volatility spillover effect of emerging - ResearchDirect

VOLATILITY SPILLOVER EFFECT OF EMERGING

MARKETS AND ECONOMIC GROWTH VERSUS OIL

PRICE VOLATILITY:

THE CASE OF THE GULF CO-OPERATION COUNCIL

COUNTRIES

By

ABDALLAH FAYYAD

A Thesis Submitted in Fulfilment of the Requirements

for the Award of the Degree

Doctor of Philosophy (Economics and Finance)

SCHOOL OF BUSINESS

UNIVERSITY OF WESTERN SYDNEY

AUSTRALIA

JULY 2013

i

Abstract

The relationship between stock markets returns, economic growth and oil price

volatility has been an issue of considerable debate. While there are many studies

showing that oil price shocks have significant effects on the economy, fewer studies

have examined the relationship between oil prices, stock markets and gross domestic

product (GDP), and even less research has investigated oil as a finite resource that will

one day vanish and force the oil-dependent countries to search for economic

diversification of their income resources.

The argument of this study is that stock market returns in the Gulf Co-operation Council

(GCC) countries are affected by volatility in both regional stock markets and oil prices,

while economic growth is affected by oil price volatility, with oil prices the main

determinants of growth in the GCC countries. This study will examine the volatility and

shock transmission mechanisms between the equity markets of the GCC countries and

crude oil prices on the one hand, and between crude oil prices and GDP on the other. A

multivariate generalised autoregressive conditional heteroscedasticity (MGARCH) Baba,

Engle, Kraft and Kroner (BEKK) model and a vector auto-regression (VAR) model will

be used to identify the source and magnitude of the volatility and volatility spillovers.

This will then be compared to the estimation results from three different groups—the

GCC stock market returns, GDPs and oil prices—over the periods 21 September 2005

to 12 February 2010 and 1987 to 2011.

The most important findings indicate that persistent volatility in the emerging markets

of the GCC countries is largely derived from domestic markets. Oil returns predicted

the Saudi Arabian stock market, but could not be predicted by any of the GCC stock

markets. The response of stock market returns to shocks generated by oil was large in

all markets, and created a memory during the periods studied. Oil prices were found to

ii

play a major role in the forecast of error in the variance of the GDPs of GCC countries.

Finally, a shock originating from oil prices had a major and persistent impact on the

GDPs of all GCC countries, with Kuwait showing the greatest response.

iii

Statement of Authentication

I, Abdallah Fayyad, declare that this thesis has not been submitted, either in whole or in

part, for a degree at this University or any other academic institution. I also certify that

the work presented in this thesis is, to the best of my knowledge and belief, my own

work and original except as acknowledged in the text.

Abdallah Fayyad

------------------------------

Signature of Candidate

iv

List of Publications During Candidature

1. Journal Paper: The Volatility of Market Returns: A Comparative Study of

Emerging versus Mature Markets

Abdallah Fayyad and Kevin Daly

International Journal of Business and Management, Vol. 5, No. 7 (2010)

2. Conference Paper—USA: World Oil Prices and Stock Market Return! The case

of GCC Countries, UK and USA

Kevin Daly and Abdallah Fayyad

IABE-2010, Las Vegas, Nevada, USA, Annual Conference, 17–20 October 2010

3. Journal Paper: World Oil Prices and Stock Market Return! The case of GCC

Countries, UK and USA


Journal of the Academy of Business and Economics, Vol.10, No. 3 (2010)

4. Journal Paper: The impact of oil price shocks on stock market returns:

Comparing GCC countries with the UK and USA


Emerging Markets Review, Vol. 12, Issue 1 (2011)

5. Journal Paper: International Transmission of Stock Returns: Mean and

Volatility Spillover Effects in the Emerging Markets of the GCC Countries, the

Developed Markets of the UK, USA and Oil


International Research Journal of Finance and Economics, Issue 67 (2011)

v

6. Conference Paper—Singapore: Returns Transmission and Volatility Spillovers

between the GCC Stock Markets, UK, USA and Oil

Abdallah Fayyad

Singapore Economic Review Conference 2011, 4–6 August 2011

7. Journal Paper: Can Oil Prices Predict Stock Market Returns? The Case of GCC

Countries, UK and USA

Kevin Daly and Abdallah Fayyad

Modern Applied Science, Vol. 5, No. 6, 41–55 (2011)

8. Journal Paper: The Oil Price Shock Effects on Macroeconomic Fundamentals of

the GCC Countries


International Journal of Business Research, 12–3 (2012)

9. Conference Paper—Malaysia: The Oil Price Shock Effects on Macroeconomic

Fundamentals of the GCC Countries

Abdallah Fayyad

International Conference on Business Infrastructure, 22–23 October 2012

vi

Dedication

I lovingly dedicate this thesis to my second country Australia, the country of freedom,

for opening the horizon for my ambitions;

I proudly dedicate this work to my homeland Palestine, for engraving the challenge, and

the determination in myself;

To my daughter and sons, Layan, Hamzah, Khalid, and Ali- may you also be motivated

and encouraged to reach your dreams.

vii

Acknowledgments

THANK YOU

To the following people who assisted me and made the completion of this thesis

possible by their support, encouragement and professional consultation in different

ways. First and foremost, I am forever indebted to my principal supervisor Associate

Professor Kevin Daly, who has been very supportive throughout this study. His keen

mind, abilities and expert guidance put me on the right track and enabled me to

complete this research. I would like to thank him for his hard work, patience, and

especially for his confidence on me. I am also grateful to my co-supervisors, Professor

Ron Ratti and Dr Anil Mishra, for their support and encouragement.

I am also indebted to University of Western Sydney for awarding me the PhD

scholarship (UWS Award) and their generous financial support during my study. I wish

also to thank both the academic and administrative staff in the School of Business at the

University of Western Sydney for their support. Many thanks go to Ms. Nikki

Gallaway for her help, assistance and patience through my candidature.

Most important of all, my sincere and warmest gratitude go to my parents Abdul-

Rahman and Nawal, my wife Arwa, my sons Hamzah, Khalid & Ali and my

daughter Layan for their moral support and motivation support during my studies.

Thank you all; without your support, this study would have not been possible.

Above all, I should say AL-HAMDULILAH, for without him, everything would cease

to be.

Abdallah Fayyad

viii

Abbreviations

ADF Augmented Dickey-Fuller

ADIA Abu Dhabi Investment Authority

ARCH Autoregressive Conditional Heteroscedasticity

BEKK Baba, Engle, Kraft and Kroner

BHHH Berndt, Hall, Hall, and Hausman

BSA Bahrain Stock Exchange

CAPM Capital Asset Pricing Model

CIA Central Intelligence Agency

CMA Capital Market Authority

EGARCH Exponential Generalised Autoregressive Conditional

EIA Energy Information Administration

EMH Efficient Market Hypothesis

FIGARCH Fractionally Integrated Generalised Autoregressive Conditional

GARCH General Autoregressive Conditional Heteroscedasticity

GCCC Gulf Co-operation Council Countries

GDP Gross Domestic Product

GFC Global Financial Crisis

HDI Human Development Index

HL Half-Life

ICT Information and Communication Technology

IMF International Monetary Fund

KIA Kuwait Investment Authority

KSE Kuwait Stock Exchange

LB Ljung-Box

LM Lagrange Multiplier

ix

LRT Likelihood Ratio Test

MGARCH Multivariate Generalised Autoregressive Conditional Heteroscedasticity

ML Maximum Likelihood

MSCI Morgan Stanley Capital International Incorporated

MSM Muscat Securities Market

OECD Organisation for Economic Co-operation and Development

OLS Ordinary Least Squares

OPEC Organization of the Petroleum Exporting Countries

S&P Standard and Poor’s

SAMA Saudi Arabian Monetary Authority

SIC Schwarz Information Criterion

SPF State Petroleum Fund

SUR Seemingly Unrelated Regression

SUTSE Seemingly Unrelated Time Series Equation

SV Stochastic Volatility

SWFs Sovereign Wealth Funds

UAE United Arab Emirates

UK United Kingdom

US United States

UN United Nations

VAR Vector AutoRegression

WTI West Texas Intermediate

x

Table of Contents

Abstract .............................................................................................................................. i

