

stationary after first differencing. The result above is different from the case of Mexico and

Indonesia.

The table above shows that all variables are not integrated of the same order. There is a need

to use a test that accommodates variables that are combinations of order 1 and 0. The ARDL

approach

5.2.6 Cointegration Result

The Johansen approach to cointegration is only suitable for variables that are integrated of the

same order, which means the variables must be order 1 or 0. The ARDL approach on the other

hand allows for a combination of the integrated order, the approach is not suitable for a variable

that is integrated of order 2. Since the result of the unit root provides an evidence of a

combination of order 0 and 1 for all the countries, the ARDL approach is therefore employed

to investigate the long – run relationship between stock returns and macroeconomic variables

in this study.

Table 18 ARDL Cointegration test Result

Table 18 shows the result of the long-run relationship between the variables. The estimated

model for Mexico shows F-statistic value of approximately 18.25 which is greater than the I0

and I1 critical value bounds. The highest critical value on the table above is 3.9 which is less

than 18.25 F-statistic value. The result suggests a rejection of the null hypothesis of no long-

run relationship among variables. The result indicates a long-run relationship among variables

estimated for Mexico. The result shows that estimated model for Indonesia with F-statistic

value of approximately 19.60 is higher than the highest critical value bounds 3.9. The result

suggests a rejection the null hypothesis of no cointegration among variables. This indicate long-

run relationship among variables estimated for Indonesia.

The table also present the F-statistic value of approximately 38.08 and 37.23 for the estimated

model for Nigeria and Turkey respectively. The F-statistic value is greater than the highest

value of the critical bound value 3.9, which indicates a long-run relationship among variables

estimated.

5.2.7 Long-Run Estimation

The table below shows the long – run coefficient of the model estimated for the four countries.

The coefficients are presented with the standard error, t – statistic value as well as the

probability level of each of the independent variables.

Null Hypothesis: No long-run relationships exist (Mexico)

Null Hypothesis: No long-run relationships exist (Indonesia)

Null Hypothesis: No long-run relationships exist (Nigeria)

Null Hypothesis: No long-run relationships exist (Turkey)

Test Statistic Value K Value K Value k Value k

F-statistic 18.246 7 19.604 7 38.079 7 37.225 7

Critical Value Bounds

Significance I0 Bound

I1 Bound

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.9

Table 19 Long-Run Coefficients

Panel A

Mexico Variable Coefficient Std. Error t-Statistic Prob.

Indonesia Variable Coefficient Std. Error t-Statistic Prob.

MSCI 0.024213 0.042728 0.566671 0.5715 MSCI 0.052101 0.035733 -1.458066 0.1461

LIP -0.053481 0.123271 -0.433854 0.6648 LIP -0.042733 0.065656 -0.650865 0.5158

LER 0.001695 0.019448 -0.087176 0.9306 LER 0.03448*** 0.020345 1.694843 0.0914

LCP 0.016622 0.012049 -1.379512 0.1690 LCP 0.004873 0.019166 0.254271 0.7995

IR 0.0012*** 0.000775 1.833916 0.0679 IR -0.00084*** 0.000512 -1.652433 0.0998

FFR -0.001938 0.002822 -0.686824 0.4928 FFR 0.001317 0.003838 0.343078 0.7318

DUM01 0.028*** 0.014866 1.895543 0.0592 DUM03 -0.004567 0.016053 -0.284461 0.0463

C 0.139525 0.328760 0.424397 0.6716 C 0.243220 0.211996 1.147288 0.2524

Panel B

Nigeria Variable Coefficient Std. Error t-Statistic Prob.

Turkey Variable Coefficient Std. Error t-Statistic Prob.

LOP -0.094202 0.071940 1.309448 0.1916 MSCI 0.1194*** 0.067532 1.768689 0.0782

MSCI 0.002880 0.036194 -0.079560 0.9367 LIP 0.4585* 0.154241 -2.972728 0.0032

LER -0.06403* 0.024118 2.654927 0.0085 LER 0.014828 0.014971 0.990498 0.3229

LCP 0.007126 0.023041 -0.309290 0.7574 LCP 0.0967*** 0.050688 1.909459 0.0574

IR 0.00278*** 0.001484 1.878795 0.0615 IR -0.000419 0.000284 -1.473745 0.1418

FFR -0.003851 0.003475 1.108193 0.2689 FFR 0.0115** 0.005961 -1.945657 0.0528

DUM97 -0.07404* 0.022967 -3.223807 0.0014 DUM00 -0.0864** 0.035660 -2.423037 0.0161

C -0.955725 0.528558 -1.808174 0.0718 C 0.886933 0.395167 2.244449 0.0257

*,**,*** show significance at 1%, 5% and 10% level

Table 19 shows the coefficients of each of the independent variables with a sign. The result for

Mexico suggests a positive long – run relationship between MSCI equity index, exchange rate,

global commodity price index, interest rate and the IPC equity returns. The result also suggests

a negative long – run relationship between industrial production and Federal funds rate. the

coefficients show that a 1% increase in MSCI world equity index, exchange rate, global

commodity price and interest rate, result in approximately 0.02%, 0.002%, 0.02% and 0.001%

increase in stock returns in Mexico stock exchange market. It also suggests that a 1% increase

in industrial production and Federal funds rate, decreases the stock returns by 0.05 and 0.03%

respectively. All the results are not statistically significant at 0.05 level. However, the

relationship between interest rate and stock return is significant at 0.10 level.

There is an evidence of long – run relationship between chosen independent variables and

Jakarta composite stock returns. The result shows a positive long – run relationship between

MSCI index, exchange rate, commodity price index, Federal funds rate and stock returns, but

a negative long – run between industrial production, interest rate and stock returns. The result

suggests that a 1% increase in MSCI index, exchange rate, commodity price index and Federal

funds rate brings about 0.05%, 0.03%, 0.005% and 0.001% increase in Jakarta stock returns.

The result also suggests that a 1% increase in industrial production and interest rate decreases

stock returns by 0.04 and 0.0008% respectively. The result however, show no significance at

5% but the positive long – run relationship with exchange rate and the negative relationship

with interest rate show significance at 0.10 level.

The result for the model displayed in Panel B show a positive long – run relationship between

MSCI index, commodity price index, interest rate and stock returns in Nigeria with the

following coefficients; 0.003, 0.007, and 0.003 respectively. The result also suggests that a 1%

increase in oil price index, exchange rate, Federal funds rate decreases stock returns by 0.09,

0.06 and 0.04% respectively. The result show that the negative long – run relationship between

exchange rate and stock returns is significant at 0.01 level. The positive long – run relationship

between interest rate and stock returns is significant at 0.10 level. All other variables show no

significance.

The result in the table, i.e. Panel B represents the long – run relationship between the

macroeconomic variables selected and Borsa Istanbul stock returns. The result shows a positive

long – run relationship between MSCI index, industrial production, exchange rate, commodity

price index, Federal funds rate and stock returns. It also shows a negative relationship between

interest rate and stock returns. The result shows that a 1% increase in MSCI index, industrial

production, exchange rate, commodity price and Federal funds rate increases stock returns by

0.12%, 0.46%, 0.01%, 0.10% and 0.01% respectively. It also shows that a 1% increase in

interest rate, decreases stock returns by 0.0004%.

The t-statistic shows that the positive long – run between industrial production, Federal funds

rate and stock returns are significant at 5% level and the positive relationship between MSCI

index and commodity price index are significant at 0.10 level. The long – run relationship

between interest rate, exchange rate and stock returns show no significance at 1%, 5% or 10%

level.

The ARDL cointegration and long – run form in Table 20 below produces least square

regressions that use both lags of both dependent and independent variables. This means that all

the macroeconomic variables as well as stock returns are included in the regression.

Taking a closer look at the result of the coefficients, the result is undoubtedly hard to interpret

the coefficients of VAR as it includes many variables and lags. One lag of a variable may

suggest one thing and the other the opposite; there are, therefore, no dynamics between

variables such as industrial production, exchange rate, Federal funds rate and stock price index

that are investigated, hence why the IRF and VDC are employed as they are the best tools to

interpret VAR.

The long – run estimation shows the dummy variables that are included in model to represent

the break dates which was explained earlier to have significant on the stock returns of the MINT

countries. The R-squared for the model estimated for Mexico is 0.48 with F-statistics of 10.51

at 0.000 probability level. The r-squared means that the variables estimated (which includes

the lag of stock returns and lags of other macroeconomic variables) including the dummy

variable shows that the model explains 48% of the variability of stock returns in Mexico.

The R –squared for the model estimated for Indonesia shows that the model explains about

43% of the variability in Jakarta stock returns. The model is reliable with a F-statistics of 8.49

and a probability level of 0.000. The R –squared for the model estimated for Nigeria is 21%,

which indicates that the variables selected and their lags explain about 21% variability in All –

Shares index returns. The result shows a corresponding F – statistics of 4.1 with 0.00

probability level.

The R – squared for the model estimated for Turkey in the table below shows that the variables

in the table explains about 31% of variability in Borsa Istanbul stock returns. The result shows

F-statistics of 7.28 with a probability level less than 0.01.

Table 20 ARDL Long -Run Estimation

RIPC is the returns of Mexican IPC index, RJCI is the returns of Jakarta composite index, RNASI is the returns of Nigeria ALL- shares index and RXU100 is the returns of Borsa Istanbul National.

Mexico Variable Coefficient Std. Error t-Statistic Prob.*

Indonesia Variable

Coefficient Std. Error t-Statistic Prob.*

Nigeria Variable

Coefficient Std. Error t-Statistic Prob.*

Turkey Variable Coefficient Std. Error t-Statistic Prob.*

RIPC(-1) -0.013585 0.057712 -0.235394 0.8141 RJCI(-1) 0.0474 0.0633 0.7485 0.4549 RNASI(-1) -0.1327 0.0611 -2.1697 0.0310 RXU100(-1) -0.1394 0.0620 -2.246 0.0255

MSCI 0.884114 0.085003 10.40095 0.0000 RJCI(-2) -0.1634 0.0603 -2.7083 0.0073 LOP 0.1067 0.0815 1.3079 0.1921 MSCI 1.2645 0.1878 6.7313 0.0000

MSCI(-1) -0.860406 0.088010 -9.776202 0.0000 MSCI 0.9479 0.0961 9.8633 0.0000 MSCI 0.2446 0.1085 2.2536 0.0251 MSCI(-1) -0.7071 0.2739 -2.5811 0.0104

LIP 0.337898 0.274877 1.229269 0.2202 MSCI(-1) -0.6964 0.1555 -4.4782 0.0000 MSCI(-1) -0.2479 0.1121 -2.2112 0.0280 MSCI(-2) -0.4213 0.2039 -2.0655 0.0399

LIP(-1) -0.327520 0.329198 -0.994900 0.3208 MSCI(-2) -0.0532 0.1586 -0.3357 0.7374 LER 0.0725 0.0275 2.6348 0.0090 LIP -0.2461 0.1667 -1.4756 0.1413

LIP(-2) -0.675106 0.335823 -2.010306 0.0455 MSCI(-3) -0.2563 0.1161 -2.2073 0.0282 LCP 0.1502 0.1193 1.2594 0.2091 LIP(-1) -0.2763 0.1683 -1.6415 0.1020

LIP(-3) 0.509888 0.258340 1.973711 0.0496 LIP 0.0759 0.0770 0.9857 0.3253 LCP(-1) 0.1407 0.1880 0.7481 0.4551 LER 0.0168 0.0170 0.9913 0.3225

LER -0.302157 0.133744 -2.259212 0.0248 LIP(-1) 0.0637 0.0760 0.8381 0.4028 LCP(-2) -0.1358 0.1873 -0.7252 0.4690 LCP -0.2323 0.1859 -1.2495 0.2127

LER(-1) -0.041057 0.198864 -0.206456 0.8366 LIP(-2) -0.1873 0.0745 -2.5120 0.0127 LCP(-3) -0.1631 0.1176 -1.3873 0.1666 LCP(-1) 0.3425 0.1918 1.7859 0.0753

LER(-2) 0.368513 0.131295 2.806765 0.0054 LER 0.0384 0.0226 1.6986 0.0907 IR 0.0031 0.0016 1.8732 0.0622 IR -0.0009 0.0002 -3.7281 0.0002

LCP -0.142733 0.085011 -1.679003 0.0945 LCP 0.0054 0.0213 0.2543 0.7994 FFR -0.0798 0.0365 -2.1874 0.0297 IR(-1) 0.0001 0.0002 0.6639 0.5073

LCP(-1) 0.138408 0.085335 1.621948 0.1061 IR -0.0060 0.0019 -3.1079 0.0021 FFR(-1) 0.0735 0.0629 1.1681 0.2439 IR(-2) 0.0008 0.0002 3.1385 0.0019

IR 0.006370 0.002090 3.047024 0.0026 IR(-1) 0.0051 0.0019 2.6831 0.0078 FFR(-2) -0.0456 0.0639 -0.7131 0.4764 IR(-3) -0.0005 0.0002 -2.2193 0.0274

IR(-1) -0.010854 0.003544 -3.062218 0.0024 FFR -0.0251 0.0319 -0.7883 0.4313 FFR(-3) 0.1894 0.0626 3.0229 0.0028 FFR -0.0132 0.0068 -1.9347 0.0542

IR(-2) 0.007851 0.003399 2.309944 0.0217 FFR(-1) -0.0385 0.0562 -0.6847 0.4942 FFR(-4) -0.1331 0.0359 -3.7006 0.0003 DUM00 -0.0984 0.0408 -2.4093 0.0167

IR(-3) -0.003342 0.001790 -1.867254 0.0631 FFR(-2) 0.1123 0.0567 1.9810 0.0487 DUM97 -0.0838 0.0264 -3.1723 0.0017 C 1.0106 0.4504 2.2436 0.0258

FFR -0.030096 0.020746 -1.450699 0.1482 FFR(-3) -0.0472 0.0319 -1.4774 0.1409 C -1.08 0.6008 -1.8017 0.0728

FFR(-1) 0.028611 0.020694 1.382570 0.1681 DUM03 0.0477 0.0488 0.9790 0.3285

DUM02 0.157301 0.056601 2.779115 0.0059 C 0.2714 0.2377 1.1418 0.2546

C 0.516295 0.372678 1.385363 0.1672

5.2.8 Short-Run Component Estimation

Table 21 ARDL Error Correction Term

Mexico Variable Coefficient Std. Error t-Statistic Prob.

Indonesia Variable

Coefficient

Std. Error t-Statistic Prob.

Nigeria Variable

Coefficient

Std. Error

t-Statistic Prob.

Turkey Variable Coefficient Std. Error t-Statistic Prob.

D(RIPC(-1)) 0.086736 0.054720 1.585090 0.1142 D(RJCI(-1)) 0.150706 0.059735 2.52288

7 0.0123 D(LOP) 0.085 0.11426 0.751 0.4534 D(MSCI) 1.256428 0.175425 7.162191 0.0000

D(MSCI) 0.905932 0.080451 11.260667 0.0000 D(MSCI) 0.957221 0.092911 10.3025

9 0.0000 D(MSCI) 0.2340 0.10619 2.2034 0.0285 D(MSCI(-1)) 0.411669 0.198226 2.076766 0.0389

D(MSCI(-1)) -0.142724 0.100771 -1.416322 0.1579 D(MSCI(-1)) 0.318514 0.114999 2.76970 0.0061 D(LER) 0.1111 0.13749 0.808 0.4194 D(LIP) -0.237804 0.140653 -1.690708 0.0922

D(LIP) 0.302057 0.252035 1.198473 0.2319 D(MSCI(-2)) 0.240045 0.114573 2.0951 0.0372 D(LCP) 0.146 0.11620 1.2646 0.2072 D(LER) -0.061386 0.152601 -0.402262 0.6878

D(LER) -0.253481 0.124556 -2.035074 0.0429 D(LIP) 0.087657 0.071118 1.2325 0.2190 D(LCP(-1)) 0.306 0.11847 2.5861 0.0103 D(LCP) -0.223853 0.184529 -1.213105 0.2263

D(LER(-1)) -0.477167 0.123460 -3.864962 0.0001 D(LIP(-1)) 0.195528 0.070830 2.7605 0.0062 D(LCP(-2)) 0.174 0.11583 1.5043 0.1338 D(IR) -0.000899 0.000214 -4.195244 0.0000

D(LCP) -0.081728 0.083996 -0.972997 0.3315 D(LER) 0.093228 0.062191 1.4990 0.1352 D(IR) -0.0026 0.00463 -0.5813 0.5616 D(IR(-1)) -0.000209 0.000258 -0.810722 0.4183

D(IR) 0.006137 0.001869 3.284234 0.0012 D(LCP) -

0.041568 0.101107 -0.4111 0.6813 D(FFR) -0.084 0.03533 -2.3872 0.0177 D(IR(-2)) 0.000595 0.000235 2.533442 0.0119

D(IR(-1)) -0.003196 0.001686 -1.895709 0.0592 D(IR) -

0.006183 0.001840 -3.3610 0.0009 D(FFR(-1)) -0.006 0.03899 -0.1619 0.8715 D(FFR) -0.026771 0.045216 -0.592065 0.5544

D(FFR) -0.020757 0.020573 -1.008960 0.3140 D(FFR) -

0.024599 0.031296 -0.7860 0.4326 D(FFR(-2)) -0.0606 0.03898 -1.5483 0.1228 D(DUM00) -0.140319 0.122135 -1.148887 0.2517

D(DUM01) -0.020769 0.054146 -0.383581 0.7016 D(FFR(-1)) -

0.064669 0.034270 -1.88705 0.0604 D(FFR(-3)) 0.1339 0.03489 3.8259 0.0002 CointEq(-1) -1.141029 0.061559 -18.535679 0.0000

CointEq(-1) -1.131667 0.088468 -12.791859 0.0000 D(FFR(-2)) 0.047780 0.031259 1.52859 0.1277 D(DUM97) -0.0730 0.07361 -0.9949 0.3208

D(DUM03) 0.053044 0.047523 1.1161 0.2655 CointEq(-1) -1.1396 0.06083 -18.7259 0.0000

CointEq(-1) -

1.089985 0.086598 -12.5866 0.0000

Table 21 above shows the error correction coefficients for the model estimated for Mexico,

Indonesia, Nigeria and Turkey are negative; -1.13, -1.09, -1.14 and -1.14 respectively. The

result shows that the error correction terms are negative and significant at 1% level. The table

displays the effect of each independent variable with their lag values as selected automatically

by the software. The table presents the number of lags used by the software to estimate ARDL

cointegration and long – run form. The table presents an evidence to support that the following

variables are significant in explaining the short – run variation in IPC return index; MSCI,

exchange rate, first lag of exchange rate, interest rate. All this show significance at 5% level.

Table 21 also shows that MSCI in its present and one month later significantly explain the

stock return index, it exhibits that lag one of industrial production and interest rate explains

stock return index in Indonesia. The results are significant at 5% level. The model estimated

for Nigeria shows that MSCI, one lag of global commodity price index, Federal funds rate and

lag 3 of Federal funds rate are significant in explaining changes in the Nigeria All – shares

return index. The results show significance at 5% level. The model for Turkey shows the

following variables as the ones that explain Borsa Istanbul stock returns index; MSCI and its

first lag, interest rate and its second lag.

5.2.9 Impulse Response Function

Cointegration and vector error correction models have shown the long-run relationship between

stock market indices and selected macroeconomic variables where deviation from a systemic

shock in the short run is corrected. However, the VECM, VAR and cointegration analyses do

not specify whether the shock is from any of the return indices (RIPC, RJCI, RNASI and

RXU100) or the macroeconomic variables. It is, therefore, important to employ the impulse

response function and variance decomposition so as to analyse and identify the reaction of

stock return to a unit shock introduced to the macro variables. The impulse response function

shows the response of RIPC to shocks added to each macroeconomic variable, while the

variance decomposition in illustrates the proportion of variation in RIPC as a result of shocks

from macroeconomic variables. Although there are different types of impulse response

function, the generalized impulse is employed in this study because it is independent in the

ordering of the variables. Figure 19 shows the impulse response and variance decomposition

of RIPC; RJCI; RNASI and RXU100 to exchange rate. interest rate, industrial production,

commodity price index, Federal Funds rate and MSCI global equity index.

1 2 3 4 5 6 7 8 9 10

Response of RIPC to RIPC

1 2 3 4 5 6 7 8 9 10

Response of RIPC to LMSCI

1 2 3 4 5 6 7 8 9 10

Response of RIPC to LIP

1 2 3 4 5 6 7 8 9 10

Response of RIPC to LER

1 2 3 4 5 6 7 8 9 10

Response of RIPC to LCP

1 2 3 4 5 6 7 8 9 10

Response of RIPC to IR

1 2 3 4 5 6 7 8 9 10

Response of RIPC to FFR

1 2 3 4 5 6 7 8 9 10

Response of RIPC to DUM01

Response to Cholesky One S.D. (d.f . adjusted) Innovations

1 2 3 4 5 6 7 8 9 10

Response of RJCI to RJCI

1 2 3 4 5 6 7 8 9 10

Response of RJCI to LMSCI

1 2 3 4 5 6 7 8 9 10

Response of RJCI to LIP

1 2 3 4 5 6 7 8 9 10

Response of RJCI to LER

1 2 3 4 5 6 7 8 9 10

Response of RJCI to LCP

1 2 3 4 5 6 7 8 9 10

Response of RJCI to IR

1 2 3 4 5 6 7 8 9 10

Response of RJCI to FFR

1 2 3 4 5 6 7 8 9 10

Response of RJCI to DUM98

1 2 3 4 5 6 7 8 9 10

Response of RNASI to RNASI

1 2 3 4 5 6 7 8 9 10

Response of RNASI to LMSCI

1 2 3 4 5 6 7 8 9 10

Response of RNASI to LOP

1 2 3 4 5 6 7 8 9 10

Response of RNASI to LER

1 2 3 4 5 6 7 8 9 10

Response of RNASI to LCP

1 2 3 4 5 6 7 8 9 10

Response of RNASI to IR

1 2 3 4 5 6 7 8 9 10

Response of RNASI to FFR

1 2 3 4 5 6 7 8 9 10

Response of RNASI to DUM97

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to RXU100

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to LMSCI

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to LIP

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to LER

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to LCP

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to IR

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to FFR

1 2 3 4 5 6 7 8 9 10

Response of RXU100 to DUM00

Figure 19 Impulse Response Function Graphs

Impulse Response Function Graphs (Mexico)

The illustration above shows that RIPC shows a largely positive response to shocks introduced

to the index for two periods and slightly positive afterwards; the response of the return index

to MSCI is positive in the first two periods and negative between the third and fourth periods,

it however show no significant changes afterwards; Shocks introduced to industrial production

causes a positive response in the first month and negative afterwards; the return index

responded sharply negative to shocks introduced to exchange rate in the first three periods and

positive afterwards; shocks introduced to commodity prices show the return index responded

slightly in the first month, negative from period two and no significant response after period 3;

RIPC responded slightly negative in the first month to shocks introduced to interest rate,

positive response between periods two and four and slightly negativ3e afterwards; the response

of the return index to shocks introduced to Federal funds rate is negative throughout the period

investigated.

Impulse Response Function Graphs (Indonesia)

Figure 19 at the top right shows the reaction of RJCI to its shock, which is largely positive for

the ten periods. RJCI shows a positive response to shocks from MSCI for 3 periods and tends

towards negative afterwards; RJCI show a slightly positive response to shocks introduced to

industrial production in the first period and sharp negative response between periods 2 and 4,

it exhibits slight negative response afterwards; the return index show a positive response to

shocks introduced to exchange rate for two periods and positive afterwards; A shock introduced

to commodity price index shows a positive response for seven periods and no significant

changes afterwards; the response of RJCI to interest rate is largely negative from the first to

third period and slightly negative afterwards; lastly the response of the return index to federal

funds rate is negative for two periods, positive between periods three and four and no

significant response afterwards.

Impulse Response Function Graphs (Nigeria)

The bottom left set of graphs show the response of RNASI to shocks introduced to the macro

variables; the response of the return index to shocks introduced to the index itself shows a

largely positive response for two periods, a negative response between periods two and three

and no significant response afterwards; the return index show a slightly positive response to

shocks introduced to oil prices.; the RNASI responded positively for at least four periods to

shocks introduced to MSCI and slightly negative afterwards; the response of RNASI to shocks

introduced to exchange rate is positive all through the 10 periods; the same positive response

to shocks introduced to commodity price index but negative after period five; the RNASI

responded negatively for three periods to shocks introduced to interest rate and positive

afterwards; the response of the return index to shocks introduced to Federal funds rate is

slightly negative for two periods and positive afterwards.

Impulse Response Function Graphs (Turkey)

The bottom right graphs show the response of Borsa Istanbul return index to shocks introduced

to itself and macroeconomic variables investigated in this study. The returns show a largely

positive response to shock introduced to the index itself; it exhibited a positive response to

shocks introduced to MSCI for three periods and negative afterwards; the return index

responded to shocks introduced to industrial production and interest rate negatively all through

the ten periods; it responded positive to shocks introduced to exchange rate and commodity

price index positively throughout the periods investigated; the RXU100 responded slightly

positive to shocks introduced to Federal funds rate is positive for about two months and

negative afterwards.

5.2.10 Variance Decomposition

Table 22 Variance Decomposition output

Period Mexico RIPC MSCI LIP LER LCP IR FFR DUM01

Indonesia RJCI MSCI LIP LER LCP IR FFR DUM98

1 100.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 100.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

5 90.855 0.3266 1.1696 4.7724 0.1802 0.4293 0.2522 2.0140 85.206 2.3796 1.1112 1.6764 0.7360 5.5502 0.3257 3.0144

10 90.144 0.3843 1.5969 4.7319 0.3088 0.5680 0.2570 2.0083 76.417 2.9389 1.6176 8.9784 0.6611 4.8517 0.3439 4.1905

15 89.399 0.4527 2.0386 4.6914 0.4311 0.6779 0.2807 2.0274 67.801 2.8982 2.0366 17.068 0.6332 4.5561 0.3523 4.6533

20 88.646 0.5149 2.4687 4.6536 0.5482 0.7790 0.3303 2.0586 60.665 2.7718 2.3919 23.832 0.6158 4.4432 0.3469 4.9316

Period Nigeria RNASI MSCI LOP LER LCP IR FFR DUM97

Turkey RXU100 MSCI LIP LER LCP IR FFR DUM00

1 100.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 100.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

5 91.579 1.4380 0.5565 1.1235 2.4504 0.4769 0.2117 2.1635 89.431 0.8386 0.5917 4.2663 0.9852 3.1081 0.2742 0.5046

10 88.122 1.4335 1.1874 1.8509 2.3925 0.9132 0.3830 3.7166 83.543 1.0966 0.7909 4.5124 1.1458 8.0907 0.3438 0.4761

15 84.858 1.4671 1.8001 2.5199 2.3879 1.2833 0.5764 5.1060 79.065 1.4338 0.9799 4.7383 1.2559 11.589 0.4904 0.4460

20 81.817 1.4970 2.3678 3.1462 2.3870 1.6206 0.7727 6.3908 75.211 1.7361 1.1433 4.9140 1.3392 14.591 0.6431 0.4209

Note: The first quadrant is the output result for Mexico, the second quadrant moving clockwise, is the output result for model estimated for Indonesia, the next is the model estimated for Turkey and the last is the output of the model estimated for Nigeria.

Variance Decomposition (VDC) (Mexico)

Table 22 above shows the variance decomposition output which identifies the level of

accountability of macroeconomic variables to variation in stock returns. The table shows the

MINT countries output in four quadrants. The first quadrant shows the result for Mexico, it

shows that changes in return of IPC index is driven mainly by its own variation in the first

period where it accounts for 100% of its own variation and by the end of fifteenth and

twentieth period, about 89.40% and 88.65% respectively accounted for by its own variation.

By the end of twentieth period, MSCI account for 0.51% and exchange rate account for

almost 4.65%, industrial production account for almost 2.47% whereas Federal funds rate,

interest rate and global commodity price index account for less than 2% of the variation in the

return index. The dummy variable account for 2.06% variation in the return index.

Variance Decomposition (VDC) (Indonesia)

The second quadrant reveals that changes in stock returns are driven by its own variation in

the first period, where it accounted for 100% of its variation, by the end of the fifteenth and

twentieth periods, the return accounts for account 67.80% and 60.67% of its variation. MSCI,

IP, ER, CP, IR and FFR accounts for 2.77%, 2.39%, 23.83%, 0.62%, 4.44% and 0.35%

respectively. Another observation of the results is that the proportion of the variation in the

return index explained by ER increases in subsequent periods. From period five, exchange

rate seems to be the leading variable followed by the dummy variable and then interest rate.

Variance Decomposition (VDC) (Turkey)

The third quadrant shows that changes in Borsa Istanbul stock returns is driven mainly by its

own variation, it accounts for 100% of its variation in the first period. By the end of the

fifteenth and twentieth period, it accounts for 79.07% and 75.21% respectively. In the

twentieth period, interest rate accounted for 14.59%, exchange rate account for 4.91%

variation, industrial production account for 1.14%, MSCI account for 1.74% and Federal

funds rate accounted for 0.64% and global commodity price index account for 1.34%. All

variables show increasing trend from periods one to twenty.

Variance Decomposition (VDC) (Nigeria)

The last quadrant reveals that changes in stock returns in Nigeria stock exchange like the

other stock returns, is driven by its own variation in the first period. However, by the end of

fifteenth and twentieth period accounts for 84.86% and 81.82% of its variation. In the

twentieth period, global commodity price index account for 2.39% of the variation in the

return index, exchange rate account for 3.15%, interest rate account for 1.62%, Federal funds

rate account for 0.77%, MSCI account for 1.50% and oil prices account for 2.37%. the result

upholds exchange rate as the leading variable that cause the most variation in the return

index.

5.2.11 Granger Causality Output

Table 23 Granger Causality Output

Dependent Variables

Independent Variables (Mexico)

Dependent Variables

Independent Variables (Indonesia)

RIPC LIP LER LCP IR FFR MSCI RJCI LIP LER LCP IR FFR MSCI

RIPC ---- 3.48 10.32* 1.14 0.33 1.20 0.95

RJCI ----- 6.38** 3.10 1.40 18.59* 0.54 4.78***

LIP 1.29 ----- 1.70 2.95 3.13 5.93** 0.51

LIP 2.86 ------ 7.36** 3.53 0.56 2.30 2.27

LER 2.78 6.99** ------ 2.83 18.76* 10.36* 3.16

LER 3.65 0.46 ------- 1.36 6.77** 0.18 2.61

LCP 0.46 0.99 0.70 ----- 0.78 2.71 15.91*

LCP 2.80 3.95 1.57 ------- 1.12 2.67 18.08*

10.97* 6.36** 18.34* 1.66 ----- 2.73 18.57*

3.24 5.03*** 27.63* 1.21 ------ 0.26 10.20*

FFR 8.97* 0.04 5.45*** 0.24 2.15 ------ 10.18* FFR 1.78 5.55*** 1.32 0.13 0.37 ------ 3.12

MSCI 3.84 0.87 7.02** 1.62 0.28 2.47 ------ MSCI 3.46 1.57 4.14 2.79 5.90* 4.36 -------

Dependent Variables

Independent Variables (Nigeria)

Dependent Variables

Independent Variables (Turkey)

RNASI LOP LER LCP IR FFR MSCI RXU100 LIP LER LCP IR FFR MSCI

RNASI ------ 1.23 1.03 8.74* 2.18 0.96 2.11

RXU100 ------- 0.41 17.29* 3.26 11.28* 0.82 2.63

LOP 0.36 ------ 0.06 1.20 0.69 1.17 1.81

LIP 4.18 ------ 8.15** 5.27*** 17.61* 6.98** 2.71

LER 4.27 1.50 ------- 8.09* 6.91** 1.63 1.77

LER 9.34* 2.28 ------ 0.03 57.20* 6.32** 9.62*

LCP 2.04 0.87 0.71 ------- 0.29 4.53 22.22* LCP 1.41 0.40 0.02 ------- 0.58 3.75 18.8* IR 3.74 2.83 1.98 0.52 ------- 4.69*** 1.50 IR

11.22* 2.97 15.11* 7.71** ------ 0.72 5.12***

FFR 5.51*** 0.16 1.98 0.14 1.23 ------- 11.13* FFR 7.26** 0.97 3.54 0.01 3.03 ------- 8.72*

MSCI 2.57 0.73 0.70 1.08 2.56 5.77*** ------ MSCI 0.97 3.40 0.19 0.91 2.11 3.12 -------

*, **, *** show significance at 1%, 5% and 10% level

The existence of a relationship between variables does not necessarily prove causality or, better

still, give the direction of influence. Time moves clockwise and not anti-clockwise; this means

that, if event A occurs before event B, then one could say that event A causes B. However, it

is impossible to think that event B is causing A; this only signifies that events in the past could

cause an event to happen in the future and not vice versa (Koop, 2000). What Granger causality

does is to ascertain whether a variable contains useful information for predicting another

variable (Diebold, 2008). We employ multivariate causality through the VAR and VECM

techniques to verify if there is a causality running from the stock market index to selected

country and global macroeconomic variables. The tables below show all variables as dependent

and independent variables and the results are analysed below.

The result above in Table 23 shows all variables and their predictive causality; the major way

to determine whether to reject or not to reject the null hypothesis in Granger causality output

is to check the F-value and the corresponding p-value. If the p-value is less than 0.05, we reject

the null hypothesis, and if the p-value is greater than 0.05, this suggests failure to reject the null

hypothesis at 5%; there is also room for f- value with a p-value less than 0.10, and this means

we can reject at 10% level of significance. The output result shows the Granger causality that

runs from one variable to the other so as for the author to determine if there is a reverse

causation or not. RIPC is our variable of interest which means our concentration will be on

how the selected macroeconomic variables Granger cause RIPC.

The null hypothesis of ‘IP does not Granger cause RIPC’ is not rejected with a chi – square

value of 3.48; the result suggests a rejection of the null hypothesis of variables such as global

commodity price index, interest rate, Federal funds rate and the MSCI index. The reason being

that the chi – square value displayed in the table against these variables are 1.14, 0.33, 1.20 and

0.95 respectively. However, the null hypothesis of ER does not Granger cause RIPC is rejected

with a chi – square value of 10.32 and at 1% level of significance.