Statement of Authentication ............................................................................................. iii

List of Publications During Candidature ....................................................................... ivv

Dedication ........................................................................................................................ vi

Acknowledgments ........................................................................................................... vii

Abbreviations ............................................................................................................... vviii

Table of Contents .............................................................................................................. x

List of Tables................................................................................................................... xv

List of Figures ............................................................................................................... xvii

Chapter One: Scope and Framework of the Study ............................................................ 1

1.1 Introduction ................................................................................................................. 1

1.2 Motivation for this Research ....................................................................................... 2

1.3 Objectives of this Research ......................................................................................... 4

1.4 Significance of this Research ...................................................................................... 7

1.5 Thesis Structure ........................................................................................................... 8

Chapter Two: Literature Review ..................................................................................... 11

2.1 Overview ................................................................................................................... 11

2.2 Oil and the Stock Market .......................................................................................... 12

2.2.1 The Importance of Oil as an Exhaustible Resource ............................................... 12

2.2.2 The Peak of Oil Production .................................................................................... 14

2.2.3 Formation of the GCC and Stock Markets in the GCC Countries ......................... 17

2.2.3.1 The Bahrain Stock Market .................................................................................. 18

2.2.3.2 The Kuwait Stock Market ................................................................................... 19

2.2.3.3 The Oman Stock Market ..................................................................................... 20

2.2.3.4 The Qatar Stock Market ...................................................................................... 20

2.2.3.5 The Saudi Arabian Stock Market ........................................................................ 21

2.2.3.6 The United Arab Emirates Stock Market ............................................................ 21

2.3 Oil and Stock Market Volatility ................................................................................ 22

2.3.1 The Importance of Volatility .................................................................................. 22

2.3.2 The Efficient Market Hypothesis ........................................................................... 23

xi

2.3.3 Stylised Facts about Oil and Financial Market Volatility ...................................... 25

2.3.4 Standard Cash-Flow/Divedend Valuation Model .................................................. 28

2.3.5 Definition of Volatility and its Measurement ........................................................ 28

2.4 Causes of Volatility in the Oil and Stock Markets .................................................... 30

2.4.1 Causes of Volatility in Oil Prices ........................................................................... 30

2.4.2 Causes of Volatility in the Stock Market ............................................................... 33

2.5 ARCH/GARCH Class Conditional Volatility Models .............................................. 35

2.5.1 The ARCH Model .................................................................................................. 36

2.5.2 The GARCH Model ............................................................................................... 38

2.6 Multivariate GARCH Models ................................................................................... 40

2.6.1 MGARCH-DVEC .................................................................................................. 42

2.6.2 MGARCH-BEKK .................................................................................................. 43

2.7 Empirical Evidence of Volatility with the ARCH/GARCH and MGARCH Models

......................................................................................................................................... 45

2.7.1 Volatility in Oil Markets ........................................................................................ 45

2.7.2 Volatility in Stock Markets .................................................................................... 49

2.7.3 Volatility in the GCC Stock Markets ..................................................................... 53

2.7.4 Volatility in Oil and Stock Markets ....................................................................... 56

2.8 GCC Countries and Economic Growth ..................................................................... 60

2.9 The Resource Curse and Dutch Disease ................................................................... 62

2.9.1 The Resource Curse ............................................................................................... 62

2.9.2 Dutch Disease......................................................................................................... 63

2.10 Volatility and GDP .................................................................................................. 64

2.10.1 Oil Prices and Economic Activity ........................................................................ 65

2.10.2 Oil Price Volatility and Global Financial Crises (GFC) ...................................... 66

2.10.3 Empirical Findings—the Impact of Oil Prices on Economic Activity ................ 68

2.10.4 National Unemployment in the GCC Countries .................................................. 70

2.10.5 Building Human Capital ...................................................................................... 72

2.10.6 Institutions and Governance ................................................................................. 72

2.10.7 Vertical Policies and Effective Public Spending ................................................. 73

2.11 Summary ................................................................................................................. 73

Chapter Three: Future Economic Sustainability of Oil Rich Countries ......................... 76

3.1 Introduction ............................................................................................................... 76

3.2 History of the GCC Countries ................................................................................... 76

xii

3.2.1 The 1980s ............................................................................................................... 76

3.2.2 Post-1990 ............................................................................................................... 77

3.3 GCC Countries in Focus ........................................................................................... 79

3.3.1 Saudi Arabia ........................................................................................................... 79

3.3.2 The UAE ................................................................................................................ 80

3.3.3 Kuwait .................................................................................................................... 81

3.4 Standard of Living Indicators ................................................................................... 82

3.4.1 Human Development Index ................................................................................... 83

3.4.2 Health Indicators .................................................................................................... 84

3.4.3 Education Indicators............................................................................................... 85

3.4.4 Information and Telecommunications Indicators .................................................. 86

3.4.5 Transport Indicators ............................................................................................... 87

3.4.6 Government Subsidies ........................................................................................... 89

3.4.7 GDP Per Capita ...................................................................................................... 90

3.5 Economic Sector Breakdown .................................................................................... 91

3.6 Why Diversify? ......................................................................................................... 96

3.6.1 The GCC and Economic Diversification ............................................................... 97

3.6.2 Government Diversification in the GCC Countries ............................................. 101

3.6.3 Non-Hydrocarbon Growth ................................................................................... 102

3.6.4 Sovereign Wealth Funds ...................................................................................... 103

3.6.5 Examples of Economic Diversification ............................................................... 104

3.7 Summary ................................................................................................................. 107

Chapter Four: Volatility Spillovers in the Emerging Markets of the GCC Countries and

Oil—a Multivariate GARCH Model ............................................................................. 108

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

4.2 Background ............................................................................................................. 110

4.3 Data and Empirical Results ..................................................................................... 113

4.4 Methodology ........................................................................................................... 115

4.4.1 Unit Root Test ...................................................................................................... 115

4.4.2 MGARCH-BEKK Methodology ......................................................................... 116

4.5 Results ..................................................................................................................... 120

4.5.1 Volatility .............................................................................................................. 120

4.5.2 ARCH: Own and Cross Innovation...................................................................... 121

ARCH: Own Innovation ............................................................................................... 121

xiii

ARCH: Cross Innovations ............................................................................................ 121

4.5.3 GARCH: Own and Cross Volatility Persistence .................................................. 122

GARCH: Own Volatility Persistence ........................................................................... 122

GARCH: Cross Volatility Persistence .......................................................................... 123

4.5.4 Ljung-Box Test for Standardised Residuals ........................................................ 124

4.6 Summary and Conclusions ...................................................................................... 125

Chapter 5: World Oil Prices and Emerging Stock Markets in the GCC Countries: VAR

Analysis ......................................................................................................................... 127

5.1 Introduction ............................................................................................................. 127


5.3 Methodology ........................................................................................................... 132

5.3.1 Unit Root Test ...................................................................................................... 132

5.3.2 VAR Methodology ............................................................................................... 133

5.3.3 VAR Estimation ................................................................................................... 135

5.4 Variance Decomposition ......................................................................................... 136

5.5 Impulse Response ................................................................................................... 142

5.6 Conclusion .............................................................................................................. 152

Chapter Six: Oil Price Shock Effects on Macroeconomic Fundamentals of the GCC

Countries ....................................................................................................................... 154

6.1 Introduction ............................................................................................................. 154

6.2 Background ............................................................................................................. 157


6.4 Methodology ........................................................................................................... 160

6.4.1 Unit Root Test ...................................................................................................... 160

6.4.2 VAR ..................................................................................................................... 160

6.5 Results ..................................................................................................................... 161

6.5.1The Effect of Oil Price Shocks on GDP ............................................................... 161

6.5.1.1 Variance Decomposition ................................................................................... 161

6.5.1.2 Impulse Response.............................................................................................. 163

6.6 Summary and Conclusions ...................................................................................... 165

Chapter Seven: Summary and Conclusions .................................................................. 165

7.1 Introduction ............................................................................................................. 167

7.2 Overview of the Thesis ........................................................................................... 168

7.3 Implications of the Results of this Study ................................................................ 174

xiv

7.4 Limitations of the Study .......................................................................................... 175

7.5 Areas for Future Research ....................................................................................... 176

Appendices .................................................................................................................... 178

References ..................................................................................................................... 188

xv

List of Tables

Table 2.1: Oil Reserves and Production in Different Countries ..................................... 17

Table 2.2: GCC Country Stock Market and Economic Characteristics .......................... 19

Table 2.3: Summary of Empirical Studies on Oil Price Volatility ................................. 47

Table 2.4: Summary of Empirical Studies on Oil Price Volatility ................................. 59

Table 2.5: Hydrocarbon GDP as a Percentage of Overall GDP ..................................... 60

Table 2.6: GCC Country Selected Economic Indicators, Estimated in 2009 ................. 62

Table 2.7: Volatility Measures in the GCC ..................................................................... 65

Table 2.8: Average Real GDP Per Capita Performances 1971–2001 ............................. 71

Table 3.1: Mortality Rate (infants per 1,000 live births) ................................................ 84

Table 3.2: GCC Country Adult Literacy Rates (percentage of people aged 15 and above)

......................................................................................................................................... 85

Table 3.3: Internet Users (per 100 people)...................................................................... 86

Table 3.4: Mobile Phone Ownership (per 100 people) ................................................... 87

Table 3.5: Passenger Cars/Motor Vehicles (per 1,000 people) ....................................... 88

Table 3.6: GDP Per Capita (USD), 2009 Expected and 2010 Forecast .......................... 90

Table 3.7: Saudi Arabia—GDP by Type of Economic Activity (million riyals) ............ 92

Table 3.8: Kuwait: Sectorial Origin of GDP at Current Prices (million dinars) ............. 94

Table 3.9: UAE Sectorial Origin of GDP at Current Prices (billion dirhams) ............... 95

Table 3.10: Per Cent GDP Growth 2000–2010, 2009 Estimated and 2010 Forecast ..... 99

Table 3.11: Percentage Average Yearly Non-Hydrocarbon GDP Growth in the Previous

Five Years ..................................................................................................................... 103

Table 4.1: GCC Economies, Stock Markets and Oil in 2007 ....................................... 110

Table 4.2: Summary Statistics of Daily Returns for Seven Stock Markets and Oil ..... 114

Table 4.3: Estimated Coefficient for Variance-Covariance Equations ......................... 122

Table 4.4: LB Test for Standardised Residuals ............................................................. 124

xvi

Table 5.1: Summary Statistics of Daily Returns for Three GCC Country Stock Markets

and Oil ........................................................................................................................... 131

Table 5.2: Variance Decomposition for the Forecast Error of Daily Market Returns for

GCC and Oil Markets During the First Period (Normal) .............................................. 137


GCC and Oil Markets During the Second Period (Rise) .............................................. 139


GCC and Oil Markets During the Third Period (Fall) .................................................. 141

Table 5.5: Accumulated Response of All Markets to One Standard Deviation Innovation

for the First Period (Normal)......................................................................................... 143

Table 5.6: Accumulated Responses of All Markets to One Standard Deviation

Innovation for the Second Period (Rise) ....................................................................... 145

Table 5.7: Accumulated Response of All Markets to One Standard Deviation Innovation

for the Third Period (Fall) ............................................................................................. 148

Table 6.1: GCC Economies, Stock markets and Oil in 2007 ........................................ 156

Table 6.2: Summary Statistics ...................................................................................... 160

Table 6.3: Variance Decomposition for the Forecast Error of GCC GDP and Oil ....... 162

Table 6.4: Accumulated Response of All Series to One Standard Deviation Innovation

Shock from Oil .............................................................................................................. 164

Table A.1: Time Series Unit Root Test (1st difference of raw data) ............................ 178

Table A.2: Unit Root Test for Oil and Market Returns Series...................................... 179

Table A.3: VAR Estimates for Oil and Market Returns 21/09/2005–06/10/2006

Normal–1st Period ........................................................................................................ 180

TableA.4: VAR Estimates for Oil and Market Returns 09/10/2006–13/10/ 2008 Rise–

2nd Period ..................................................................................................................... 182

TableA.5: VAR Estimates for Oil and Market Returns 14/10/2008–11/02/ 2010 ..............

Fall–3rd Period .............................................................................................................. 184

Table A.6: Time Series Unit Root Test ......................................................................... 186

Table A.7: VAR Estimates for Oil and GDP 1987–2011 ............................................. 187

xvii

List of Figures

Figure 2.1: Sources of Energy Consumed ...................................................................... 13

Figure 2.2: US Oil Production and the Hubbert High Estimate for the US .................... 16

Figure 2.3: S&P 500 USA Index Return ......................................................................... 26

Figure 2.4: Europe Brent Oil Price ................................................................................. 26

Figure 2.5: Comparison of Stock Market Performance between the GCC Markets and

Other Major Markets in 2002 .......................................................................................... 54

Figure 2.6: Five GCC Stock Market and Brent Oil Prices for the Period 21/04/2006–

5/10/2009 ........................................................................................................................ 57

Figure 2.7: GCC GDP Per Capita ................................................................................... 61

Figure 2.8: GDP Growths of the GCC Countries ........................................................... 65

Figure 2.9: US and Major Oil and Gas Exporters ........................................................... 67

Figure 2.10: National Unemployment Growth Rates in the GCC Countries 1974–2002

......................................................................................................................................... 71

Figure 3.1: Demographic Trends—Population in Millions ............................................ 83

Figure 3.2: Human Development Index .......................................................................... 83

Figure 3.3: Life Expectancy (2000–2009) ...................................................................... 84

Figure 3.4: Tertiary Enrolment (Gross Percentage) ........................................................ 86

Figure 3.5: Fixed Broadband Internet Subscribers ......................................................... 87

Figure 3.6: Air Transport, Passengers Carried ................................................................ 88

Figure 3.7: Health Expenditure Per Capita (current USD) ............................................. 89

Figure 3.8: Public Spending on Education (per cent GDP) ............................................ 90

Figure 3.9: GDP Derived from Hydrocarbon in 2007 (USD, billions) ........................... 99

Figure 3.10: Economic Concentration and Diversification in the GCC countries,

Norway and Canada, Real 2005 GDP ........................................................................... 101

Figure 3.11: GCC’S Sovereign Wealth Funds (USD billions) ..................................... 104

Figure 4.1: Europe Brent Daily Oil Price (1987–2011) ................................................ 109

xviii

Figure 4.2: Five GCC Stock Markets and Brent Oil Prices 25/05/2006–18/11/2009 ... 112

Figure 4.3: Market Daily Returns Between 21/09/2005 and 12/02/2010 ..................... 115

Figure 4.4: ACF of Observations .................................................................................. 116

Figure 4.5: Estimated Conditional Variance for Daily Returns for Oil and Stock Markets

....................................................................................................................................... 120

Figure 4.6: QQ Plots of Standardised Residuals ........................................................... 125

Figure 5.1: Five GCC Stock Markets and Brent Oil Prices Between 25/05/2006 and

18/11/2009 .................................................................................................................... 129

Figure 5.2: Stock Market Prices and the Crude Oil Price January 1971–March 2008 . 130

Figure 5.3: Market Daily Returns Between 21/09/2005 and 11/02/2010 ..................... 132

Figure 5.4: Accumulated Response of All Markets to One Standard Deviation

Innovation for the First Period (Normal) ...................................................................... 144


Innovation for the Second Period (Rise) ....................................................................... 147


Innovation for the Third Period (Fall) ........................................................................... 150

Figure 6.1: Average Oil Price and GDP for Selected GCC Countries 1987–2011....... 155

Figure 6.2: Geographical Distribution of Proven Oil and Gas Reserves ...................... 156

Figure 6.3: Variance Decomposition for the Forecast Error of GCC GDP and Oil ..... 163

Figure 6.4: Accumulated Response of All Series to One Standard Deviation Innovation

in Oil ............................................................................................................................. 165

1

Chapter One: Scope and Framework of the Study

1.1 Introduction

The relation between stock market return and oil price volatility has been a significant

topic of interest in the finance literature. Nevertheless, of the many studies showing that

oil price shocks have significant effects on the economy, rather less research has

examined the relationship between oil prices, stock markets and gross domestic product

(GDP), while even fewer studies investigate oil as a finite resource, which will one day

vanish, forcing the oil-dependent countries to search for economic diversification of

their income resources.