It is evident with the result presented that RIPC contains useful information for predicting

interest rate and Federal funds rate. The table shows uni – directional causality running from

IPC index return to interest rate and Federal funds rate. It exhibits a bi - directional causality

running from IPC return index to exchange rate and vice – versa.

Granger Causality Result (Indonesia)

The table presents the Granger causality output for Indonesia, it presents evidence in support

of a rejection of the null hypothesis of IP does not Granger cause RJCI; IR does not Granger

cause RJCI and MSCI does not Granger cause RJCI. This means there is an evidence of uni –

directional causality running from industrial production, interest rate and MSCI to Jakarta

composite index returns. The result shows no evidence of causal relationship between the stock

returns and exchange rate, commodity prices and Federal funds rate.

Granger Causality Result (Nigeria)

The table shows that Nigeria All – shares index returns is only influenced by global

commodity price index, this is because the null hypothesis that variables such as oil prices,

exchange rate, interest rate, Federal funds rate and MSCI Granger cause RNASI is not

rejected with chi – square values of 1.23, 1.03, 2.18, 0.96 and 2.11 respectively. The result

however presents an evidence to reject the null hypothesis of global commodity price index

Granger cause RNASI with a chi – square value of 8.74. The result suggest that we reject the

null hypothesis at 1% level of significance. The result connotes that there is a uni –

directional causality running from global commodity price index to stock returns in Nigeria.

The result also shows that the Nigeria All – shares index return have a uni – directional with

Federal funds rate, however the causality runs from the stock return index to the Federal

funds rate.

Granger Causality Result (Turkey)

The result in the last quadrant on the right show an evidence to reject the null hypothesis of

exchange rate and interest rate does not Granger cause Borsa Istanbul return index. This is

supported with a chi – square values of 17.29 and 11.28 respectively. It however suggests the

failure to reject the null hypothesis of industrial production, global commodity index, Federal

funds rate and MSCI does not Granger cause the stock return index in Turkey. The result

suggests a bi – directional causality between exchange rate, interest rate and stock returns, it

also shows a uni – directional causality running from the stock return index to Federal funds

rate. The bi – directional causality evidence suggest that causality runs from exchange rate,

interest rate to stock returns and vice – versa.

5.2.12 Variance Ratio test

Table 24 Variance Ratio Output Table

Mexico (RIPC) Indonesia

(RJCI)

Nigeria

(RNASI)

Turkey (RXU100)

Var [z] 5.20 4.52 4.51 5.57

Prob. <0.05 <0.05 <0.05 <0.05

To ascertain whether stock returns are predictable has long been an area of interest. Lo and

MacKinlay (1988) developed what is known as the overlapping variance ratio test. The

statistical software used in this study (EViews 9) allows user to perform joint tests of the

variance ratio restriction for several intervals it performed for period 2, 4, 8 and 16. The null

hypothesis for the test is that stock returns are a martingale which connotes random movement;

the output result in Table 23 shows the variance z score with the probability. The probability

value for the z score for MINT stock return indices are less than 0.05 which indicates a rejection

of the null hypothesis of random walk movement in the stock indices in the MINT countries.

The result indicates that MINT countries’ stock price indices are not efficient or exhibit a weak

form of market efficiency.

5.2.13 Diagnostic Tests

The diagnostic test helps to show the level of reliability of the estimated model in this study.

The author checks the model by conducting the CUSUM test. The test is developed by Brown

et al. (1975) and finds instability by plotting the cumulative sum of the recursive residual with

a 5% critical line. Instability is shown when the sum goes outside the 5% critical line.

Figure 20 Cusum Test Output

Figure 20 indicate stability in the equation estimated for the MINT countries. This shows that

the equation estimated during the sample period (1993month 1 to 2014month 12) is stable. This

means the result derived in this study is reliable.

1998 2000 2002 2004 2006 2008 2010 2012 2014

CUSUM 5% Significance

1998 2000 2002 2004 2006 2008 2010 2012 2014

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

94 96 98 00 02 04 06 08 10 12 14

Table 25 The Autocorrelation output

Country Lag AC PAC Q - Stat Probability Adj. R-sq

Mexico 1 -0.007 -0.007 0.0129 0.910 0.398626

2 -0.012 -0.012 0.0483 0.976 0.400119

3 -0.095 -0.095 2.4532 0.484 0.397707

4 -0.056 -0.058 3.2996 0.509 0.401873

5 -0.020 -0.023 3.4024 0.638 0.405819

6 -0.118 -0.130 7.1361 0.308 0.401411

7 0.106 0.093 10.168 0.179 0.399235

8 0.016 0.006 10.236 0.249 0.399227

9 0.032 0.010 10.523 0.310 0.403472

10 -0.008 -0.001 10.540 0.394 0.394590

Indonesia 1 -0.007 -0.007 0.0117 0.914 0.368572

2 -0.024 -0.024 0.1664 0.920 0.365857

3 0.006 0.006 0.1767 0.981 0.366969

4 -0.008 -0.008 0.1918 0.996 0.369057

5 -0.079 -0.079 1.8654 0.867 0.361216

6 -0.025 -0.027 2.0396 0.916 0.367699

7 -0.125 -0.130 6.2835 0.507 0.367584

8 -0.059 -0.064 7.2378 0.511 0.367554

9 -0.072 -0.085 8.6598 0.469 0.365213

10 0.021 0.007 8.7769 0.553 0.362921

Nigeria 1 0.007 0.007 0.0124 0.911 0.135358

2 -0.019 -0.019 0.1039 0.949 0.137863

3 0.010 0.010 0.1279 0.988 0.128205

4 0.088 0.087 2.1802 0.703 0.136560

5 -0.081 -0.082 3.9145 0.562 0.139375

6 -0.017 -0.013 3.9964 0.677 0.129821

7 -0.076 -0.081 5.5570 0.592 0.129607

8 0.124 0.121 9.6987 0.287 0.131562

9 0.007 0.015 9.7128 0.374 0.137522

10 -0.011 -0.009 9.7450 0.463 0.140207

Turkey 1 0.005 0.005 0.0066 0.935 0.243368

2 -0.028 -0.028 0.2219 0.895 0.239027

3 0.050 0.050 0.8856 0.829 0.243713

4 0.042 0.041 1.3566 0.852 0.234546

5 -0.055 -0.052 2.1622 0.826 0.241998

6 -0.143 -0.144 7.7568 0.256 0.241541

7 0.127 0.124 12.135 0.096 0.235575

8 0.075 0.073 13.665 0.091 0.240639

9 -0.006 0.016 13.674 0.134 0.240621

10 0.082 0.082 15.526 0.114 0.240318

*Probabilities not valid for the equation

Table 24 above presents the autocorrelation output for the ARDL model estimated in this study,

it is important that the errors of this model are not serially correlated but independent, the

reason is that, if the errors of the model are not independent, the parameter estimates won’t be

consistent, this is because of the lagged value of stock returns that appear as regressors in the

model in Table 20. We are interested in the p – values which are reported in the table above,

the p – values are all greater than 0.10 which presents a strong suggestion that there is no

evidence of autocorrelation in the residuals of the model estimated.

5.3 Summary

This chapter has given a step-by-step analysis of the model used to determine the possible long-

and short-run relationships that exist between country and global macroeconomic variables and

the stock markets in the MINT countries. The chapter began with the descriptive statistics that

show the distribution of the variables in question; this is done to help familiarise the author

with the type of variable we are about to estimate. Variables are cross-checked when an unusual

pattern is noticed in their distribution. The next step is to check for collinearity among the

explanatory variables through the OLS estimation; this helps to remove the variables and make

sure results derived from them are not biased or spurious. After the multicollinearity check,

variables are graphically inspected or observed to have a clue of how they behave or move over

time; there is a saying by Yogi Berra that ‘you can observe a lot by watching’. (Kaplan, 2008).

While observing the variables, the questions that must run through the mind is if series look

stationary or not; to be able to give an acceptable response to this issue, physical observation

can only give a suggestion without a concrete empirical evidence, hence the need for a unit

root test for stationarity. As mentioned in the previous chapter, the concept of lag length is

paramount as the Granger causality model is particular about the appropriate selection of lag

length; the section has expressly used three different information criteria to determine the

appropriate lag length for the model estimation.

The ARDL cointegration result, which shows the long-run equilibrium relationship among

variables, has been estimated and the shot – run dynamics have also been estimated by

determining whether there exists relationship between variables or not. The impulse response

function, which is a useful tool to interpret the cointegration result, shows a graphical

interpretation of the model results and the variance decomposition helps to apportion numerical

values to the impulse response graphical representation. The final test, which is the Granger

causality test, shows the direction of causality of each of the explanatory variable to the

variables in question (stock return indices).

CHAPTER SIX

6.0 Discussion

6.1 Introduction

The core objective of this chapter is to compare the results derived in the previous chapter

across countries. There have been mixed findings in the literature regarding the response of

emerging markets to domestic and global macroeconomic variables of which this research is

set to identify how the MINT countries respond to the same round of variables. These results

help in decision making in investments of funds, and portfolio managers would be able to

clearly identify how their country of interest responds to changes in macroeconomic factors;

this would also give policymakers an idea of how to make changes in policies whenever there

is tapering in global factors. The results provide suggestions on the issue of market integration

and segmentation and show the level of interaction of the MINT countries’ stock markets with

the world economy. It also helps to have an idea on how factors like monetary policy in

advanced economies and changes in global commodity prices contribute to the well-being of

developing nations. The descriptive statistics result shows the stock market index variability

and the macroeconomic variables employed in this research. The author generates a ranking

table so as to compare the level of variability in the MINT stock indices. Since a market with

high variability is said to be less predictable, the MINT countries’ stock markets are examined

to identify the most volatile among the stock indices. Investors who are interested in taking a

higher risk for a higher return would be able to compare notes.

Emerging markets are known to be more volatile and are likely to yield higher returns when

compared to developed ones; this explains why international investors target emerging markets

with a higher rate of return on their investment.

6.2 Variability of the Markets

The Mexican stock market exhibits moderate volatility with a maximum stock return of 0.19

and a minimum price of -0.29. This shows that changes in the rate of returns over the period

(1993–2014) are not far apart. Indonesia’s stock market exhibits moderate variability with a

maximum share price of 0.28 and a minimum price of -0.32; this also shows that changes in

stock returns for the period understudied are not distant. The Nigerian stock market exhibits a

moderate variability with a maximum rate of return of 0.38 and a minimum price of -0.31;

although the return indices are not as close as that of Mexico and Indonesia, one could notice

that the differences are almost negligible. Turkey’s stock market exhibits high but moderate

variability in its return index. With a maximum share return of 0.79 and a minimum return of

-0.39, the stock market shows the greatest variability when compared to Mexican, Indonesian

and Nigerian stock markets. The table below shows the variability ranking of the MINT

countries.

Table 26 Table of the Variability of the MINT Countries' Stock Markets

Countries Variability Rank

Mexico 4th

Indonesia 3rd

Nigeria 2nd

Turkey 1st

Table 25 above shows that the stock market in Turkey is less predictable when compared to

other countries included in the MINT acronym; the stock market in Mexico also shows a bit of

stability by exhibiting a minimal variability when compared to the other three countries

(Nigeria, Turkey and Indonesia).

6.2.1 The Long-Run Estimation

The cointegration results in Table 18 show that stock markets in Mexico, Indonesia, Nigeria

and Turkey exhibit a long-run relationship with the set of macroeconomic variables selected in

this research. The long-run equilibrium relationship result derived in this research is in line

with the findings of prima et al. (2013); Olukayode and Atanda (2010); Olorunleke (2014).

The findings also suggest a long-run relationship between the Nigerian stock market and

selected macroeconomic variables; this result is in line with the results of Olukayode and

Atanda (2010) as well as Olorunleke (2014) even though their variable selection is somewhat

different from the ones selected in this study. The result suggests that macroeconomic variables

and stock returns ‘move together in equilibrium’ (see Stewart, 2005 p. 798).

The model estimated for Turkey which show evidence of long – run relatioship with the

macroeconomic variables selected. The result suggests that selected macroeconomic variables

and stock returns in Turkey move together in equilibrium. The findings correspond with those

of (Semra & Ayhan, 2010) where they show an evidence of a long-run relationship between

two of the variables selected in this research (exchange rate and industrial production) and

stock return index in Turkey.

6.2.2 Specific Interaction Between Stock Market and Each of the Macroeconomic

Variables as Reported by the IRF Output

The responses for 10 months are displayed in Figure 19. It is observed that the reaction of

Mexico IPC return index to its lag is mostly positive which is expected; the response of the

IPC return index to industrial production shows a positive one in the first month and moves

towards negative in the subsequent months. The immediate positive response shows that an

increase in growth in Mexico’s economy increases returns for several months. Since an

increase in stock price suggests an increase in the present value of future cash flows of firms,

there is, therefore, an expectation of a higher return on investment whenever there is a boom

in the economy. In as much as this corresponds with our expectation, there is, however, a

negative response after the third month. The explanation for this finding goes thus: an increase

in the industrial production gives a boost to the manufacturing and mining industries in the

economy. When these sectors increase their level of production, there is a huge profit for firms

and their value as well as the equity returns increases; in as much as most of their production

inputs are imported, there will be a surge in import propensity (see Moreno-Brid & Ros, 2009).

The pressure on the import shifts the benefit of the high production to the country where most

of the production inputs are derived from. Moreno-Brid (1999) emphasized the negative impact

that trade liberalization is having in a country like Mexico.

Mexico’s IPC return index shows a negative response to a shock in the exchange rate; this

means that whenever there is depreciation in domestic currency, there is a negative signal sent

to stock market, this information pushes equity returns down instantly. According to Moreno-

Brid (1999), a developing country like Mexico has a higher demand for imported production

input, hence the beneficial impact of high exportation is eradicated in the economy – there is

an increase in demand for currencies of countries of import. The major country that Mexico

imports from is the US; therefore, an increase in the demand for US Dollars in Mexico puts

downward pressure on the value of the domestic currency. This shows that exchange rate risk

is not priced in Mexico IPC index; this is in contrast to the suggestion of Aggarwal & Harper

(2010) where they suggest that exchange rate risk should be incorporated in stock market index.

The result shows that the negative effect lasts for about 3 months, after which the market will

re-adjusts by absorbing the information and the impact becomes positive in the subsequent

months.

The response of Mexico IPC index to gloobal commodity price index is negative; as an export-

oriented economy, changes in commodity prices is expected to be good news in the country.

Mexico joined the free trade in 1994 (North American Free Trade Agreement-NAFTA) and

90% of their trade is under the free trade agreement. The country export products are oil, silver,

coffee, cotton, fruits and sugarcane. They are the third largest supplier of goods imported to

the US. However, since the economy does not largely depend on abundance of natural

resources, the result show no significant response of the return index to changes in global

commodity price index.The positive response of the stock return index to commodity prices is

expected.

Mexico IPC return index shows a negative reaction to interest rate; this is in line with the Fisher

effect where there is a suggestion of a negative feedback of an increase in interest rate. The

adverse reaction suggests that whenever the apex bank in Mexico (Banco de Mexico) uses

interest rate as a monetary policy tool in the country; there is a possibility that foreign investors

are attracted to the market because of the higher rate of return on investment. This explains the

positive impact in the second, third and fourth month. The impact later on tends to be negative

as companies finds it hard to borrow at a reasonable level or borrow at a higher rate to expand

businesses. This eats deep into the profit and does not give room for companies to take

advantage of new opportunities that presents itself.

The response of the Mexico IPC return index to the Federal funds rate is negative; just as we

have suggested earlier that an increase in the interest rate in Mexico attracts foreign investors,

one could also apply this to a possible increase in the Federal funds rate. Since the Federal

funds rate is the US interest rate, foreign investors are likely to move their funds from an

emerging market (which is usually categorized as a high risk environment) into a more defined

environment with less risk. The Federal Reserve in the US has maintained a near-zero rate

since the GFC. There has been a huge capital movement from developed markets like the US

to emerging markets; this is evident in the FDI data graphed in Figure 12. Any news to increase

the Federal funds rate would definitely result in capital flight out of emerging markets like

Mexico, this is why the major stock price index responds negatively to a shock in the Federal

funds rate.

The response of the IPC return index to MSCI is instantly positive for about two months.

According to Hau et al. (2010), countries that have increasing representation in the MSCI

Global equity index, experience currency appreciation upon announcement of index change.

However, since curreny appreciation as seen in the explanation of the result of exchange rate

shows that appreciation of currency could trigger a negative influence stock returns in Mexico,

hence why the positive impact of MSCI cannot last.

Indonesia

The plots in Figure 19 shows that the Indonesia JCI return response to its own lag is largely

positive as expected; it, however, exhibited a negative response afterwards. The response of

Indonesia JCI to exchange rate is positive and negative after two months. Indonesia is an

export-oriented country just like Mexico, most of their exports are sold to countries such as the

US, China, Japan and India. However, most of their imports are from countries like Singapore,

China and Japan. Since they get US Dollars in exchange for their products and import from

countries that have their currency value below the US Dollars, the impact on their economy is

expected to be positive. Nonetheless, most of the countries that are Indonesia’s import partners

exchange trade across borders with countries like the US, which means it will only take some

time for the impact of their trading to spread to Indonesia, hence why there is a negative impact

after a couple of months.