Stock market volatility has been of critical importance for all research related to market

efficiency. Volatility as a phenomenon and a concept remains central to modern

financial markets and academic research. The link between volatility and risk has been

to some extent vague, but stock market volatility is not necessarily a bad thing.

However, the existence of certain financial phenomena such as the end-of-the-week

effect, the January effect and volatility clustering makes it possible to estimate stock

prices and the magnitude of stock price movement. In reality, volatility can form the

basis for efficient price discovery, while volatility dependence implies predictability,

which is welcomed by traders and medium term investors (Gregoriou 2009).

One of the major breakthroughs in financial economics is in the modelling of variable

variances (conditional heteroscedasticity) and volatility clustering in equity returns. The

general autoregressive conditional heteroscedasticity (GARCH) framework builds on

the conception of volatility dependence to measure the impact of last period’s forecast

error and volatility in determining current volatility.

2

The ARCH and GARCH models have proven to capture the time-varying variances of

economic data well in the univariate case. This has encouraged many researchers to

expand these models to the multivariate dimension (Tse and Tsui 2000).

This research will examine the volatility and shock transmission mechanisms between

the equity markets of the GCC (Gulf Co-operation Council) countries and crude oil

prices on one hand and between crude oil prices and GCC country GDPs on the other

hand. A MGARCH-BEKK model and a vector autoregression (VAR) model will be

used to identify the source and magnitude of spillovers.

The thesis will be divided into three parts: first, building the MGARCH-BEKK model

between the market return of GCC countries and Brent crude oil; second, building the

VAR model between the market return of GCC countries and Brent crude oil; and

finally, studying the relation between oil as a finite exhaustible resource and GCC

GDPs by applying the VAR model.

The GCC was established in 1981 and includes six countries; Bahrain, Oman, Kuwait,

Qatar, Saudi Arabia and the United Arab Emirates (UAE). The GCC countries share

several common structural patterns. In 2007, they produced approximately 20 per cent

of all world oil, control 36 per cent of world oil exports and acquire 47 per cent of

verified reserves. Oil exports largely determine their earnings, government budget

revenues and expenditures and aggregate demand. The contributions of oil to GDP

range from 22 per cent in Bahrain to 44 per cent in Saudi Arabia (Arouri & Rault 2010).

1.2 Motivation for this Research

This thesis is an empirical study employing advanced econometric techniques to capture

a number of effects emanating from outside the GCC countries, and secondly, within

the GCC countries. The thesis will employ the BEKK and VAR techniques to

specifically identify the effects of changes in oil prices on financial (i.e., stock market)

returns and GDPs within the GCC countries, which are predominantly Islamic with

cultural/political/religious backgrounds that differ significantly from the advanced

economies. While this thesis will not focus on these aspects, these differences need to

3

be incorporated within the thesis. The MGARCH and VAR models have been

extensively applied to multivariate time series in order to capture volatility spillover.

The motivation for this research is the current practice of examining stock market

volatility spillover through the analysis of conditional variance structures associated

with returns on the different stock market indices. The conditional variance of the return

on a particular stock market can be affected by the market return itself, the returns to

other markets in the local, regional and global areas and world oil prices.

Secondly, this research will focus on the relationship between oil prices and GCC

country GDPs referring to the economic theory of exhaustible resources (e.g., oil),

which can be traced back to 1914 (Chermak 2000). World oil production peaked in July

2008 at 74.82 million barrels/day (mbd), and in 2009 had fallen to about 71 mbd. It is

expected that oil production will decline at the rate of about 2.2 mbd, and that after 2010

this resulting annual production decline rate will increase to 3.4 per cent. From 2011,

the Organization of the Petroleum Exporting Countries (OPEC) will not have the ability

to offset cumulative non-OPEC declines and world oil production is forecast to stay

below its 2008 peak (Drum 2009). As long as consumption is less than the proceeds

from extracted minerals—mainly oil in the GCC countries—there is some positive

saving, and this serves to convert wealth from the form of oil reserves to another forms,

such as financial assets held overseas or domestically and physical and human capital

held domestically (Beblawi 1984). From this point of view the motivation for this

research is the current practice of examining the relationship between GCC country

GDPs and oil prices using the VAR model.

This research will be conducted on three of the GCC countries (Saudi Arabia, the UAE

and Kuwait). Those three countries have been selected because they possess the highest

oil reserves among the six GCC countries and they rank first, fifth and sixth world-wide

for oil reserves (Master 2010). Over the past decade, the economic and social

development of the GCC countries has been financed mainly by government oil returns.

In addition, it appears that any future development of the region will continue to depend

on oil revenues, at least in the short term. Certainly, the GCC countries are very well

aware that one day oil will run out, and they have all made efforts to diversify their

economies in order to reduce their dependence on oil.

4

As a consequence, this research will investigate economic diversification in the GCC

countries. High economic concentration (the concentration ratio measures a nation’s

concentration in a given sector by taking the sum of the squares of the per cent

contribution to GDP) makes an economy susceptible to external shocks, such as

volatility in the price of the dominant commodity, which in the GCC countries is oil,

reflecting the sensitivity of GCC real activity to oil price shocks.

Diversification will appear in the long term. For example, Dubai has attempted to

diversify its economy away from oil sector by investing heavily in areas such as

construction, financial services, tourism and manufacturing. In 2005, only 5 per cent of

Dubai’s GDP came from the oil and gas sector. Contrast this with the UAE’s Abu

Dhabi, which derived 59 per cent of its 2005 GDP from oil and gas, and whose growth

in non-oil sectors continues to lag.

A common explanation for the persistent lack of economic diversity seen in areas such

as Abu Dhabi is that economic concentration is inevitable in regions that are rich in a

particular natural resource. Examples include Brunei, whose economy is entirely

dependent on its reserves of petroleum and natural gas; Zambia, whose economy is

entirely dependent on its reserves of copper, and Botswana, whose economy is tied

tightly to the diamond-mining industry (Shediac, Abouchakra & Najjar 2008).

For Saudi Arabia, Kuwait and the UAE, as well as for other GCC countries, economic

diversification requires the development of their non-oil sectors, together with the

reduction of the proportions of their government revenues and export earnings

attributable to oil and gas. Inherent to this process would is a decrease in the role of the

public sector (Alnasseri 2005).

1.3 Objectives of this Research

This thesis has three related objectives. The first is the development of a procedure to

model volatility spillover that is based on the BEKK approach pioneered by Baba,

Engle, Kraft and Kroner (1990). This procedure will be used to test for the volatility and

volatility spillovers between oil and selected GCC country stock markets returns. It will

5

also be used evaluate the dynamic conditional covariance and dynamic conditional

correlation between the equity markets of developing GCC countries and the

international price of oil—the Europe Brent Spot Price.

The second and third related aims of this thesis are to research the relationship between

oil prices and stock markets return by applying the VAR model, and also examine the

relationship between oil prices volatility and GCC country GDPs. The VAR approach is

commonly used for forecasting systems of interrelated time series and for analysing the

dynamic impact of random disturbances on the system of variables. The VAR approach

bypasses the need for structural modelling by treating every variable in the system as

endogenous as a function of the lagged values of all endogenous variables in the system.