The response of Indonesia JCI to commodity price index is positive for seven months, this can

be attributed to a higher percentage of the country export which is oil and gas. Oil as a

commodity is an important part and the most traded commodity in the world. Indonesia also

has up to 14% of their exports in animals and vegetables; the food processing industry in

Indonesia is determined and has the advantage of using local raw materials to produce output.

A positive response of the stock return index shows that the economy benefits from positive

changes in global commodity price index.

Indonesia JCI shows a negative response to a shock introduced to interest rate, this means that

whenever interest rate is used as a monetary policy tool in Indonesia, there is a largely negative

impact for 3 months and slightly negative afterwards. This implies that firms in Indonesia could

have an alternative source of capital whenever there is an increase in interest rate. This could

be as a result of a number of Islamic banks in the country; since the significant difference

between Islamic and conventional banks is the interest-free rate, most firms would likely go to

where they would get an easier cost of capital for investment. Ismal (2011) compared the

growth of conventional banks and Islamic banks and discovered that Islamic banks’ growth is

higher than that of conventional banks in Indonesia; the outcome explains the slight response

of stock returns to shocks introduced to the interest rate on Indonesia JCI.

Indonesia JCI return index response to Federal funds rate is negative and the explanation for

this outcome is similar to the one presented in Mexico. The positive and negative response of

MSCI Global equity index suits the case explained under Mexico.

Nigeria

The Nigerian stock return index responded positively to shock introduced to itself; the reaction

of the stock index to exchange rate, however, is positive. This signifies that a shock added to

exchange rate causes the stock return index to increase in Nigeria. The depreciation of a

nation’s currency is not necessarily bad news for the stock market; this is because depreciation

of a national currency makes the product of such a country to be cheaper. Since Nigeria is an

export-oriented country (especially with the majority of the government revenue coming from

oil), it is expected that export partners would prefer to buy products such as crude oil at a

cheaper rate from Nigeria; by so doing, the country receives a higher capital inflow through its

balance of payment account. A higher revenue generation by the government increases capital

projects and brings about an increase in money supply and thus fosters the economy. If most

firms that are listed on the stock exchange are local companies, all they need to be concerned

with are the changes in exchange rate whenever they import raw materials because of the input

in their production process.

The result in Figure 19 shows that Nigeria stock return index shows no significant changes to

a shock introduced to oil production index; the result derived here is unexpected since the oil

sector plays a major role in the Nigerian economy. To explain this, we should point out that

although Nigeria is a major producer of oil, not every firm in Nigeria benefits from a growth

in the output capacity of oil. It is worthy of note again that increase in production does not

necessarily reflect in oil companies as most of these enterprises have to pay to refine the crude

oil before importing it back into the country for distribution. Since an increase in production

capacity increases the tax burden on oil companies, the impact is possibly neither positive nor

negative. This also explains the minimal improvement in the Nigeria indicator despite huge

export earnings (see Odulari, 2008).

Nigeria all-share return index shows a positive response to commodity price index; this means

that whenever there is an increase in commodity prices, the economy stands to gain from it.

This is expected as growth in commodity prices increases revenue generation for the country;

the oil boom in the 1970s gave a boost to the overall economy..This result is in line with

Adaramola (2011) where he showed a positive impact of the increase in oil prices on the stock

market.

The response of the Nigeria All-share Index to interest rate was negative for about three months

and positive afterwards. The results suggest that, whenever the interest rate is used as a

monetary policy tool in Nigeria, there is a negative immediate impact felt in the stock market.

This might be because firms that borrow this time have higher interest rate, espacially the ones

with variable interest rate; and when it lingers on for some time, most of these firms hedge

against such shocks by increasing the prices of goods and service so that the shocks is passed

on to consumers ( this is particular to countries where there is a very slim chance to obtain

alternative external financing). These findings are in accordance with the results of (Osisanwo

& Atanda, 2012) where they show interest rate to have a negative impact on stock returns in

Nigeria in their model estimation.

Nigeria All-Share Index reacted negatively to shocks introduced to Federal funds rate, this

response however lasted for 2 months before it shows no significant difference the return index

to Federal Funds rate. The negative result can be likened to the one given about capital flight

explanation under Mexico. The sharp negative reaction might be a result of the news and

investors’ response in the market. However, since the findings of this study show that the

depreciation of the Naira against the US Dollar does not send a negative signal to the stock

market. A research on the impact of domestic and foreign investment in Nigeria shows that

capital markets in Nigeria benefit from domestic investment rather than foreign investment.

The study attributed the findings to the slackness of the Nigerian stock exchange market to

embrace liberalization, and the development of the banking industry in the era understudied

(see Ezeoha et al., 2009). The banking industry in Nigeria is one of the most active in the stock

market with about 48.5% of the MSCI Nigeria index (MSCI, 2017); since most investors in the

market are within the country, it gives an explanation as to why exchange rate risk and changes

in Federal funds rate does not send a significant signals to the market in the long run.

The response of the return index to MSCI Global equity index is the same as the explanation

of other countries. Countries like Nigeria, which is classified as a frontier market have its stock

market to have downweighted feature which results in a lower permanent comovement of their

currency. Depreciation of the currency, increases stock returns as explained earlier.

Turkey

The response of the stock market in Turkey to its own shock is largely positive like other

countries; however, the response to industrial production shows a negative impact throughout

the period studied. Kadir (2008) using data from 1997–2005 shows no significance of industrial

production on the stock market in Turkey; however, Semra & Ayhan (2010), who used more

recent data (2003–2010), show a positive impact of industrial production on the stock market

in Turkey. The negative findings show that whenever there is economic growth in Turkey, the

stock market does not benefit from it; This means that the economic condition of the country

does not have a direct impact on the stock return index.

The response of the stock market in Turkey to exchange rate is positive; just like the

explanation given in the case of Nigeria, export partners increase their demand for the country’s

products which implies a boost in industrial production and exerts positively on the stock

market. This result is in line with that of Ozlen & Ergun (2012) on the impact of exchange rate

on the stock market in Turkey. The response of the stock market to shocks introduced to

commodity prices is positive and the explanation suitable for the one given in the case of

Nigeria.

The stock market in Turkey responded negatively for ten months to a shock introduced to

interest rate. The negative response could be as a result of higher cost of capital which supports

the Fisher effect.

The response of the stock market in Turkey to Federal funds rate is slightly positive in the first

three months and negative afterwards. This is because the stock market in Turkey responded

positively to shocks in exchange rate. This result could be as a result of Turkey as an export

destination; since most of the trading partners of Turkey are within the EU region, Middle East

and Asia, there is a possibility of most of the foreign investors coming from other parts of the

world rather than the US.

The response of Borsa Istanbul stock return index to MSCI index is similar to that of Nigeria

which is a positive response for three months and negative afterwards.

6.2.3 Macroeconomic Variables and How They Account for Variation in the Stock

Market and Their Causal Links

The variance decomposition outputs in Table 22 determine the forecast error variance of the

stock return index that can be explained by exogenous shocks to macroeconomic variables in

the MINT countries. This part of the research also explains the result of the Granger causality

in Table 23. The result shows the leading variables that explain the variations in each of the

stock markets. The result for Mexico shows that interest rate is one of the leading variable that

explains the variation in Mexico IPC index; the result confirms that about 0.77% of the

variation in the stock market in Mexico is explained by changes in interest rate; however, the

Granger causality result shows a uni-directional causality running from Mexico IPC return

index to the interest rate. Since the IRF shows a positive response of stock return index to

interest rate, the Granger causality suggests that growth in stock market enriches or increases

the wealth of local investors, and an increase in demand for money enhances the level of

interest rate. The result is in line with a researcher who suggests that a controlled interest rate

benefits Mexico’s stock market through demand pull way of attracting investors (see Alam &

Uddin, 2009).

Another important variable is the exchange rate, with about 4.65% in the twentieth month. The

exchange rate is an important macroeconomic variable as the Granger causality suggests a uni-

directional causality running from exchange rate to Mexico IPC index. The result indicates an

increasing impact of the exchange rate in the long run. This means that even though the IRF

result suggests a negative response of Mexico IPC return index, the impact on the stock market

diminishes after some time. It is also noteworthy that the well-being of the stock market

influences the exchange rate movement by giving the currency a boost.

Industrial production is also an important variable that explains variation in stock return index,

it accounts for about 2.47% variation. However, the Granger causality result does not suggest

a causal relationship between IPC stock return index and industrial production. MSCI index

and commodity prices show a minimal contribution to the variation in Mexico IPC index. The

IRF illustration in Figure 19 indicates that Mexico IPC index exhibits minimal changes as a

result of shocks introduced to both industrial production and commodity prices. The Federal

funds rate also accounts for a minimal variation in the stock market. However, there is evidence

of a uni-directional relationship running from Mexico IPC index to Federal funds rate. This

means that whenever a boom is experienced in the stock market, there is a corresponding

increase in capital flight into the economy. The capital flight from the US may cause a shortage

of money in circulation and the Fed responds to this by using the Federal funds as a monetary

tool to make loans cheaper (decrease in Federal funds) so as to increase money in circulation.

Indonesia

The major variable that account for the variation in Jakarta composite index the most is the

exchange rate; the proportion accounted for is 23.83%, and the Granger causality result shows

no causality running between the variables.

Interest rate accounts for 4.44% of variation after twenty months; the Granger causality result

also shows that there is a uni-directional causality running from interest rate to Jakarta

composite return index. However, the IRF shows a negative response of Jakarta Composite

Index to the interest rate. Industrial production account for a minimal variation in JCI returns,

the Granger causality show a uni – directional causality running from industrial production to

JCI returns. MSCI explains about 2.77% variation in JCI returns, the impact is positive from

the IRF response and the Granger causality confirms a uni – directional causality running from

MSCI index to JCI stock return. Commodity price index and Federal funds rate does not

exhibit a causal relationship with JCI stock returns. They account for less than 2% of variation

in the index altogether.

Nigeria

Commodity price index accounts for a significant variation in the Nigerian stock market, with

2.39%, and it also shows a uni-directional causality running from commodity price index to

stock return index. The result shows that changes in commodity prices influence the stock

market, and the dependence of the economy on oil could also lead to the high sensitivity of

stock market to changes in prices of commodity. Oil production index explains about 2.37%

of the variation in Nigeria stock return index. Granger causality output show no causal

relationship. Interest rate account for less than 1% in the variation of the stock index in Nigeria.

The Granger causality suggests no causality running from both oil production index to stock

market or vice versa – the same with the interest rate. Exchange rate accounts for 3.15% of the

variation in the stock market. However, the Federal funds rate accounts for 0.77% of the

variation in the Nigerian stock market and the Granger causality shows a uni-directional

causality running from Federal funds rate to the stock market.

Turkey

Industrial production index accounts for a negligible proportion in the variation of the stock

market in Turkey; commodity price index, exchange rate, MSCI index and Federal funds rate

account for 1.34%, 4.91%, 1.74% and 0.64% respectively. The Granger causality suggests a

uni-directional causality running from the two variables to the stock market in Turkey. The

result shows that a growing and stable stock market index influences movement in the two

variables. The result suggests a uni – directional causality running from stock returns to Federal

Funds rate. The result means that innovations in the market draw the attention of foreign

investors which draws capital from the developed economy to the emerging economy such as

Turkey; this explains the interaction between the stock market index and changes in monetary

policy in an advanced economy (the US).

Interest rate accounts for 14.59% of the variation in the stock index; the Granger causality

shows a bi-directional causality running from the stock market index in Turkey and interest

rate and vice versa – the same causality relationship with exchange rate. The findings show

that interest rate is an important macroeconomic variable that explains the movement in the

stock market index in Turkey.

6.3 Review of Research Aim and Objectives

The aim of this research is to compare how emerging markets (using the MINT countries)

respond to changes in the country and global macroeconomic variables in the short- and long-

run. In the first chapter, the aim has been sectioned into parts to make the research more focused

and meet the set goal. This section of the study takes a look at each of the objectives to observe

whether they are met in this research.

6.3.1 Objective One

To review the various theoretical frameworks such as arbitrage pricing theory (APT), efficient

market hypothesis (EMH) and analyse the empirical findings by various researchers on the

impact of macroeconomic factors on emerging markets as a prelude to this research.

The relevance of evaluating theory in this area is to establish the need for this research.

Theory of market behaviour has received the attention of academicians over the years. This

has changed from the 1950s (portfolio theory), 1960s (CAPT) and 1970s (EMH and APT).

One could say that researchers focus mainly on validating either the EMH or the APT. Since

intense arguments emerged to support or oppose either of these theoretical frameworks, it is

important for researchers to search for a statistical methodology to support their stand. This is

seen in the empirical evidence provided by Bhayu & Rider, 2012; Hsing et al., 2013;

Izedonmi & Abdullahi, 2011; Kadir et al., 2009; Olukayode & Atanda, 2010; Osamwonyi &

Osagie, 2012. Conflicting results derived by researchers may be due to the time frame used in

particular countries or the statistical method used for analysis. Although the literature relating

to emerging markets like Mexico, Indonesia, Nigeria and Turkey has received a considerable

attention from researchers who have empirically proved an existence of a relationship

between stock market and country macroeconomic variables in these countries, there is,

however, a major gap to clarify if the set of emerging markets investigated reacts to changes

in global macroeconomic factors to acertain the issue of integration.

There are benefits of investing in emerging markets, emerging markets like the MINT,

provides diversification opportunities to investors. According to Helmut (2000), international

diversification reduces risk better than domestic diversification because domestic exhibit

stronger correlations because of their joint exposure to common country shocks. This means

there are possibilities of reduced risks in markets that have negative or no correlations, the

benefits however depend on the correlation between foreign and domestic assets.

Diversifications exposes countries with potential growths to development opportunities. The

predictions by Goldman Sachs is that by 2030, emerging markets will command up to 59% of

the world output. Even though emerging markets are associated with high risks, their

performance is less correlated with developed markets, indicating possibilities of risk

reduction through diversification. In the early 1990s, emerging markets have high risk, high

rate of return and low correlation with developed markets; However, the performances of the

markets have changed due to liberalisation and globalisation. Integration with developed

markets and several financial crisis have reduced the benefits of emerging markets. Despite

these changes, the returns in the emerging market is higher than in developed markets.

This research tested the APT model using predetermined global and domestic

macroeconomic factors, the short and long – run interaction of stock market returns with the

factors show that both global and domestic factors are significant in determining expected

stock returns. The APT gives an alternative to a one risk factor model (CAPM) by suggesting

asset returns are exposed to various risk from different sources in the economy that cannot be

eradicated by diversification. These sources are considered to be related to the economy. This

study supports that risk is multidimensional and that total reliance on beta alone as suggested

by the CAPM is not reliable. It further gives a suggestion that the various risk assumption of

the APT model are not only related to the domestic economy but global economy.

This study is important for foreign and domestic investors in terms of portfolio diversification

and risk management. It contributes to the argument on the issue of market integration and

segmentation. The research is done using a multi-statistical method (more than one) adopted

from past literature to clarify the logical contradictory outcome in this type of investgation and

to give a suggestion on how much it should bother these sets of emerging markets whenever

news on tapering the Federal funds rate is aired or published.

6.3.2 Objective Two

To ascertain the efficiency of the MINT countries’ stock markets using trend analysis and the

variance ratio test

Pattern and trend analysis is a way of visually examining data; this objective is met in Chapter

5 (5.2.5) where the unit root test result is tabulated for the data collected. Data collated shows

volatility in their movement over time; Figure 18 shows the movement of data during economic

turmoil in Mexico; this was noticed during a major crisis like the Peso crisis in 1994. Figure

18 also show data clustering together between 1997 and 1998, which is as a result of the Asian

financial crisis which emanated from the Thai Baht crisis. The same Table shows variables in

Nigeria and significant movement in the variables were traced to oil price shocks. Nigeria’s

dependence on oil reflects on the movement of macroeconomic variables and the major stock

prices in the country. Figure 18 shows major changes in at least two periods. These years were

traced to a currency crisis in 1994 and the economic crisis in 2000 which happened in Turkey.

Since an efficient market assumes that stock price indices are informationally efficient, which

means that all stocks are priced correctly, the theory also suggests that research on security

prices is a waste of time for researchers. If the MINT countries’ stock markets were to be

efficient, we would expect that prices of stocks in the market should incorporate both internal

and external risks (national and global factors impact) but then, how does one explain the

movement in prices noticed during currency and economic crisis in these countries? This is the

underlying issue that prompted the author of this study.

The visual findings in this research clearly show progress in variables during the crisis in the

economy. Since the APT suggests that asset returns can be predicted when the relationship

between assets and common risk factors are known, one could generalise this by assuming that

asset returns in the MINT countries can be predicted when the relationship between asset

returns and common risk factors, such as exchange rate6 and economic growth7 as well as

global financial crisis8, are known. This fact explains why financial analysts are of the opinion

that it is possible to predict stock return movements using important macroeconomic variables;

it also explains the possibility of having financial experts like Warren Buffet beat the market.

Being a financial analyst is still a very lucrative profession; if markets were to be efficient,

there wouldn’t be anyone left in the profession. However, since finance experts are still able to

make excess profit by predicting asset price movement to know when to hold, sell or buy shares

as well as what asset to include in their portfolios, there will always be a need for this type of

research using updated data.

Even though the author suggests by visually observing the data that it does not exhibit random

walk movement, it is important to verify this by using a statistical test. The variance ratio test

result in Table 24 shows that the stock markets in Mexico, Indonesia, Nigeria and Turkey show

an evidence against random walk movement. The result shows that there is significant

deviation from efficiency in the MINT stock markets; the implication of this is that the market

6 Evidence from the visual changes in variables as a result of the currency crisis. 7 Evidence from changes in variable movement as a result of the economic crisis in Turkey. 8 Evidence from changes in stock prices as a result of the 2008/2009 GFC.

may offer profit to investors who have access to information at the expense of the less informed

ones. Guidi and Gupta (2013) suggest that the use of unit root to check for random movement

in data sets is common among researchers; they, however, suggest that other tests such as the

variance ratio test help to check for robustness of outcome.

6.3.3 Objective Three

To explain the reliability of the vector error correction model and cointegration in estimating

short- and long-term equilibrium relationships between stock market index and

macroeconomic variables.

The cointegrating estimation helps to determine the long-run relationship among variables.

This means it takes a general look at whether the error term of the variables moves in the same

direction. All independent variables are therefore taken into consideration at the same time in

the analysis. In the previous chapter a numerical value is attributed to give a proportion to each

of the independent variables estimated in the research. The cointegration output shows the

number of cointegrating equations among the variables. The result confirms a long-run

relationship between Mexico’s stock market and selected macroeconomic variables – the result

is the same in the case of Indonesia, Nigeria and Turkey.

The error correction model is the test that measures the time it will take stock returns to deviate

and go back to their equilibrium price after a long-run impact from macroeconomic variables.

The short-run dynamic is better explained using the IRF derived from the VECM; the result

reported in the previous chapter shows a positive short-run dynamic relationship between the

Mexico stock market and macroeconomic factors in variables such as industrial production,

MSCI and a negative short-run dynamic relationship between the Mexico stock market and

variables such as exchange rate, commodity price index, interest rate and Federal funds rate.

The result shows a positive short-run dynamic relationship between Indonesia’s stock market

index and variables such as industrial production, MSCI index and commodity prices, but

exhibits a negative short-run dynamic relationship with interest rate and Federal funds rate.

The result of the estimated model for Nigeria shows a negative short-run relationship with

interest rate and Federal Funds rate. The result confirms a positive short-run relationship

between the stock market and variables such as exchange rate, oil prices, commodity prices

and MSCI index. The stock market in Turkey shows a positive short-run dynamic relationship

with variables such as; MSCI index, exchange rate, commodity price index and Federal funds

rate. Since there is a clearer picture of the short-run dynamic relationship between the MINT

countries’ stock markets and macroeconomic variables, it is important to use a statistical tool

to identify the proportion of changes each of these macroeconomic factors account for in the

variation of the stock markets.

6.3.4 Objective Four

To determine whether MINT countries’ stock markets are integrated or segmented.

The issue of market integration has to do with how open a country is on the matter of trade.

There is a growing commitment to liberalization and trade integration through free trade

agreement formation. A country like Mexico has up to 12 free trade agreements with up to 44

countries (Villareal, 2012). Indonesia has eight free trade agreements in effect which consist

of two bilateral and six regional agreements (Ing, 2015). Turkey has 17 free trade agreements

in force and Nigeria has some regional and bilateral trade agreements with some countries

(Savaser, 2013). Openness to trade among nations contributes to its level of integration. This

is evident in a research carried out which shows that the Chinese stock market exhibits

segmentation when investigated before its involvement in the World Trade Organisation

(WTO) in 2001 and an increasing level of integration after the free trade agreement

membership (see Goh et al., 2013).

The investigation carried out in the MINT countries, especially the evidence provided in the

variance ratio analysis, shows evidence of sufficient integration in the markets, which means

MINT stock markets interact with global factors. The result suggests an integration of the

Mexico, Indonesia, Nigeria and Turkey stock market indices.

6.3.5 Objective Five

To develop a table of the stock markets according to their response to each country and global

macroeconomic variable that is selected, and to suggest recommendations for investment and

policymaking in the MINT countries.

The table below shows the countries investigated as well as the macroeconomic variables. For

clarification purposes, numbers 1–5 are given to the variables. Number 1 connotes the

macroeconomic variable that shows the most impact on the stock market using the variance

decomposition output in the previous chapter.

Table 27 Table of the Response of MINT Stock Market to Exchange Rate, Interest Rate,

Industrial/Oil production Index, Commodity Price Index and Federal Funds Rate

Countries/macroeconomic variables Exchange rate Interest

Industrial

production/ oil

production

Commodity price

Federal

funds rate

Mexico IPC index (1) 3 2 4 6 5

Indonesia JC index 1 (2) 4 5 6 3

Nigeria All-share index 1 4 3 (2) 6 5

Turkey national 100 index 2 (1) 5 4 6 3

The table above shows the weight of each of the selected macroeconomic variables on the

MINT countries’ stock market. The author selected the three variables that influence the stock

market index the most; the suggested impact of these variables will be discussed in the

concluding chapter. Considering the Granger causality output, the numbers in brackets

represent the variables that the Granger causality suggests variables have causal relationship

with the stock markets, which means these variables are the most important macroeconomic

variables that can be used to predict the MINT countries’ stock markets. We therefore, have

exchange rate as the most important variable, followed by industrial production and then

interest rate for Mexico. The table shows interest rate as the leading variable for Indonesia,

followed by exchange rate and MSCI index. For Nigeria, we have commodity price index as

the main variable, followed by exchange rate and oil prices. Turkey also has the interest rate

as the main variable, followed by exchange rate and MSCI index.

6.4. Review of Research Hypotheses

This part of the research examines the research hypothesis; since the objectives of the study

have been met, it is important to give a suggestion to the following assumptions established in

Chapter 3:

Mexico

market in Mexico

in Mexico

market in Mexico

in Mexico

𝐻03: Global factors have a significant influence on the stock market in Mexico

𝐻𝐴3: Global factors do not have a significant influence on the stock market in Mexico

Based on the cointegration result, for the first hypothesis we reject the null hypothesis of no

long-run relationship between selected macroeconomic variables and the stock market index

in Mexico; this means we suggest that selected macroeconomic variables have a significant

long-run relationship with the stock market in Mexico. For the second hypothesis, we also

reject the null hypothesis of no short-run dynamic relationship between stock market and

macroeconomic variables in Mexico; therefore, we suggest a significant short-run relationship

between the stock market and macroeconomic variables in Mexico. For the third hypothesis,

there is a suggestion to fail to reject the null hypothesis that global factors significantly

influence the stock market in Mexico.

Indonesia

market in Indonesia

in Indonesia

market in Indonesia

in Indonesia

𝐻03: Global factors have a significant influence on the stock market in Indonesia

𝐻𝐴3: Global factors do not have a significant influence on the stock market in Indonesia

The outcome suggests we reject the null hypothesis of no long-run relationship between

selected macroeconomic variables and stock market in Indonesia, for the second assumption,

we reject the null hypothesis of no short-run relationship between macroeconomic variables

and the stock market index and suggest a short-run dynamic relationship between stock market

and macroeconomic variables in the stock market in Indonesia. The result suggests failure to

reject the null hypothesis that global factors have significant influence on the stock market

index in Indonesia.

Nigeria

market in Nigeria

in Nigeria

market in Nigeria

in Nigeria

𝐻03: Global factors have a significant influence on the stock market in Nigeria

𝐻𝐴3: Global factors do not have a significant influence on the stock market in Nigeria

The result derived in the research suggests we reject the null hypothesis of no long-run

relationship between stock market and macroeconomic variables; we, therefore, suggest a long-

run relationship between selected macroeconomic variables and the stock market index in

Nigeria. We also reject the second null hypothesis and propose a short-run relationship between

stock market and macroeconomic variables in Nigeria. The result suggests failure to reject the

null hypothesis that global factors have significant effect on the stock market index in Nigeria.

Turkey

market in Turkey

in Turkey

market in Turkey

in Turkey

𝐻03: Global factors have a significant influence on the stock market in Turkey

𝐻𝐴3: Global factors do not have a significant influence on the stock market in Turkey

The result suggests we reject null hypothesis of no long-run relationship between stock market

and macroeconomic variables; we, therefore, suggest a long-run relationship between selected

macroeconomic variables and the stock market index in Turkey. We also reject the second null

hypothesis and propose a short-run relationship between stock market and macroeconomic

variables in Turkey. The result suggests failure to reject the null hypothesis of a significant

influence of global factors on the stock market index in Turkey.

6.5 Summary

The chapter gives an extensive implication of the result presented in the previous chapter; it

started by comparing the MINT countries’ stock markets’ variability, and interprets the long-

run as well as the short-run relationship result by comparing it to the result of past studies in

the field. It also provides a theoretical explanation of some possible interactions suggested in

the result. The chapter seeks to investigate whether the research meets the aim and the

objectives set in the first chapter. We also evaluate the research assumptions and propose

reasonable suggestions based on the methodologies employed in this research.

CHAPTER SEVEN

7.0 Summary, Conclusion, Recommendation and Opportunities for Further Research

7.1 Introduction

This chapter provides a reflection of the entire study. It covers the contents of the six chapters

already discussed. This research emanates from four research questions that explain the

interaction between stock market and macroeconomic (global and domestic) variables. The

previous chapter reviews the objectives and the hypothesis of the thesis and has ensured that

the estimated model meets each of the objectives; nonetheless, the author must ensure that

answers are given to the research questions stated at the beginning of the thesis. The summary

is done using a step-by-step approach to profound solutions to the research questions stated at

the start of the study. The chapter is concluded by explaining how the research has contributed

to theory and practice, especially in the areas of investment and policymaking.

The limitations of the research as well as opportunities for further research are discussed

towards the end of this chapter. Constraints in this research are mainly in the area of data

collection and accessing databases which the author experienced at the initial stage of the study.

Policy recommendation for policymakers in each of the countries is also mentioned in this

section of the research.

7.2 Summary

The thesis examines the interaction between stock returns in MINT countries and

macroeconomic (global and domestic) factors both empirically and theoretically. The study

reviewed monthly frequency data of key stock market return index in the MINT countries over

a period between 1993 and 2014. It is one of the studies that compare the impact of global

factors to that of domestic factors on the stock market in the set of emerging markets known as

the MINT. Although there is abundant research that investigates the impact of domestic

macroeconomic factors on emerging stock markets, there are a limited number of research

papers that clarify the factors that have the most impact on the markets. The relationship

between stock market and macroeconomic factors as suggested by some theoretical

frameworks gives an insight to how the variables are expected to influence stock return index;

however, having this at the back of the mind, the author has employed statistical methods to

confirm some of the theories.

This study uses some model estimation techniques to verify the impact of macroeconomic

variables on MINT stock markets. Furthermore, the results derived from these methods are

harmonized to avoid contradictions that have been an issue in this type of study. The thesis

employs the cointegration technique to study the long-run interactions between stock market

and macroeconomic variables; it also uses the VAR and VECM to examine the short-run

dynamic relationship between the variables. For consistency purposes, the IRF is used to

interpret the short-run interactions between stock market and macroeconomic variables. The

variance decomposition test aided in the classification of the variables that have the least and

the most impact on the stock market. Granger causality shows the direction of the relationship,

and these clarifies whether macroeconomic variables affect stock market or vice versa.

7.2.1 Key Findings

The extensive literature review indicates that there is empirical evidence that demonstrates the

relationship between stock market and domestic macroeconomic variables. Some support the

existence of a long-run relationship between stock market and macroeconomic variables such

as interest rate and exchange rates, while others show the evidence of no long-run relationship.

The result of this thesis shows that the stock returns and macroeconomic variables exhibits a

long-run relationship in the MINT countries. This shows that there is uniformity in the response

of this group of countries to selected macroeconomic variables.

The findings highlight a negative short-run and positive long-run interaction between variables

such as exchange rate, interest rate and stock returns; a negative short- and long-run

relationship between Federal funds rate and stock returns; and a positive short- and long-run

relationship between variables such as commodity price index, MSCI Global equity index and

stock returns; a positive short – run and a negative long – run interaction between industrial

production and stock returns the model investigated for Mexico.

The model estimated for Indonesia exhibits; a positive short- and long – run relationship

between variables such as exchange rate, MSCI world equity index, commodity price index

and stock returns; a negative short- and long – run relationship between interest rate and stock

return; a positive short – run and negative long – run between industrial production and stock

returns; and a negative short -run but positive long – run between Federal funds rate and stock

returns in Indonesia.

The short- and long-run relationship evidence in Nigeria shows a positive short- and long-run

relationship between variables such as commodity price index, MSCI world equity index and

stock returns; a negative short – run and positive long – run between interest rate and stock

returns; a negative short- and long – run interaction between Federal funds rate and stock

returns. The result also proposes a positive short - run and negative long-run relationship

between variables such as oil prices, exchange rate and stock returns respectively.

The result shows a positive short- and long-run relationship between variables such as

commodity prices, exchange rate, Federal funds rate, MSCI world equity index and stock

returns; a negative short but positive long-run relationship between industrial production and

stock returns; and a negative short but positive long – run relationship between interest rate and

stock returns in Turkey.

The MINT countries show uniformity in the positive relationship exhibited between

commodity price index, MSCI world equity index and stock returns; the countries also show a

negative short – run impact of interest rate on stock returns; the stock returns of Mexico,

Indonesia and Nigeria show similar patterns in the impact of Federal funds rate, which is

negative. Nigeria and Turkey show uniformity by exhibiting a positive relationship between

exchange rate and stock returns.

7.2.2 Theoretical Evidence and Practical Implication

The APT suggestion of determinants of asset return has received considerable attention.

Existing studies have tried to identify a number of systemic risk factors as suggested by the

theory. Many of these researchers have been inward looking, which means they focus on

factors that influence stock prices and returns in an economy within a particular country.

Although the DCF tries to model an equation to estimate the stock price of which most of the

domestic factors that have been used in recent studies can be traced to factors that influence

cash flows and discount rates, few articles have investigated the movement that is not attributed

to domestic factors in emerging markets. To fill this gap, the broad model developed in Chapter

3 has suggested that macroeconomic variables included in our model should be classified into

two, which are domestic and global macroeconomic variables. This gives an insight on whether

domestic factors fully determine movement in the stock return index of emerging markets. The

thesis therefore includes factors such as Federal funds rate, MSCI Global equity index and

commodity price index; these variables represent significant global business information and

monetary policy decisions. The link between global factors and emerging markets puts

emerging markets into a new light as a source to gain typical exposure to changes in global

economic activity.

The variance decomposition result in this thesis shows that for Mexico, domestic factors such

as exchange rate, industrial production and interest rate are the most important variables that

determine movement in stock prices. The result also shows significant interaction between the

global factors selected and the stock returns. However, the direction of causality helps to clarify

which of the variables impact the other, thus exchange rate is the only variable identified using

Granger causality that has a direct influence on stock return index. Since a positive interaction

exists between interest rate and the stock market, there is a proposition that policymakers in

Mexico could use interest rate as a tool to encourage foreign investment participation in the

economy; however, the government should try to make sure that firms use more of domestic

products as input in their production process. Being aware that most of their profits are

submerged by the high pressure put on the importation of raw materials whenever there is a

boom in the economy, the suggestion of the use of local products in production increases the

benefit of high level of export that is due the economy.

Mexico

The empirical evidence in this thesis shows that the level of trade of a country determines their

exposure to exchange rate risks; policymakers are advised to be mindful of the policies made

on the exchange rate. Devaluation of the Mexican Peso sends a negative signal to investors in

the market, which causes capital flight. Since the major trading partner of the country is the

US, entering into a trade agreement with countries with a lesser value of currency than the US

Dollar could help reduce this type of risk. The uni-directional link between the stock market

and exchange rate shows that growth and development in the stock market also attract foreign

investments; this means that whenever policies that aid growth and development of the stock

market are implemented, there is a possibility of an increasing level of foreign participation in

the market. The negative interaction of Federal funds rate suggests that there is a transmission

channel through which the performance of stocks in Mexico influences Federal funds rate; this

could be that since changes in Federal funds rate is a determinant of capital movement in the

economy, foreign investors who prefer an increase in their rate of return will move their

investment from US to Mexico, even though there is an understanding of how risky the market

is in comparison with that of the US. Investors who are risk takers prefer a higher risk, higher

return market than one with a stable environment with minimal risk. The positive interaction

between stock returns and MSCI world equity shows that an increase in the equity

representation of Mexico should be supported since it helps boost the co-movement of Peso

with the basket of other currencies MSCI currencies.

Indonesia

The variance decomposition result arrived at for stock return index in Indonesia shows that

exchange rate is the most important variable that explains movement in stock returns. The result

proposes that an appreciation in domestic currency increases returns in the stock market. The

granger causality result confirms causal relationship between industrial production, interest

rate and MSCI world equity index. The uni – directional causality runs from these variables to

stock returns. There are many factors that affect the domestic economy, inflation, political

instability, terrorism but to mention a few, also policies made by various governments

contributes to the betterment of the economy. The result in this thesis, suggests that when

policies are made to boost the economy, there is a tendency of an increased participation in the

stock market. A stable economy attracts foreign investments to the market as emerging markets

are known for a higher return when compared to developed ones, the economic environment is

one of the factors investors put into consideration whenever they make investment decisions.