The term ‘autoregressive’ is due to the appearance of the lagged values of the dependent

variable on the right-hand side and the term ‘vector’ is due to the fact that a vector of

two (or more) variables is included in the system model (Hung 2009).

Oil is an exhaustible resource; thus, a good forecasting structure of oil supply is vital to

all parties involved in the energy business, such as oil companies, financial institutions,

public policy planners and makers, and oil exporting and importing countries. Such a

study will help bring stability and security to the crude oil market. Decline curve

analysis, black oil model history matching and past trend extrapolations are often

considered statistical methods of production forecasting. This research will also discuss

the Hubbert model, since this model is one of the most renowned statistical models for

the prediction of oil and gas production. The Hubbert model was originally developed

in 1956 (Hubbert 1956). The objective of this study is to discuss multi-cyclic Hubbert

model forecasting techniques for world oil production after evaluating production trends

of the major GCC oil producing countries, which essentially supply the world with

crude oil. Each country has its own prediction table; the world model will be discussed

by combining the data from all countries.

Oil is an exhaustible resource. This means that one day it will disappear, and to begin

again, the GCC countries should assess the concentration and diversification of their

economies. This study will determine whether the GCC countries’ GDPs are

consistently distributed across a wide variety of economic sectors, or whether they

depend heavily on just one or two sectors. The level of concentration for Canada and

6

Norway will be assigned as a benchmark for this comparison, since these countries are

pioneers in diversification. The degree of diversification will be calculated by

evaluating the distribution of a nation’s GDP across its various economic sectors, such

as agriculture or manufacturing, while the concentration ratio measures a nation’s

concentration in a given sector by taking the sum of the squares of per cent contribution

to GDP. The diversification quotient is the inverse of the concentration ratio; it provides

a metric that politicians can use to scale their nation’s economic diversity. Essentially,

the lower the concentration ratio and the higher the diversification quotient, the more

diversified a nation’s economy (Shediac, Abouchakra & Najjar 2008).

7

1.4 Significance of this Research

This research extends the limited literature on volatility spillovers in the GCC countries.

It will examine the volatility transmission relationship between the stock market returns

of selected GCC countries and their GDP’s on one side, and international oil prices on

the other.

The significance of this research arise from the importance of the GCC countries, as

they produce 20 per cent of the world’s crude oil output and have acquired 47 per cent

of the world’s oil reserves. According to an innovative study, the GCC countries will

reach their oil production peak at some point in 2027 for Saudi Arabia, in 2030 for the

UAE and in 2033 for Kuwait (Nashawi, Malallah & Al-Bisharah 2010). A key

conclusion that can be drawn from this is that the GCC countries’ non-oil sectors must

be developed in order to fill their structural gaps, such as inefficiencies in labour, capital,

knowledge and technology. In addition, previous findings of oil peaks suggest that

revenues from oil and gas should be reinvested effectively in GCC countries. Excess

liquidity should be used to fund these nations’ internal economies, rather than external

economies. Russia, for example, despite being consumption-geared, recently announced

that it would begin investing more of its oil and gas revenues in infrastructure projects

such as power and transportation, as it is eager to create an infrastructure that can better

support growing businesses across all of its territories beyond Moscow and St.

Petersburg, which will enable the development of outbound industries.

The significant of this research lies in the importance of the GCC countries as an

alternative investment to the developed markets, and in turn because of the

diversification opportunities allowable due to the higher volatility in the exhaustible

resource of crude oil prices. In addition to these factors, much research has shown that

the correlations between the GCC countries’ markets and the advanced markets are low,

which encourages investment in the GCC equity markets. Finally, the value of this

research derives from the fact of non-economic diversification in the GCC countries due

to their dependence on oil. This research identified a link between economic

diversification and sustainable growth. These findings provide a strong reminder to

legislators in the GCC countries that one key to building a strong, sustainable economy

8

is building a diversified economy that does not depend excessively on a single

exhaustible commodity and that has a strong external, as well as internal, focus.

1.5 Thesis Structure

This thesis is presented in six further chapters. The current chapter has provided an

overview of the research topic, motivation, objectives and significance of the thesis. The

remainder of the thesis is organised as follows. Chapter 2 introduces the theoretical

considerations and relevant prior work on oil, stock market and GDP volatility. It begins

with an introduction to stock market volatility, outlining the importance of volatility, the

stylised facts about volatility and some of the basic measures of oil and financial market

volatility spillovers. Oil as an exhaustible resource is then described; as well as the need

for a good forecasting structure of oil supply as being vital to all parties involved in the

energy business, such as oil companies, financial institutions, politicians, and oil

exporting and importing countries. This type of study will help bring stability and

security to the crude oil market.

Chapter 3 provides an outline of the GCC countries’ economic sectors. Key areas

identified include: the government structure, the major sectors in their economies and

the market liberalisation programmes that have been used by the GCC country

governments to reinvest in their capital markets and contribute to economic

diversification. The chapter then provides an introduction to the crude oil price volatility

used as benchmarks for the performance of the GCC countries’ economies and thus is

used to measure economic volatility. The chapter then goes on to examine how the GCC

countries should gauge the economic concentration and diversification of their

economies. This study will determine whether the GCC country GDPs are consistently

distributed across a variety of economic sectors or whether they will continue to be

heavily dependent on oil and the energy sector. This chapter will also describe the

international experience of the Canadian and Norwegian models of economic

diversification in comparison with the GCC countries.

Chapter 4 describes the research methods and models employed in this thesis. It starts

with the provision of a general structure for modelling multivariate volatility and

9

volatility spillover, drawing on Chapter 2. Using general and then specific methodology,

and with imposing restrictions on a general model, finite crude oil volatility and

volatility spillover models are proposed and described as further potential specifications

of volatility dynamics in the GCC economy and GCC stock market returns. The chapter

then describes the methods used to estimate the unknown parameters of the proposed

models and introduces a numerical procedure, the Berndt, Hall, Hall, and Hausman

(BHHH) algorithm, that will be used to maximise the log-likelihood function. The ML

estimate is then applied to obtain an estimate of the unknown parameters of the BEKK

model.

Furthermore, the chapter presents methods of measuring the persistence of volatility

shown in the GCC countries’ economies and stock market returns. Finally, the chapter

presents a detailed description of the primary tests, including the unit root and stationary

tests, used in this thesis in order to determine if the prices in each dataset are stationary

in their levels or first differences. This is necessary to determine whether the volatility

analysis should be performed on prices or on returns. An important part of any

empirical work is a transparent account of the data used.

Chapter 5 will apply the VAR model in order to capture the effect of oil price shocks on

stock markets. It will first examine the VAR estimation by the determination of the

appropriate lag structure for the system, then will estimate the variance decomposition

and finally will apply the impulse response. The results should not look unusual, as

these countries essentially depend, to varying degrees, on oil, and are the world’s most

significant oil exporting countries and possess the largest oil reserves of the oil

exporting countries world-wide.

Chapter 6 will examine the effects of oil price shocks on the oil exports of Kuwait, the

UAE and Saudi Arabia through the assessment of the dynamic relationship between real

economic activity (GDP) and oil price shocks by using the most recent time series data.

In this chapter, an unrestricted VAR model is employed to investigate the long-run

relationships between real economic activity and oil prices in Kuwait, the UAE and

Saudi Arabia. This chapter raises the importance of oil as a finite resource, as the GCC

countries will reach their oil production peaks between 2027 and 2033. This means that

the GCC countries should seek to minimise their economic concentration and maximise

10

their economic diversification to make their economies less vulnerable to external

events, such as changes in the price of the dominant commodity, oil.

Lastly, Chapter 7 concludes the thesis. It begins with an overview of the thesis in terms

of its objectives and results. This is followed by a section dealing with the implications

of the results for modelling the volatility and volatility spillover of crude oil and stock

market returns. This study can only fill a minor gap in the literature on the MGARCH

and VAR models’ assessments of the volatility of financial markets. It then discusses

the volatility of exhaustible crude oil in relation to GCC country GDPs. Finally, the

chapter discusses diversification as a bottleneck solution that should be employed to

avoid economic concentration and economic volatility. While answering some

important questions, this study raises others, and forms the basis for further research.