Interest rate is also an important domestic macroeconomic variable that impacts the stock

market; the impact could be direct and indirect. The impact could be direct as higher interest

rate is perceived to be bad news to companies with high debt burdens. These companies pay

more for loans and the increase eats into their profitability margin. This translates into reduced

dividend for shareholders. the result suggest that shareholders are to be mindful of gearing ratio

of the companies in which they invest.

The government should try to stabilise interest rate and not make it the ready tool to manoeuvre

the economy since it is one of the factors considered by investors. Policies that aid the

development of other external sources of finance for firms in the country increase the chances

of eradicating the significant changes that might occur when interest rate fluctuates. If this is

in place, policymakers could increase the interest rate to attract foreign investors and at the

same time, availability of alternative sources of finance would enhance the participation of

domestic investors in the market.

An increased representation of Indonesia stocks in the All country world index will give a boost

to the stock market.

The result also indicates that changes in the commodity price index have a positive interaction

with Jakarta Composite Index. In this era of lower commodity prices, especially oil, there is an

expectation of a significant fall in the returns of stock in Indonesia. The stock market also

shows a short-term reaction to changes in Federal funds rate; this shows that investors’

response to the global news is instant after which the stock market gains its stability. This is

evident in the FDI data observed in Chapter 3 which shows that a regional crisis influences

capital flight (1998–2000) rather than the GFC (2008/2009); also as major export partners are

in the Asia region; it is likely that global news only influences the stock market for a short

while. The outcome of this thesis shows that the stock market has a form of a hedge against

some domestic and global risk factors which helps diminishes the impact of these factors on

the stock market.

Nigeria

The variance decomposition result derived for Nigeria shows exchange rate, commodity price

index and oil prices as the principal variables that determine movement in the stock market;

the impulse response function confirms commodity price index as the only variable that have

its causal relationship running to stock returns in Nigeria; although most export-oriented

economies are vulnerable to external shocks, the magnitude and size, however, depends on

shipping and trading partners. The concentration of the Nigerian economy on trading portfolio

explains the large significant impact of the global risk factors like commodity price index and

Federal funds rate.

Nigeria as a country structures most of its production towards export-led growth, thus exposing

the economy to global risk factors. The Granger causality results clearly identified two

variables: commodity price index and Federal funds rate. The outcome shows that economic

openness of the country is the main reason behind the fluctuation in the stock market. This

means that commodity price fluctuation determines the participation of both domestic and

foreign investment in the market. The uni-directional link between Federal funds rate and stock

market shows that contractionary monetary policies implemented in the US transmit directly

and indirectly to the stock market. This transmission is possible through the exchange rate; the

country’s trade is mostly in US Dollars, hence the significance.

Investors in Nigeria should focus more on studying the impact of external macroeconomic

shocks rather than internal macroeconomic shocks; policymakers should also try to diversify

the economy by shifting the reliance of the economy from commodities such as oil. If need be,

they can also structure production towards domestic demands and make the economy more

open to countries in the region.

Turkey

The result in the thesis shows that domestic factors rather than global factors are determinants

of movement in stock prices in Turkey. The outcome shows that exchange rate is one of the

significant determinants of changes in stock returns; this is because countries cannot do without

trade. The significance could be attributed to the fact that the country has the US as one of their

major trade partners. The study suggests that interest rate accounts for almost 15% of the

variation in the stock returns. The result in this study shows that interest rate is the leading

macroeconomic variable that impacts stock returns.

The Granger causality shows causality running from interest rate to stock return and vice –

versa, the same result for exchange rate. The result also shows a causality running from stock

returns to Federal funds rate. Based on the result in this thesis, investors are to be on the lookout

for exchange rate fluctuations in making decisions; also, policymakers are to include

monitoring of exchange rate movement in their economic surveillance; secondly, whenever

interest rate is used as a tool to reduce money in circulation, there is a decrease in return on

stocks which works contrary to increase in foreign investment participation in the stock market.

7.2.3 Conclusion

The main findings of this research can be concluded as follows: firstly, the evidence in the

thesis, through the use of a multifactor model, reveals MINT countries have significant

differences in magnitude and association with each of the domestic and global macroeconomic

variables selected. Secondly, the result for Mexico, and Turkey depicts internal variables as the

most important determinants of changes in the stock market. Indonesia exhibits both global

and national factors as major determinants in the movement in stock returns, while Nigeria

shows global factors as the major determinants of changes in the stock market returns. The

valid responses of the MINT equity markets to macroeconomic factors do not only show

important differences in the economies of these countries but are also a sign of differing

responses of the various stock markets to exogenous shocks from global factors which include

cyclical behaviour.

In all, the findings help to contribute to making important portfolio and investment decisions.

The outcome substantiates that stock market movement does not only depend on changes in

state variables but also in global variables; which means whenever a study of this sort is carried

out, authors shouldn’t restrict variable selection to the local economy but should consider

including world factors. These would also serve as pointers to investors that they should give

a close watch to what happens in both national and international environments.

7.2.4 Main Contributions

Policymakers want to achieve better growth in the economy through the financial system;

financial experts are also keen on maximising their potentials by minimising and/or identifying

the risk factors they are exposed to. This study examined the interaction between policy

decisions and its impact on investment. This research would be of great benefit to investors

who are interested in diversifying investments across different geographical locations.

To show the importance of the contribution of this study, it is necessary to produce a schematic

representation of how the author came about the gap in the literature review and how the gap

is developed into assumptions.

Figure 20 Contributing to the APT Framework

The main contributions in this study can be summarized in different parts. Firstly, Figure 20

above shows how the study contributes to one key area in the Arbitrage Pricing framework by

identifying factors that can be included as part of systematic risk in emerging markets. The

APT identifies two factors as major determinants of market portfolio, which are systematic and

unsystematic risk factors. Unsystematic risk factors are diversifiable by making sure assets in

a particular portfolio are not exposed to the same type of risk; however, systematic risk factors

can only be minimised but could hardly be diversified.

The empirical literature in this field has focused mostly on the impact of domestic

macroeconomic factors on emerging stock markets. The study makes the first attempt to widen

the focus of financial experts on risk management and portfolio diversification by identifying

global risk factors as stock market return determinants. The study seeks to expand the APT by

suggesting the inclusion of global risk factors such as an important monetary policy decision

tool (Federal funds rate), MSCI Global equity index and global commodity price index in the

number of systemic factors that influence emerging stock market; it also provides a detailed

evidence of how MINT stock markets interact with global and internal macroeconomic factors.

More importantly, the research has contributed immensely by tracing the long and short – runs

through which the variables interact with each other.

Secondly, this study bridges a gap in literature where some researchers are of the opinion that

factors other than selected domestic macroeconomic factors influence the movement in the

stock market. The study begins with cointegration analysis to examine the long-run impact of

the combination of global and domestic variables after which the result determines whether

further tests are needed to be carried out. The use of IRF and VDC to trace and quantify the

Arbitrage Pricing theory

Sytematic risk factors

Domestic macroeconomic factors

(empirical literature)

Global macroeconomic factors

(Contribution)

Unsytematic risk factors

extent to which stock markets react to shocks in macroeconomic variables helps to give an

insight in the classification of the impact of macroeconomic variables on the stock market.

Thirdly, this thesis provides an explanation of the global exposure of emerging markets from a

different viewpoint. The global risk factor, through liberalization, economic openness and

trade, impacts emerging markets. However, the MINT countries show that countries indicate

different reactions to the same set of macroeconomic variables. This shows that results derived

in this type of study cannot be generalized for all emerging markets. Furthermore, a more

precise interaction between the variables is established using Granger causality which shows

the direction of the impact.

Besides, this study contributes to the existing knowledge in the field of finance and economics

by adding new evidence of the inclusion of global factors in determining movement of stock

returns in emerging markets. Practitioners and academics use the multifactor model to

investigate the impact of domestic macroeconomic factors on the stock market, this research

shows that the reaction of emerging stock market to a global crisis can be explained by their

exposure to global risk factors.

7.3 Limitations of the Research

It is worth noting that there are some limitations to the findings in this study. The use of

monthly data for the analysis causes limitation because stock markets vary on a daily basis.

The use of data on a monthly basis may contribute to insignificant outcomes. Collecting daily

data would have yielded a more accurate result as it will capture the daily variations of changes

in stock returns. However, since macroeconomic variables do not move on a regular basis as

compared to the stock returns, it is almost impossible to retrieve economic data in daily

frequency from most archives.

Furthermore, firms that are listed in the stock exchange markets have varying structures. This

means that oil firms are based on changes in oil price movements whereas financial institutions

such as banks have structures based on interest rate; therefore, several different factors may

contribute significantly to explaining the share returns of different firms. This means that this

type of research, when applied to individual listed firms or industries, would likely show a

more accurate result than using the stock return index that represents the whole market.

Additionally, there is the issue of unavailability of up-to-date industrial production index data

in Nigeria – although the industrial production index data recorded in the Nigeria Bureau of

Statistics shows data from 1990 to 2005, there has been no update on the data since then. The

inclusion of Nigeria in the MINT is a welcome development; however, when data is not

available for research purposes, it may restrict the level of investigation in the country. The

inclusion of oil production index may not give the model a sound basis for comparison with

other nations in the newly formulated group of countries.

7.4 Further Research

This research builds on the research of Abugri (2008) where he used a single index model

that relates to the world market portfolio to quantify stock market risk, the MSCI returns

monthly. The research findings show that developing markets are sensitive to state world

markets. The study suggests a future research on how markets respond to forces that

influences the world economy such as stance of monetary policy of developed economies and

commodity prices. This thesis has included the suggested variables in the model estimated.

however, the thesis opened a number of promising ideas that can be introduced in this area of

study, these can be summarized as follows;

To start with, due to the differences in major export partners of emerging economies, there is

room to include the exchange rate of currency other than the US dollars. For example, for a

study on countries like Turkey, the exchange rate of Turkish Lira per Euro could be used.

Besides, there is room to narrow the field to firms and industries listed on the stock exchange

market. In addition, the econometric method used in this thesis could be applied to examine

other variables; this means that more variables could be added in the models, especially

macroeconomic variables of countries that are export partners.

Finally, research of this type can be carried out using other econometric methods, such as panel

data analysis, and the findings can be compared with the outcome in this study.

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Appendices

(i) Descriptive Statistics (MINT)

RIPC MSCI LIP LER LCP IR FFR

Mean 0.0149 6.9672 4.5167 2.2393 4.4969 11.3644 2.9230

Median 0.0160 7.0248 4.5332 2.3630 4.2576 5.7700 3.0200

Maximum 0.1931 7.4666 4.7620 2.6838 5.3931 70.2600 6.5400

Minimum -0.2951 6.2102 4.1863 1.1305 3.7369 2.1500 0.0700

Std. Dev. 0.0701 0.2995 0.1455 0.3880 0.5045 11.0962 2.2640

Skewness -0.4628 -0.4931 -0.7040 -1.7661 0.3241 2.0175 -0.0074

Kurtosis 4.6711 2.4281 2.6500 5.4596 1.5259 8.0085 1.3897

Jarque-Bera 40.1458 14.2958 23.1583 203.80 28.523 455.048 28.525

Probability 0.0000 0.0007 0.0000 0.0000 0.0000 0.0000 0.0000

Sum 3.9375 1839.34 1192.43 591.20 1187.19 3000.22 771.68

Sum Sq. Dev. 1.2939 23.600 5.5685 39.602 66.946 32382.1 1348.1

Observations 264 264 264 264 264 264 264

RJCI MSCI LIP LER LCP IR FFR

Mean 0.0144 6.9672 4.4665 8.8310 4.4969 12.5249 2.9230

Median 0.0183 7.0248 4.4529 9.1103 4.2576 9.5950 3.0200

Maximum 0.2842 7.4666 4.8483 9.5441 5.3931 70.8100 6.5400

Minimum -0.3151 6.2102 4.0452 7.6327 3.7369 5.7500 0.0700

Std. Dev. 0.0797 0.2995 0.1625 0.6009 0.5045 10.1429 2.2640

Skewness -0.4991 -0.4931 0.1543 -1.2273 0.3241 3.7650 -0.0074

Kurtosis 5.6373 2.4281 2.5603 2.7236 1.5259 18.8327 1.3897

Jarque-Bera 87.473 14.295 3.1743 67.118 28.523 3381.173 28.525

Probability 0.0000 0.0007 0.2045 0.0000 0.0000 0.0000 0.0000

Sum 3.8034 1839.34 1179.17 2331.38 1187.19 3306.59 771.68

Sum Sq. Dev. 1.6718 23.600 6.9457 94.971 66.946 27057.45 1348.12

Observations 264 264 264 264 264 264 264

Nigeria RNASI LOP MSCI LER LCP IR FFR

Mean 0.0162 7.7173 6.96721 4.7504 4.496955 13.6439 2.923030

Median 0.0093 7.7097 7.024857 4.9020 4.257658 13.5000 3.020000

Maximum 0.3827 7.8991 7.466621 5.2388 5.393171 26.0000 6.540000

Minimum -0.3064 7.3426 6.210265 3.2132 3.736993 6.0000 0.070000

Std. Dev. 0.0792 0.0919 0.299562 0.3820 0.504531 4.3011 2.264056

Skewness 0.2673 -0.1316 -0.493108 -1.5963 0.324110 0.6115 -0.007441

Kurtosis 6.8375 2.9074 2.428158 5.5417 1.525933 3.8271 1.389710

Jarque-Bera 165.143 0.8572 14.29588 183.195 28.52369 23.982 28.52581

Probability 0.0000 0.6513 0.000786 0.0000 0.000001 0.0000 0.000001

Sum 4.27 2037.37 1839.34 1254.10 1187.196 3602.00 771.6800

Sum Sq. Dev. 1.6522 2.2229 23.60090 38.3888 66.94695 4865.40 1348.124

Observations 264 264 264 264 264 264 264

RXU100 MSCI LIP LER LCP IR FFR

Mean 0.0382 6.967215 4.3708 -0.5649 4.496955 42.0281 2.923030

Median 0.0276 7.024857 4.3496 0.2945 4.257658 25.9850 3.020000

Maximum 0.7978 7.466621 4.8589 0.8306 5.393171 435.9900 6.540000

Minimum -0.3903 6.210265 3.8670 -4.7444 3.736993 1.5000 0.070000

Std. Dev. 0.1419 0.299562 0.2612 1.5338 0.504531 48.8131 2.264056

Skewness 1.1858 -0.49310 0.0764 -1.2920 0.324110 4.0676 -0.007441

Kurtosis 7.4803 2.4281 1.7624 3.3804 1.525933 28.5772 1.389710

Jarque-Bera 282.68 14.295 17.103 75.043 28.52369 7924.19 28.52581

Probability 0.0000 0.0007 0.0001 0.0000 0.000001 0.0000 0.000001

Sum 10.091 1839.34 1153.90 -149.15 1187.196 11095.44 771.6800

Sum Sq. Dev. 5.2968 23.6009 17.9467 618.73 66.94695 626657.3 1348.124

Observations 264 264 264 264 264 264 264

(ii)Variance Inflation factor

Variance Inflation Factors

Date: 09/23/17 Time: 08:50

Sample: 1993M01 2014M12

Included observations: 264 Coefficient Uncentered Centered

Variable Variance VIF VIF C 0.163728 9317.177 NA

LIP 0.021537 2502.28 9.85094

LER 0.000527 154.9409 4.500094

IR 6.58E-07 9.435420 4.596090

LCP 0.000236 274.5399 3.400034

FFR 1.38E-05 10.70026 4.002833

MSCI 0.002496 6908.683 9.69996

Date: 09/23/17 Time: 08:51

Sample: 1993M01 2014M12

Included observations: 264

Coefficient Uncentered Centered

Variable Variance VIF VIF MSCI 0.001797 3680.158 6.765091

LIP 0.004627 3893.111 5.127304

LER 0.000336 1109.052 5.092395

LCP 0.000590 508.7943 6.301152

IR 4.11E-07 4.488660 1.773733

FFR 1.87E-05 10.76181 4.025859

C 0.060121 2532.418 NA

Date: 09/23/17 Time: 08:52

Sample: 1993M01 2014M12

Variable Variance VIF VIF LOP 0.006525 16613.93 2.348573

MSCI 0.001082 2249.618 4.135384

LER 0.000610 592.3860 3.792745

LCP 0.000533 466.4997 5.777355

IR 3.11E-06 27.16949 2.447481

FFR 1.36E-05 7.948356 2.973380

C 0.298177 12746.22 NA

Date: 09/23/17 Time: 08:53

Sample: 1993M01 2014M12

Variable Variance VIF VIF MSCI 0.005767 4002.679 7.357968

LIP 0.021038 5757.110 7.41309

LER 0.000137 5.205414 4.581408

LCP 0.002604 760.9714 9.424234

IR 5.41E-08 3.198551 1.833884

FFR 5.02E-05 9.773121 3.656001

C 0.161665 2307.441 NA

(iii) Break point test

Multiple breakpoint tests

Bai-Perron tests of L+1 vs. L sequentially determined breaks

Date: 09/23/17 Time: 09:13

Sample: 1993M01 2014M12

Breaking variables: MSCI LIP LER LCP IR FFR C

Break test options: Trimming 0.15, Max. breaks 5, Sig. level 0.05

Allow heterogeneous error distributions across breaks Sequential F-statistic determined breaks: 1

Scaled Critical

Break Test F-statistic F-statistic Value** 0 vs. 1 * 6.865421 48.05795 21.87

1 vs. 2 2.952051 18.66436 24.17 * Significant at the 0.05 level.

** Bai-Perron (Econometric Journal, 2003) critical values.

Break dates:

Sequential Repartition

1 1995M02 1995M02

Date: 09/23/17 Time: 09:10

Sample: 1993M01 2014M12

Allow heterogeneous error distributions across breaks Sequential F-statistic determined breaks: 1 Scaled Critical

Break Test F-statistic F-statistic Value** 0 vs. 1 * 4.325168 30.27618 21.87

Break dates:

1 1998M09 1998M09

Date: 09/23/17 Time: 09:15

Sample: 1993M01 2014M12

Breaking variables: LOP MSCI LER LCP IR FFR C

Break Test F-statistic F-statistic Value** 0 vs. 1 3.032510 21.22757 21.87

Break dates:

1 2008M02 2008M02

Date: 09/23/17 Time: 09:16

Sample: 1993M01 2014M12

Break Test F-statistic F-statistic Value** 0 vs. 1 2.539324 17.77526 21.87

Break dates:

1 2001M02 2001M02

(iv) Random Walk Result

Null Hypothesis: RIPC is a martingale

Date: 09/17/17 Time: 22:29

Sample: 1993M01 2014M12

Included observations: 263 (after adjustments)

Heteroskedasticity robust standard error estimates

User-specified lags: 2 4 8 16 Joint Tests Value df Probability

Max |z| (at period 2)* 5.199807 263 0.0000

Individual Tests

Period Var. Ratio Std. Error z-Statistic Probability

2 0.516289 0.093025 -5.199807 0.0000

4 0.252447 0.161777 -4.620889 0.0000

8 0.127954 0.233775 -3.730276 0.0002

16 0.068984 0.330620 -2.815974 0.0049 *Probability approximation using studentized maximum modulus with

parameter value 4 and infinite degrees of freedom

Test Details (Mean = -0.000100818324075) Period Variance Var. Ratio Obs.

1 0.00988 -- 263

2 0.00510 0.51629 262

4 0.00249 0.25245 260

8 0.00126 0.12795 256

16 0.00068 0.06898 248

Null Hypothesis: RJCI is a martingale

Date: 09/17/17 Time: 22:30

Sample: 1993M01 2014M12

Max |z| (at period 4)* 4.522356 263 0.0000

Individual Tests

2 0.651577 0.085716 -4.064857 0.0000

4 0.295898 0.155694 -4.522356 0.0000

8 0.160724 0.239404 -3.505689 0.0005

Test Details (Mean = 5.68877741986e-05) Period Variance Var. Ratio Obs.

1 0.01069 -- 263

2 0.00697 0.65158 262

4 0.00316 0.29590 260

8 0.00172 0.16072 256

16 0.00092 0.08593 248

Null Hypothesis: RNASI is a martingale

Date: 09/17/17 Time: 22:31

Sample: 1993M01 2014M12

Max |z| (at period 2)* 4.510823 263 0.0000

Individual Tests

2 0.422140 0.128105 -4.510823 0.0000

4 0.212981 0.212662 -3.700793 0.0002

8 0.109304 0.316975 -2.809983 0.0050

Test Details (Mean = 1.25594093221e-05) Period Variance Var. Ratio Obs.

1 0.01265 -- 263

2 0.00534 0.42214 262

4 0.00269 0.21298 260

8 0.00138 0.10930 256

16 0.00089 0.07003 248

Null Hypothesis: RXU100 is a martingale

Date: 09/17/17 Time: 22:31

Sample: 1993M01 2014M12

Max |z| (at period 2)* 5.574745 263 0.0000

Individual Tests

2 0.482814 0.092773 -5.574745 0.0000

4 0.253645 0.154677 -4.825232 0.0000

8 0.129233 0.227701 -3.824174 0.0001

Test Details (Mean = -1.97508698968e-05) Period Variance Var. Ratio Obs.

1 0.04029 -- 263

2 0.01945 0.48281 262

4 0.01022 0.25365 260

8 0.00521 0.12923 256

16 0.00281 0.06970 248

(v)Granger Causality Result

VEC Granger Causality/Block Exogeneity Wald Tests

Date: 10/27/17 Time: 11:36

Sample: 1993M01 2014M12

Dependent variable: D(RIPC) Excluded Chi-sq df Prob. D(MSCI) 0.949665 2 0.6220

D(LIP) 3.475556 2 0.1759

D(LER) 10.32184 2 0.0057

D(LCP) 1.136804 2 0.5664

D(IR) 0.328974 2 0.8483

D(FFR) 1.203075 2 0.5480

D(DUM01) 7.676872 2 0.0215 All 25.07974 14 0.0338

Dependent variable: D(MSCI) Excluded Chi-sq df Prob. D(RIPC) 3.844232 2 0.1463

D(LIP) 0.870569 2 0.6471

D(LER) 7.023040 2 0.0299

D(LCP) 1.624517 2 0.4439

D(IR) 0.276460 2 0.8709

D(FFR) 2.466104 2 0.2914

D(DUM01) 5.256779 2 0.0722 All 24.44741 14 0.0404

Dependent variable: D(LIP) Excluded Chi-sq df Prob. D(RIPC) 1.289489 2 0.5248

D(MSCI) 0.512254 2 0.7740

D(LER) 1.701754 2 0.4270

D(LCP) 2.951397 2 0.2286

D(IR) 3.133350 2 0.2087

D(FFR) 5.931322 2 0.0515

D(DUM01) 0.936062 2 0.6262 All 34.16293 14 0.0020

Dependent variable: D(LER) Excluded Chi-sq df Prob. D(RIPC) 2.783844 2 0.2486

D(MSCI) 3.175508 2 0.2044

D(LIP) 6.986959 2 0.0304

D(LCP) 2.828258 2 0.2431

D(IR) 18.75938 2 0.0001

D(FFR) 10.35723 2 0.0056

D(DUM01) 0.122419 2 0.9406 All 39.49576 14 0.0003

Dependent variable: D(LCP) Excluded Chi-sq df Prob. D(RIPC) 0.456414 2 0.7960

D(MSCI) 15.91358 2 0.0004

D(LIP) 0.999311 2 0.6067

D(LER) 0.695485 2 0.7063

D(IR) 0.782236 2 0.6763

D(FFR) 2.711246 2 0.2578

D(DUM01) 0.464426 2 0.7928 All 24.31022 14 0.0420

Dependent variable: D(IR) Excluded Chi-sq df Prob. D(RIPC) 10.97414 2 0.0041

D(MSCI) 18.57107 2 0.0001

D(LIP) 6.363950 2 0.0415

D(LER) 18.33838 2 0.0001

D(LCP) 1.655403 2 0.4371

D(FFR) 2.732581 2 0.2551

D(DUM01) 0.248682 2 0.8831 All 58.36299 14 0.0000

Dependent variable: D(FFR) Excluded Chi-sq df Prob. D(RIPC) 8.969458 2 0.0113

D(MSCI) 10.18446 2 0.0061

D(LIP) 0.037335 2 0.9815

D(LER) 5.450095 2 0.0655

D(LCP) 0.241060 2 0.8865

D(IR) 2.153860 2 0.3406

D(DUM01) 11.65556 2 0.0029 All 44.87466 14 0.0000

Dependent variable: D(DUM01) Excluded Chi-sq df Prob. D(RIPC) 4.411517 2 0.1102

D(MSCI) 2.786266 2 0.2483

D(LIP) 2.950735 2 0.2287

D(LER) 1.553125 2 0.4600

D(LCP) 2.276233 2 0.3204

D(IR) 0.258658 2 0.8787

D(FFR) 14.17037 2 0.0008 All 23.78795 14 0.0486

Date: 10/27/17 Time: 11:38

Sample: 1993M01 2014M12

Dependent variable: D(RJCI) Excluded Chi-sq df Prob. D(MSCI) 4.781795 2 0.0915

D(LIP) 6.381713 2 0.0411

D(LER) 3.108247 2 0.2114

D(LCP) 1.398207 2 0.4970

D(IR) 18.58824 2 0.0001

D(FFR) 0.540704 2 0.7631

D(DUM98) 3.710627 2 0.1564 All 37.65336 14 0.0006

Dependent variable: D(MSCI) Excluded Chi-sq df Prob. D(RJCI) 3.461857 2 0.1771

D(LIP) 1.574059 2 0.4552

D(LER) 4.139130 2 0.1262

D(LCP) 2.793750 2 0.2474

D(IR) 5.904652 2 0.0522

D(FFR) 4.359409 2 0.1131

D(DUM98) 1.006690 2 0.6045 All 27.40924 14 0.0170

Dependent variable: D(LIP) Excluded Chi-sq df Prob. D(RJCI) 2.860903 2 0.2392

D(MSCI) 2.270424 2 0.3214

D(LER) 7.362393 2 0.0252

D(LCP) 3.535143 2 0.1707

D(IR) 0.568743 2 0.7525

D(FFR) 2.306476 2 0.3156

D(DUM98) 18.26145 2 0.0001 All 49.98524 14 0.0000

Dependent variable: D(LER) Excluded Chi-sq df Prob. D(RJCI) 3.654482 2 0.1609

D(MSCI) 2.618729 2 0.2700

D(LIP) 0.460856 2 0.7942

D(LCP) 1.359854 2 0.5067

D(IR) 6.765568 2 0.0340

D(FFR) 0.182383 2 0.9128

D(DUM98) 6.670016 2 0.0356 All 18.26631 14 0.1949

Dependent variable: D(LCP) Excluded Chi-sq df Prob. D(RJCI) 2.796502 2 0.2470

D(MSCI) 18.07948 2 0.0001

D(LIP) 3.952042 2 0.1386

D(LER) 1.569847 2 0.4562

D(IR) 1.118316 2 0.5717

D(FFR) 2.667167 2 0.2635

D(DUM98) 1.896585 2 0.3874 All 36.83496 14 0.0008

Dependent variable: D(IR) Excluded Chi-sq df Prob. D(RJCI) 3.240006 2 0.1979

D(MSCI) 10.19507 2 0.0061

D(LIP) 5.034144 2 0.0807

D(LER) 27.63052 2 0.0000

D(LCP) 1.206569 2 0.5470

D(FFR) 0.259049 2 0.8785

D(DUM98) 15.13124 2 0.0005 All 75.89247 14 0.0000

Dependent variable: D(FFR) Excluded Chi-sq df Prob. D(RJCI) 1.781662 2 0.4103

D(MSCI) 3.124221 2 0.2097

D(LIP) 5.553612 2 0.0622

D(LER) 1.327034 2 0.5150

D(LCP) 0.127649 2 0.9382

D(IR) 0.368123 2 0.8319

D(DUM98) 0.870351 2 0.6472 All 13.93777 14 0.4544

Dependent variable: D(DUM98) Excluded Chi-sq df Prob. D(RJCI) 113.8019 2 0.0000

D(MSCI) 17.62454 2 0.0001

D(LIP) 1.113313 2 0.5731

D(LER) 74.16881 2 0.0000

D(LCP) 4.008534 2 0.1348

D(IR) 67.45496 2 0.0000

D(FFR) 5.414828 2 0.0667 All 230.0389 14 0.0000

Date: 10/27/17 Time: 11:41

Sample: 1993M01 2014M12

Dependent variable: D(RNASI)

Excluded Chi-sq df Prob. D(MSCI) 2.109161 2 0.3483

D(LOP) 1.236415 2 0.5389

D(LER) 1.034749 2 0.5961

D(LCP) 8.742744 2 0.0126

D(IR) 2.184170 2 0.3355

D(FFR) 0.957311 2 0.6196

D(DUM97) 0.207399 2 0.9015 All 17.48381 14 0.2313

Dependent variable: D(MSCI) Excluded Chi-sq df Prob. D(RNASI) 2.571282 2 0.2765

D(LOP) 0.733933 2 0.6928

D(LER) 0.699056 2 0.7050

D(LCP) 1.083669 2 0.5817

D(IR) 2.559385 2 0.2781

D(FFR) 5.767562 2 0.0559

D(DUM97) 1.806829 2 0.4052 All 15.94636 14 0.3167

Dependent variable: D(LOP) Excluded Chi-sq df Prob. D(RNASI) 0.359552 2 0.8355

D(MSCI) 1.808092 2 0.4049

D(LER) 0.055833 2 0.9725

D(LCP) 1.199039 2 0.5491

D(IR) 0.688876 2 0.7086

D(FFR) 1.173732 2 0.5561

D(DUM97) 0.689038 2 0.7086 All 5.660247 14 0.9743

Dependent variable: D(LER) Excluded Chi-sq df Prob. D(RNASI) 4.268501 2 0.1183

D(MSCI) 1.769063 2 0.4129

D(LOP) 1.504954 2 0.4712

D(LCP) 8.086833 2 0.0175

D(IR) 6.906703 2 0.0316

D(FFR) 1.632425 2 0.4421

D(DUM97) 1.394076 2 0.4981 All 30.94517 14 0.0056

Dependent variable: D(LCP)

Excluded Chi-sq df Prob. D(RNASI) 2.044798 2 0.3597

D(MSCI) 22.21823 2 0.0000

D(LOP) 0.871281 2 0.6469

D(LER) 0.713804 2 0.6998

D(IR) 0.294855 2 0.8629

D(FFR) 4.526853 2 0.1040

D(DUM97) 3.531171 2 0.1711 All 33.38231 14 0.0025

Dependent variable: D(IR) Excluded Chi-sq df Prob. D(RNASI) 3.735374 2 0.1545

D(MSCI) 1.503528 2 0.4715

D(LOP) 2.833483 2 0.2425

D(LER) 1.981476 2 0.3713

D(LCP) 0.518308 2 0.7717

D(FFR) 4.694479 2 0.0956

D(DUM97) 0.114188 2 0.9445 All 14.17049 14 0.4371

Dependent variable: D(FFR) Excluded Chi-sq df Prob. D(RNASI) 5.511469 2 0.0636

D(MSCI) 11.13231 2 0.0038

D(LOP) 0.163844 2 0.9213

D(LER) 1.980146 2 0.3715

D(LCP) 0.135600 2 0.9344

D(IR) 1.229835 2 0.5407

D(DUM97) 0.728649 2 0.6947 All 19.90227 14 0.1333

Dependent variable: D(DUM97) Excluded Chi-sq df Prob. D(RNASI) 0.864307 2 0.6491

D(MSCI) 0.894063 2 0.6395

D(LOP) 1.405220 2 0.4953

D(LER) 0.115320 2 0.9440

D(LCP) 1.104657 2 0.5756

D(IR) 0.020303 2 0.9899

D(FFR) 1.946171 2 0.3779 All 5.306388 14 0.9811

Date: 10/27/17 Time: 11:43

Sample: 1993M01 2014M12

Dependent variable: D(RXU100) Excluded Chi-sq df Prob. D(MSCI) 2.631172 2 0.2683

D(LIP) 0.406887 2 0.8159

D(LER) 17.28530 2 0.0002

D(LCP) 3.263544 2 0.1956

D(IR) 11.27863 2 0.0036

D(FFR) 0.823899 2 0.6624

D(DUM00) 1.102452 2 0.5762 All 37.98847 14 0.0005

Dependent variable: D(MSCI) Excluded Chi-sq df Prob. D(RXU100) 0.968588 2 0.6161

D(LIP) 3.396892 2 0.1830

D(LER) 0.185130 2 0.9116

D(LCP) 0.909041 2 0.6348

D(IR) 2.110149 2 0.3482

D(FFR) 3.121641 2 0.2100

D(DUM00) 2.677478 2 0.2622 All 17.69694 14 0.2209

Dependent variable: D(LIP) Excluded Chi-sq df Prob. D(RXU100) 4.181868 2 0.1236

D(MSCI) 2.713451 2 0.2575

D(LER) 8.145022 2 0.0170

D(LCP) 5.267710 2 0.0718

D(IR) 17.61239 2 0.0001

D(FFR) 6.976854 2 0.0305

D(DUM00) 8.428749 2 0.0148 All 66.79071 14 0.0000

Dependent variable: D(LER) Excluded Chi-sq df Prob. D(RXU100) 9.339682 2 0.0094

D(MSCI) 9.623477 2 0.0081

D(LIP) 2.278745 2 0.3200

D(LCP) 0.029710 2 0.9853

D(IR) 57.20322 2 0.0000

D(FFR) 6.315853 2 0.0425

D(DUM00) 0.031212 2 0.9845 All 106.8580 14 0.0000

Dependent variable: D(LCP) Excluded Chi-sq df Prob. D(RXU100) 1.409571 2 0.4942

D(MSCI) 18.82860 2 0.0001

D(LIP) 0.398734 2 0.8192

D(LER) 0.016662 2 0.9917

D(IR) 0.583863 2 0.7468

D(FFR) 3.750284 2 0.1533

D(DUM00) 4.682326 2 0.0962 All 31.19747 14 0.0052

Dependent variable: D(IR) Excluded Chi-sq df Prob. D(RXU100) 11.21580 2 0.0037

D(MSCI) 5.109689 2 0.0777

D(LIP) 2.967128 2 0.2268

D(LER) 15.10992 2 0.0005

D(LCP) 7.705670 2 0.0212

D(FFR) 0.723286 2 0.6965

D(DUM00) 1.370136 2 0.5041 All 45.26025 14 0.0000

Dependent variable: D(FFR) Excluded Chi-sq df Prob. D(RXU100) 7.256604 2 0.0266

D(MSCI) 8.715978 2 0.0128

D(LIP) 0.969745 2 0.6158

D(LER) 3.535911 2 0.1707

D(LCP) 0.012034 2 0.9940

D(IR) 3.025939 2 0.2203

D(DUM00) 0.134384 2 0.9350 All 30.43014 14 0.0067

Dependent variable: D(DUM00) Excluded Chi-sq df Prob. D(RXU100) 33.19391 2 0.0000