Thus, the concluding chapter outlines areas for future research, in particular,

applications to other markets and the expansion of the MGARCH and VAR equations

with relevant exogenous variables.

11

Chapter Two: Literature Review

2.1 Overview

The growing importance of oil price and stock return volatility has been a topic of

interest in academic circles in economic and finance, particularly in the last few decades.

Some of the questions that have been raised in the literature include: What are the

causes of oil price and stock market volatility? Has this volatility increased over time?

Has international financial integration led to faster transmission of volatility between

international oil prices and stock market returns? What role do policy-makers play in

the volatility process? Are the GCC countries aware that one day oil will run out? What

efforts have they applied to diversify their economies and to reduce their dependence on

oil? These issues are significant because of the broad impact of oil price volatility on the

world economy. For individual investors a high level of volatility in financial market

returns increases the risk of loss and therefore raises concerns about market stability.

Stock market volatility varies greatly between countries. The general hypothesis for the

difference between stock markets is that the world’s developed stock markets are more

liquid and efficient, and thus experience lower levels of stock price deviation and return

volatility when compared to their emerging counterparts. On the other hand, the

emerging markets have differing characteristics, such as higher average returns, lower

correlations with developed markets and more predictable returns. Each of these

characteristics has made the volatility of emerging markets an interesting topic (see e.g.,

Bekaert & Harvey 1997; Kawakatsu & Mori 1999; Schaller & Van Norden 1997).

Stock market volatility is of significant importance to market efficiency. The efficient

market hypothesis (EMH) is one of the most highly researched concepts in finance, and

forms the basis for assessments of different financial markets. Stock market volatility

12

involves frequent large variations in stock prices, but this is not necessarily in

contravention of the EMH.

This chapter is organised as follows. The next section presents the importance of oil and

stock markets in the GCC countries. Section 3 discusses the volatility in the oil and

stock markets in this context. In Section 4, the causes of volatility in oil and stock

markets will be critically examined. Sections 5 to 7 describe the theoretical and

empirical literature on the ARCH/GARCH and MGARCH models of oil prices and

stock market returns. Section 8 presents the empirical evidence on economic growth in

the GCC countries. Section 9 presents the resource curse and Dutch disease. Section 10

presents volatility and economic activity, and finally, Section 11 provides a summary.

2.2 Oil and the Stock Market

2.2.1 The Importance of Oil as an Exhaustible Resource

Few understand how completely dependent our entire way of life is on oil. Without oil

most of us would not have enough to eat. Oil is the source of our pesticides and

fertilizers and fuels the machinery that makes our farms approximately ten times more

productive than they were in pre-oil days. The pre-oil world supported about a billion

people, while today’s world supports almost seven billion people. In an optimistic

scenario which assumes that technology (genetic engineering) might double our

possible non-oil food production capacity, in a post-oil world we could feed two billion

people. That means the lives of at least five billion people hang in the balance as we run

out of oil.

Without oil most of us would not have electricity, and without electricity most of our

economies would not operate. Clearly, oil makes possible most of our industrial

capability, our technological advances, our imports, our food, our environmental control

(heating and cooling), our medical services, our sources of entertainment and recreation,

our information infrastructure, and almost all of our transportation (Observation 2007).

The current prediction is that we will run out of oil over the next few decades as we are

forced to recover oil from more distant areas and in more unreachable forms such as tar

sands. This should worry us and be a source of wide public and political debate.

13

The economic strength of the GCC countries depends mostly on the tangible value of

oil on international markets, and oil has continued to play a major role in the political,

social, demographic, and cultural developments in these countries (Mackenzie 1996). It

has been estimated that oil makes the largest contribution to the world’s total energy

supply, accounting for about 35 per cent of this, compared with about 25 per cent for

coal, 21 per cent for natural gas and 19 per cent for other sources, as shown in Figure

2.1 (IEA 2005).

Source: IEA (2005).

Figure 2.1: Sources of Energy Consumed

The economic theory of exhaustible resources can be traced back to the early twentieth

century. In 1914 Gray suggested that Ricardo’s economic concept of rent needed

modification to take into account the exhaustibility of certain natural resources

(Chermak 2000). This prediction was formalised in 1931 by Hotelling, who proposed

that the owner of land with mineral deposits has two alternatives: either leave the

mineral in the ground or excavate it and sell it on the market. Unless there is an

expectation that the value of the mineral will rise over time, it would better for the

owner to extract all of it as soon as possible (assuming that this is technically possible).

Hotelling then continued to ask a number of remarkable questions:

How much of the proceeds of a mine should be reckoned as income, and how

much as return of capital? What is the value of a mine when its contents are

35%

25% 2%

10%

1%

6%

21%

Sources of Energy Consumed

Crude oil

Coal

Hydro

Biomass and Waste

Other Renewable

Nuclear

Natural Gas

14

supposedly fully known, and what is the effect of uncertainty of estimate? If a

mine-owner produces too rapidly, he will depress the price, perhaps to zero. If

he produces too slowly, his profit, though larger, may be postponed farther

into the future than the rate of interest warrants. Where is his golden mean?

And how does this most profitable rate of production vary as exhaustion

approaches? Is it more profitable to complete the extraction within a finite

time, to extend it indefinitely in such a way that the amount remaining in the

mine approaches zero at limit, or to exploit so slowly that mining operations

will not only continue at a diminishing rate forever but leave an amount in the

ground which does not approach zero? What about import duties on coal and

oil? And for these dynamical systems what becomes of the classic theories of

monopoly, duopoly, and free competition? (Hotelling 1931, p. 19).

Hotelling’s questions and his endeavour to answer them launched the fundamentals of

the early theory of exhaustible resources. Another major advance in these theoretical

developments took place during the debate around the Club of Rome’s projection

regarding limits to growth and oil price shocks of the 1970s. Many of these hypothetical

analyses were refined and consolidated in studies by Pearce and Rose (1975) and Heal

(1993), and in more contemporary efforts such as those of Cavallo (2002) and Tilton

(2003).

2.2.2 The Peak of Oil Production

It required millions of years for nature to create the existing reservoirs of hydrocarbons.

At any given time, only part of this oil can be recovered at an acceptable cost using the

available techniques. The recoverable part constitutes the ‘known oil reserves’. Oil

reserves are sometimes categorised into three main classes in the oil industry. The first

refers to the ‘proven reserves’, or those that have a 90 per cent chance of being

exploited. The second group is referred to as ‘probable reserves’ and includes those that

are believed to have a 50 per cent chance of being extracted using current technology.

‘Possible reserves’ belong to the third class. They have a 10 per cent chance of being

exploited in the future and require very favourable conditions for that to occur. Political

or commercial aims may lead to pressure to underestimate or overestimate the oil

reserves in some countries. Consequently, an accurate estimate of oil reserves is very

15

difficult to produce. The useful reserve depends on the price that the consumer is ready

to pay. Some oil resources may be interesting to exploit only if the price is above a

certain value (Ngo & Natowitz 2009).

The present annual consumption of fuel is larger than the amount that is newly

discovered each year. This situation has existed since the 1980s. Currently, about 80 per

cent of oil being recovered comes from deposits discovered as early as 1973. Some

experts believe that we have revealed 90 per cent of the conventional crude oil and that

production will soon begin to decay. This is based on an extension of studies originally

undertaken in the 1950s by King Hubbert, a geophysicist working at the Shell Oil

Company. Hubbert’s estimation was based on the pattern of discovery and depletion of

crude oil, two events similar in the nature but swinging in time. In 1956 he predicted

that the oil production in the US would peak in 1969 and decline afterward. This

forecast proved to be very accurate, since in reality production began to decline in 1970.

The basic idea of Hubbert’s model is that the exploitation of any limited resource

follows a curve as shown in Figure 2.2. The maximum of the curve (the peak) is

reached when the resource is half exploited.

There is a debate over oil reserves and there are basically two opposite schools

of thought, the pessimists and the optimists. The pessimists think that the oil

peak will soon be reached ( 2010 or even before). The optimists say that the

peak will not occur before 2030–2040. They claim, in particular, that new oil

discoveries can be made if people search in unexplored areas. Interestingly,

many of these optimists are mostly associated with oil companies while many

of those belonging to pessimistic community are persons retired from the oil

industry (Ngo & Natowitz 2009, p. 32).

16

Source: EcoSystems (2007).

Figure 2.2: US Oil Production and the Hubbert High Estimate for the US

World oil production peaked in July 2008 at 74.82 mbd and in 2009 has fallen to about

71 mbd. It is expected that oil production will decline at about 2.2 mbd per year. After

2010 the resulting annual production decline rate has increased to 3.4 per cent. By 2011,

OPEC will not have the ability to offset cumulative non-OPEC declines and world oil

production is forecast to stay below its 2008 peak (Drum 2009). Table 2.1 below shows

the oil reserves and production peaks for major oil producing countries and the world;

these results were obtained by applying a multi-cyclic Hubbert model (Al-Fattah and

Startzmann 1999). Whether the oil peak occurs in 2010 or 2040 is not the critical point.

The significant point is that even the optimists expect the peak to occur within decades.

Therefore, we will rapidly be confronted with a progressive lack of crude oil and we

must change the way we use oil because the price of this resource will steadily increase

and its availability will shrink.

17

Table 2.1: Oil Reserves and Production in Different Countries

Country

Peak production Reserve

Year Million barrels/day Proven reserve,

billion barrels

Ultimate recovery,

billion barrels

Kuwait 2033 6.579 101.5 138.341

Qatar 2019 1.141 15.207 22.877

Saudi Arabia 2027 13.970 262.3 370.538

UAE 2030 6.726 97.8 122.473

Oman 1998 0.9 5.572 13.469

Algeria 2012 2.202 12.27 26.057

Iran 1974 6.022 136.27 194.981

Iraq 2036 8.494 115.0 144.581

Libya 2023 4.129 41.464 66.074

Nigeria 2017 5.286 36.22 61.085

Venezuela 2028 5.487 80.012 137.324

Brazil 2010 1.981 11.772 20.947

Canada 1973 1.742 16.5 38.582

China 2009 3.816 18.25 48.539

Kazakhstan 2020 5.563 39.62 43.880

Russia 2009 11.153 74.436 137.722

US 1970 9.659 29.9 217.748

OPEC 2026 53.0

Non-OPEC 2006 39.6

World 2014 79.0

Source: Nashawi et al. (2010).

2.2.3 Formation of the GCC and Stock Markets in the GCC Countries

On 25 May 1981, the leaders of the UAE, the State of Bahrain, the Kingdom of Saudi

Arabia, Sultanate of Oman, State of Qatar and State of Kuwait met in Abu Dhabi, UAE,

where they reached a co-operative framework joining the six states to effect co-

ordination, integration and interconnection among the member states in all fields in

order to achieve unity. The GCC Charter states that the basic objectives are to effect co-

ordination, integration and interconnection between member states in all fields,

strengthening ties between their peoples, formulating similar regulations in various

fields such as the economy, finance, trade, customs, tourism, legislation, administration,

as well as fostering scientific and technical progress in industry, mining, agriculture,

18

water and animal resources, establishing scientific research centres, setting up joint

ventures, and encouraging co-operation of the private sector (Gulf 2011).

2.2.3.1 The Bahrain Stock Market

The Bahraini stock market was initiated in 1957 with an initial public offering from the

National Bank of Bahrain. However, it was not until 1987 that the Bahrain Stock

Exchange (BSE) was established by a government decree, with 29 listed companies.

The BSE is self-regulated by its Board, which is chaired by the Minister of Commerce

and Agriculture and joined by members of the Ministry of Finance and National

Economy, the Bahrain Monetary Agency and the Bahrain Chamber of Commerce. The

equity market capitalisation has risen from $2.7 billion in 1989 to $6.6 billion in 2000

and $12.7 billion in 2004. However, with an average daily trading volume of $1.4

million in 2004, it has the lowest trading volume in the GCC region. Currently, 48

companies are listed on the BSE. Through a Prince Decree in 1999, the market was

opened up to foreign investors, allowing GCC nationals to own up to 100 per cent and

non-GCC nationals up to 49 per cent of a local company’s shares. Seven companies

(mostly financial institutions) are open to 100 per cent foreign ownership. While trading

is conducted electronically, an automated clearing, settlement, and depository system

ensures that subsequent settlement procedures are limited to two days. The average

annual market index return for the period 2000–2004 was 5.9 per cent. A GCC market

return comparison is shown in Table 2.2 (Bley & Chen 2006).

19

Table 2.2: GCC Country Stock Market and Economic Characteristics

Characteristic Markets/Economies

Bahrain Kuwait Oman Qatar Saudi

Arabia UAE

Stock trading began 1957 1952 1988 1997 1935 1989

Current market system established 1987 1983 1998 1997 1985 2000

Electronic trading since 1987 1995 1998 2002 1988 2000

Number of companies listed 48 114 128 30 72 49

Average daily trading volume (USD,

millions) 1.4 183.8 10.4 9.9 1931 113.6

Max per cent of foreign investment* 49–100 100 49 25 0–49 49

Market capitalisation in 2005 (USD,

millions) 16,585 91,845 12,617 12,617 573,253 187,008

Length of settlement procedure

(days) 2 1 2 3 Real time 2

Foreign investment through mutual

funds Yes Yes Yes Yes Yes Yes

Stock index future trading No Limited No No No No

Average index return per annum,

2000–2004 (per cent) 5.9 33.5 6.6 28.3 26.5 17.1

GDP per capita in 2003 (USD) 11,310 16,700 8,070 31,400 8,700 19,630

GDP per capita in 2010 (USD) 26,807 38,293 26,197 88,232 23,742 36,973

* Lower limits for GCC citizen investors and upper limits for non-GCC citizens.

Source: United Nation Statistics Division 2011; MSCI Barra (2005); Bley and Chen

(2006).

2.2.3.2 The Kuwait Stock Market

Share trading in Kuwait started in 1952, when the first public shareholding company,

the Kuwait National Bank, was established. In 1983, the Kuwait Stock Exchange (KSE)

was reorganised as an independent financial institution. After the Iraqi invasion of

Kuwait in August 1990, the KSE was closed until September 1992. Securities trading

became more efficient in 1995, with the implementation of an electronic trading and

settlement system. While general settlements between all parties take place every

Saturday, cash balances are available after one day. Other GCC nationals were granted

market access in 2000. Non-GCC investors are not allowed to invest directly in the

Kuwaiti stock market but may subscribe to overseas-based mutual funds trading in

Kuwaiti securities. The Future Financial Investment Service, the first equity futures

20

market in the Arab world, was launched in 1996 for a limited number of stocks. At the

end of 2004, 114 companies were listed on the KSE, with a combined market value of

about $66 billion. Trading activities are dominated by the Kuwait Investment Authority

(KIA) and local commercial banks. With an average annual market index return of 33.5

per cent for the period 2000–2004, the Kuwaiti stock market is the best performing

stock market in the GCC region (Bley & Chen 2006).

2.2.3.3 The Oman Stock Market

The Muscat Securities Market (MSM) was established in 1988 and reorganised as an

automated trading and settlement system in 1998, and has the lowest capitalisation of

the six GCC stock markets. At the end of 2004, the combined value of the 128

companies was only $9.3 billion. The stock exchange is a governmental entity,

regulated by the Capital Market Authority (CMA). Settlement procedures through a

settlement bank with a settlement guarantee fund usually take two days. The Sultanate

of Oman welcomes international investors, and allows up to 100 per cent foreign

ownership. Similar to the other GCC countries, Oman does not levy taxes on dividend

income or capital gains. With investors focusing on the Saudi Arabian and UAE

markets, Omani stocks are still modestly priced, with an average Price/Earnings (PE) of

11 (compared to 28 and 27 for Saudi Arabia and UAE, respectively) at the end of 2004

(Bley & Chen 2006).