D(MSCI) 3.776268 2 0.1514

D(LIP) 1.268734 2 0.5303

D(LER) 0.053981 2 0.9734

D(LCP) 0.145561 2 0.9298

D(IR) 6.882096 2 0.0320

D(FFR) 2.513164 2 0.2846 All 46.44767 14 0.0000

(vi) ARDL Bounds Result

ARDL Bounds Test

Date: 09/23/17 Time: 10:19

Sample: 1993M03 2014M12

Null Hypothesis: No long-run relationships exist Test Statistic Value k F-statistic 18.24567 7

Critical Value Bounds Significance I0 Bound I1 Bound 10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.9

Test Equation:

Dependent Variable: D(RIPC)

Method: Least Squares

Date: 09/23/17 Time: 10:19

Sample: 1993M03 2014M12

ARDL Bounds Test

Date: 09/23/17 Time: 10:20

Sample: 1993M04 2014M12

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.9

ARDL Bounds Test

Date: 09/23/17 Time: 10:21

Sample: 1993M05 2014M12

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.9

ARDL Bounds Test

Date: 09/23/17 Time: 10:22

Sample: 1993M04 2014M12

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.9

(vii) Unit Root Result

(viii)Variance Decomposition Result

Variance Decomposi

tion of RIPC:

Period S.E. RIPC MSCI LIP LER LCP IR FFR DUM01

1 0.068895 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.070861 94.56235 0.166236 0.071812 4.836553 0.032899 0.067080 0.171317 0.091753

3 0.072035 91.58295 0.187823 0.862539 4.810765 0.103352 0.340350 0.176205 1.936011

4 0.072246 91.28320 0.203115 1.053995 4.794639 0.118295 0.350678 0.187068 2.009013

5 0.072420 90.85541 0.326687 1.169623 4.772438 0.180206 0.429318 0.252266 2.014048

6 0.072481 90.70156 0.331217 1.246300 4.765608 0.206325 0.480250 0.256320 2.012423

7 0.072578 90.58517 0.337321 1.324216 4.754320 0.229254 0.505806 0.255882 2.008035

8 0.072675 90.43407 0.354969 1.410416 4.748408 0.255663 0.531107 0.255448 2.009922

9 0.072752 90.28870 0.371544 1.503092 4.740794 0.284036 0.548541 0.255327 2.007972

10 0.072830 90.14446 0.384397 1.596938 4.731984 0.308812 0.568075 0.257022 2.008317

11 0.072909 89.99501 0.399406 1.687204 4.723200 0.333671 0.591474 0.259131 2.010905

12 0.072988 89.84522 0.413633 1.775766 4.715194 0.358599 0.614801 0.262391 2.014394

13 0.073066 89.69792 0.426726 1.863529 4.707145 0.383062 0.636351 0.267187 2.018080

14 0.073145 89.54950 0.439693 1.951205 4.699220 0.407170 0.657257 0.273450 2.022502

15 0.073224 89.39982 0.452721 2.038645 4.691472 0.431165 0.677949 0.280770 2.027464

16 0.073302 89.24958 0.465491 2.125692 4.683787 0.454944 0.698482 0.289100 2.032923

17 0.073381 89.09902 0.478062 2.212179 4.676150 0.478505 0.718897 0.298321 2.038864

18 0.073460 88.94820 0.490487 2.298150 4.668596 0.501896 0.739152 0.308327 2.045191

19 0.073539 88.79730 0.502762 2.383677 4.661111 0.525127 0.759192 0.319026 2.051800

20 0.073617 88.64639 0.514906 2.468799 4.653682 0.548200 0.779043 0.330328 2.058654

Period S.E. RJCI MSCI LIP LER LCP IR FFR 1 0.080821 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.088800 91.28091 0.451575 0.140759 1.068637 0.553021 4.433551 0.194188

3 0.091108 87.48488 0.907727 1.170532 1.015374 0.744515 5.902224 0.344591

4 0.093640 86.03092 2.157394 1.156065 1.052417 0.765725 5.749606 0.328270

5 0.095643 85.20610 2.379681 1.111260 1.676468 0.736068 5.550271 0.325732

6 0.097115 83.53808 2.559401 1.304980 2.709678 0.713957 5.408676 0.325414

7 0.098798 81.78658 2.773587 1.395209 4.062323 0.691204 5.228237 0.324847

8 0.100498 80.06120 2.878552 1.417639 5.686577 0.677824 5.085877 0.330668

9 0.102127 78.23970 2.922458 1.523561 7.310819 0.670852 4.962435 0.339040

10 0.103773 76.41766 2.938909 1.617645 8.978420 0.661172 4.851718 0.343941

11 0.105416 74.61074 2.946928 1.693679 10.68483 0.653723 4.765415 0.346959

12 0.107048 72.83308 2.947163 1.787638 12.33515 0.648079 4.696711 0.349812

13 0.108678 71.10448 2.935246 1.873604 13.95339 0.642335 4.639011 0.351613

14 0.110305 69.42417 2.918048 1.954291 15.54061 0.637466 4.593078 0.352206

15 0.111925 67.80142 2.898287 2.036697 17.06844 0.633251 4.556158 0.352347

16 0.113536 66.24507 2.874804 2.113762 18.53952 0.629232 4.525415 0.351983

17 0.115134 64.75340 2.849613 2.187420 19.95357 0.625599 4.499830 0.351119

18 0.116717 63.32705 2.823963 2.259152 21.30518 0.622209 4.478187 0.349964

19 0.118286 61.96562 2.797852 2.327038 22.59748 0.618954 4.459506 0.348572

20 0.119837 60.66596 2.771865 2.391933 23.83242 0.615890 4.443256 0.346988

Period S.E. RNASI MSCI LOP LER LCP IR FFR DUM97

1 0.075870 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.077409 97.26501 0.171274 0.156662 0.554883 1.070811 0.203067 0.036091 0.542203

3 0.079251 93.41859 1.456858 0.168564 0.830575 2.424237 0.212622 0.059598 1.428957

4 0.079872 92.25177 1.448941 0.439241 1.030147 2.455165 0.313515 0.191933 1.869287

5 0.080184 91.57918 1.438015 0.556597 1.123519 2.450411 0.476953 0.211723 2.163598

6 0.080502 90.87694 1.429997 0.667645 1.265426 2.431887 0.580112 0.248705 2.499291

7 0.080810 90.18835 1.423721 0.796032 1.418899 2.414332 0.667046 0.277015 2.814607

8 0.081137 89.48248 1.423102 0.935239 1.571232 2.402574 0.749876 0.311927 3.123565

9 0.081455 88.80107 1.427022 1.061337 1.712604 2.395802 0.832717 0.346369 3.423080

10 0.081777 88.12254 1.433571 1.187435 1.850925 2.392565 0.913295 0.383031 3.716636

11 0.082098 87.45277 1.440397 1.313939 1.987532 2.390436 0.990886 0.420140 4.003897

12 0.082420 86.79087 1.447141 1.438530 2.123251 2.389285 1.066218 0.458387 4.286314

13 0.082741 86.13812 1.453916 1.560737 2.257280 2.388603 1.140016 0.497366 4.563964

14 0.083062 85.49391 1.460658 1.681314 2.389493 2.388233 1.212397 0.536832 4.837161

15 0.083382 84.85880 1.467189 1.800127 2.519936 2.387985 1.283398 0.576466 5.106096

16 0.083702 84.23274 1.473499 1.917122 2.648688 2.387794 1.353112 0.616139 5.370906

17 0.084020 83.61569 1.479618 2.032354 2.775706 2.387614 1.421638 0.655705 5.631680

18 0.084338 83.00754 1.485566 2.145868 2.900970 2.387436 1.489039 0.695053 5.888527

19 0.084654 82.40822 1.491356 2.257686 3.024483 2.387251 1.555361 0.734098 6.141543

20 0.084969 81.81759 1.497006 2.367847 3.146260 2.387056 1.620647 0.772781 6.390812

Period S.E. RXU100 MSCI LIP LER LCP IR FFR DUM00 1 0.139762 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.141925 97.16451 0.723979 0.399998 0.108831 0.000992 1.130949 0.185842 0.284900 3 0.145859 92.08858 0.812979 0.398429 3.658659 0.928817 1.391398 0.226671 0.494463 4 0.148639 89.68747 0.847063 0.404320 4.176684 0.993472 3.139891 0.267381 0.483719 5 0.149639 89.43101 0.838671 0.591740 4.266342 0.985213 3.108171 0.274231 0.504623 6 0.151528 87.63540 0.833564 0.578700 4.431666 1.069762 4.678752 0.277855 0.494299 7 0.152855 86.84744 0.893197 0.667892 4.430515 1.057277 5.312076 0.295038 0.496563 8 0.154389 85.56719 0.951952 0.703537 4.462807 1.106388 6.414795 0.304454 0.488881 9 0.155927 84.61039 1.026743 0.743302 4.480396 1.119933 7.214868 0.323294 0.481079 10 0.157456 83.54329 1.096623 0.790999 4.512472 1.145810 8.090751 0.343858 0.476194 11 0.158934 82.61538 1.168449 0.829039 4.559154 1.171536 8.817724 0.369686 0.469036 12 0.160433 81.66044 1.237168 0.868471 4.606060 1.194832 9.571945 0.397912 0.463172

13 0.161884 80.78217 1.304361 0.907564 4.652583 1.216439 10.25137 0.428245 0.457266 14 0.163343 79.90302 1.369345 0.943498 4.697516 1.237421 10.93873 0.458998 0.451471 15 0.164782 79.06577 1.433823 0.979913 4.738311 1.255923 11.58977 0.490473 0.446015 16 0.166215 78.24374 1.496796 1.014446 4.777094 1.274311 12.23134 0.521617 0.440658 17 0.167638 77.45158 1.558821 1.048212 4.813412 1.291350 12.84845 0.552691 0.435482 18 0.169052 76.68053 1.619378 1.080917 4.848196 1.307947 13.44924 0.583308 0.430484 19 0.170455 75.93516 1.678547 1.112593 4.881691 1.323850 14.02903 0.613507 0.425621 20 0.171849 75.21168 1.736150 1.143301 4.914058 1.339225 14.59151 0.643153 0.420921

(ix) Unit Root Result

(x) ARDL Cointegration Result

Dependent Variable: RIPC

Method: ARDL

Date: 09/23/17 Time: 09:49

Sample (adjusted): 1993M04 2014M12

Included observations: 261 after adjustments

Maximum dependent lags: 4 (Automatic selection)

Model selection method: Akaike info criterion (AIC)

Dynamic regressors (4 lags, automatic): MSCI LIP LER LCP IR FFR

Fixed regressors: C

Number of models evalulated: 312500

Selected Model: ARDL(1, 1, 3, 2, 1, 3, 1, 2)

Note: final equation sample is larger than selection sample Variable Coefficient Std. Error t-Statistic Prob.* RIPC(-1) -0.013585 0.057712 -0.235394 0.8141

MSCI 0.884114 0.085003 10.40095 0.0000

MSCI(-1) -0.860406 0.088010 -9.776202 0.0000

LIP 0.337898 0.274877 1.229269 0.2202

LIP(-1) -0.327520 0.329198 -0.994900 0.3208

LIP(-2) -0.675106 0.335823 -2.010306 0.0455

LIP(-3) 0.509888 0.258340 1.973711 0.0496

LER -0.302157 0.133744 -2.259212 0.0248

LER(-1) -0.041057 0.198864 -0.206456 0.8366

LER(-2) 0.368513 0.131295 2.806765 0.0054

LCP -0.142733 0.085011 -1.679003 0.0945

LCP(-1) 0.138408 0.085335 1.621948 0.1061

IR 0.006370 0.002090 3.047024 0.0026

IR(-1) -0.010854 0.003544 -3.062218 0.0024

IR(-2) 0.007851 0.003399 2.309944 0.0217

IR(-3) -0.003342 0.001790 -1.867254 0.0631

FFR -0.030096 0.020746 -1.450699 0.1482

FFR(-1) 0.028611 0.020694 1.382570 0.1681

DUM02 0.157301 0.056601 2.779115 0.0059

C 0.516295 0.372678 1.385363 0.1672 R-squared 0.480119 Mean dependent var 0.014776

Adjusted R-squared 0.434439 S.D. dependent var 0.069901

S.E. of regression 0.052568 Akaike info criterion -2.972889

Sum squared resid 0.660454 Schwarz criterion -2.672431

Log likelihood 409.9620 Hannan-Quinn criter. -2.852115

F-statistic 10.51050 Durbin-Watson stat 1.953420

Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model

selection.

Dependent Variable: RJCI

Method: ARDL

Date: 09/23/17 Time: 09:53

Fixed regressors: C

Note: final equation sample is larger than selection sample Variable Coefficient Std. Error t-Statistic Prob.*

RJCI(-1) 0.047407 0.063332 0.748544 0.4549

RJCI(-2) -0.163447 0.060349 -2.708376 0.0073

MSCI 0.947905 0.096104 9.863315 0.0000

MSCI(-1) -0.696423 0.155513 -4.478219 0.0000

MSCI(-2) -0.053248 0.158604 -0.335730 0.7374

MSCI(-3) -0.256381 0.116150 -2.207333 0.0282

LIP 0.075919 0.077018 0.985737 0.3253

LIP(-1) 0.063785 0.076098 0.838194 0.4028

LIP(-2) -0.187396 0.074599 -2.512031 0.0127

LER 0.038482 0.022654 1.698672 0.0907

LCP 0.005439 0.021381 0.254385 0.7994

IR -0.006052 0.001947 -3.107960 0.0021

IR(-1) 0.005107 0.001903 2.683189 0.0078

FFR -0.025191 0.031956 -0.788324 0.4313

FFR(-1) -0.038503 0.056228 -0.684764 0.4942

FFR(-2) 0.112365 0.056718 1.981098 0.0487

FFR(-3) -0.047201 0.031946 -1.477492 0.1409

DUM03 0.047799 0.048820 0.979095 0.3285

selection.

Dependent Variable: RNASI

Method: ARDL

Date: 09/23/17 Time: 09:56

Dynamic regressors (4 lags, automatic): LOP MSCI LER LCP IR FFR

Fixed regressors: C

Selected Model: ARDL(1, 0, 1, 0, 3, 0, 4, 0) Variable Coefficient Std. Error t-Statistic Prob.* RNASI(-1) -0.132709 0.061163 -2.169739 0.0310

LOP 0.106703 0.081579 1.307972 0.1921

MSCI 0.244688 0.108573 2.253680 0.0251

MSCI(-1) -0.247950 0.112132 -2.211231 0.0280

LER 0.072528 0.027527 2.634826 0.0090

LCP 0.150278 0.119322 1.259440 0.2091

LCP(-1) 0.140707 0.188076 0.748141 0.4551

LCP(-2) -0.135886 0.187353 -0.725296 0.4690

LCP(-3) -0.163172 0.117610 -1.387396 0.1666

IR 0.003157 0.001686 1.873223 0.0622

FFR -0.079849 0.036503 -2.187450 0.0297

FFR(-1) 0.073516 0.062932 1.168178 0.2439

FFR(-2) -0.045630 0.063980 -0.713184 0.4764

FFR(-3) 0.189484 0.062681 3.022968 0.0028

FFR(-4) -0.133158 0.035982 -3.700697 0.0003

DUM97 -0.083868 0.026437 -3.172371 0.0017

C -1.082558 0.600825 -1.801786 0.0728 R-squared 0.212819 Mean dependent var 0.016338

selection.

Dependent Variable: RXU100

Method: ARDL

Date: 09/23/17 Time: 09:57

Fixed regressors: C

Note: final equation sample is larger than selection sample Variable Coefficient Std. Error t-Statistic Prob.* RXU100(-1) -0.139487 0.062079 -2.246917 0.0255

MSCI 1.264595 0.187867 6.731314 0.0000

MSCI(-1) -0.707175 0.273976 -2.581155 0.0104

MSCI(-2) -0.421316 0.203969 -2.065591 0.0399

LIP -0.246129 0.166797 -1.475622 0.1413

LIP(-1) -0.276346 0.168347 -1.641526 0.1020

LER 0.016897 0.017044 0.991363 0.3225

LCP -0.232311 0.185921 -1.249516 0.2127

LCP(-1) 0.342598 0.191830 1.785943 0.0753

IR -0.000900 0.000241 -3.728174 0.0002

IR(-1) 0.000169 0.000254 0.663951 0.5073

IR(-2) 0.000803 0.000256 3.138540 0.0019

IR(-3) -0.000549 0.000247 -2.219398 0.0274

FFR -0.013217 0.006831 -1.934751 0.0542

DUM00 -0.098458 0.040865 -2.409317 0.0167

selection.

(xi) Autocorrelation Result Output

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