2.2.3.4 The Qatar Stock Market

Trading on the Doha Securities Market started manually in 1997, with electronic central

clearing and settlement, and became fully automated in 2002. The market began

operations with 17 companies. By the end of 2004, 30 companies, with a total market

capitalisation of about $35 billion, were listed. The level of industry concentration is

relatively high—financial institutions account for approximately 45 per cent of the total

market capitalisation. Foreign stock ownership is limited to GCC national only, with a

maximum of 25 per cent. All investors are required to conduct trading activities through

one of the ten currently authorised brokers. Over the period 2000–2004, the Qatar

market was the second best performer (after Kuwait), with an average annual index

return of 28.3 per cent (Bley & Chen 2006).

21

2.2.3.5 The Saudi Arabian Stock Market

The Saudi stock market is by far the largest, with a market capitalisation of $237.1

billion at the end of 2004. An average daily trading volume of $1.93 billion also makes

Saudi Arabia the most active of the GCC stock markets. Stock trading began in 1935,

with the Arabian Automobile Company, but remained irregular until the privatisation of

several electricity companies in the early 1960s. The bulk of the currently listed

companies were offered to the public in the late 1970s and early 1980s. Market

activities, however, remained largely unregulated until 1985 when the Saudi Arabian

Monetary Authority (SAMA) exclusively authorised 12 domestic commercial banks to

act as brokers. Electronic trading and settlement systems were implemented in 1988. In

2003, the Saudi CMA was established to regulate and develop the Saudi Arabian capital

markets. The Saudi Arabian stock market is closed to foreign investors, including other

GCC country nationals. Several offshore mutual funds, however, provide an investment

alternative to direct stock ownership (Bley & Chen 2006).

2.2.3.6 The United Arab Emirates Stock Market

Founded in 2000, the combined market capitalisation of Dubai and Abu Dhabi ($67.1

billion) has already elevated the UAE stock market to the second largest stock market in

the GCC region, closely followed by the Kuwaiti stock market ($65.9 billion), which

was established more than 20 years ago. With Dubai becoming the financial and

economic hub of the Gulf region, most investors in UAE securities focus on the Dubai

Financial Market, which already accounts for about three quarters of the country’s total

trading volume. The combined average daily trading volume of $114 million is

concentrated on ten of the 49 listed companies. About half of the listed companies allow

non-GCC stock ownership of up to 49 per cent. The Emirates Securities and

Commodities Authority is the regulatory body that supervises all aspects of financial

market activities and reports to the Ministry of Economy and Commerce (Bley & Chen

2006).

22

2.3 Oil and Stock Market Volatility

The two parts of this thesis on oil and stock market returns are undoubtedly linked, as

oil price shocks influence stock prices by affecting expected cash flows and/or discount

rates. Oil price volatility can affect cash flow since oil is an input in production and

because oil price variations can influence the demand for output at both industry and

national levels. Oil price shocks can affect the discount rate for cash flow by influencing

the expected rate of inflation and the expected real interest rate (Miller & Ratti 2009).

Corporate investment decisions can be affected directly by changes in the oil price and

by changes in stock prices relative to book value.

2.3.1 The Importance of Volatility

The modelling of volatility is considered to be important for practitioners and

academics due to its use in forecasting aspects of oil hedging and future returns. These

predictions can then be used in investment, options pricing and hedging, market-making,

portfolio selection and many other financial and economic activities. The forecasting of

volatility is an important duty in oil and financial markets. Experts and academics have

paid considerable attention to volatility, as evidenced by the fact that at least 93

published and working papers study the forecasting abilities of various volatility models.

Many more have been written on the subject of modelling volatility alone without the

forecasting aspect. This reflects the importance of volatility in investment, security

valuation, oil hedging, risk management and monetary policy-making (Poon & Granger

2005).

The basic supposition when modelling volatility is that volatility can be divided into

foreseeable and unforeseeable components, with most research concentrating on the

foreseeable component of volatility. In financial time series, the predictable component

of volatility is motivated by the fact that the risk premium is a function of volatility

(Pagan & Schwert 1990). Therefore, it is essential to understand the determining factors

of volatility because of its relationship to risk. On the contrary, volatility is not the same

as risk. Once volatility is interpreted as uncertainty, it becomes the key input into

investment decisions and portfolio creations. Indeed, volatility is the most important

23

variable in the pricing of derivatives and oil hedging. In addition, it is possible to buy

derivative securities that are created on volatility itself. In order to price these

instruments, accurate measures of volatility are needed (Poon & Granger 2003).

Financial risk management has become serious for many financial bodies around the

world, with the introduction of the first Basel Accord in 1996. The Basel Accord makes

it obligatory for financial institutions to forecast stock price volatility in order to set

aside reserve capital against their value-at-risk. Therefore, the measurement of volatility

is important for all financial institutions (Bakry 2006).

The main reason why there is concern over oil and stock market volatility is the belief

that it can adversely affect real economic activity. The possibility that oil and financial

market volatility have such wide consequences for the economy as a whole obviously

requires a greater consideration of the volatility process. It is then imperative for

politicians to find solutions to reduce volatility.

2.3.2 The Efficient Market Hypothesis

The EMH is founded on the concept that in an efficient capital market, prices adjust

fully and instantaneously to all available relevant information. According to Fama

(1970) there are three forms of market efficiency. Each form depends on the subgroup

of information that is included in the information set and thus is reflected in stock prices.

The weak form of market efficiency proposes that current stock prices reflect all

information contained in earlier stock prices. Therefore, it is not possible for a

shareholder to consistently earn abnormal returns from trading based on historical prices

alone. The strong form of market efficiency suggests that current stock prices reflect all

available information; this includes all private and public information (Bakry 2006).

Additionally, the fact that stock market prices are assumed to reflect all available

information at any given time means that the current price of an asset should be an equal

estimate of the intrinsic value. Thus, market participants will have differing opinions as

to the intrinsic value of assets, causing asset prices to hike around their intrinsic value.

Competition among shareholders makes sure that any price differences will not be large

enough to be used profitably. To be precise, stock market prices ‘random walk’ so that

market prices adjust instantaneously to new information as soon as it becomes available

in order to ensure that only competitive rates of return are likely to be earned (El-Erian

24

& Kumar 1995). The random walk model is constructed on two basic theories. The first

is that sequential values of returns on an individual stock are independent, and the

second is that stock returns conform to some probability distribution. This thesis focuses

on the first theory; the main issue being whether sequential stock price changes show

any systematic patterns—that is, volatility clustering—or whether they are vague from

the viewpoint of the random walk model.

Fama (1965) tested the applicability of the random walk model in the US capital market

using the daily closing prices of the 30 stocks comprising the Dow Jones industrial

average covering a five year period. Solnik (1973) tested the random walk model in

European markets using 234 securities from eight major European stock markets: the

UK, France, Germany, Italy, The Netherlands, Belgium, Switzerland and Sweden. Fama

concluded that although there were slight dependencies, these did not violate the

conditions of market efficiency, while Solnik found that the European capital markets

are less efficient than the US capital market. The inefficiencies in the European markets

may be explained by market thinness, loose requirements for disclosure of information

and lack of control on insider trading.

Conrad and Juttner (1973) studied stock price conduct for Germany using daily closing

prices of 54 stocks for the period from January 1968 to April 1971. They did not find

any provision for the random walk process, and the runs test rejected the random walk

hypothesis. The serial correlation coefficients for most stocks were significantly greater

than zero. This conclusion is in contrast to the results of Solnik (1973), who found a

much lower coefficient for Germany. Conrad and Juttner (1973) argued that the failure

of the random walk model to describe the conduct of stock prices in Germany might be

due at least in part to three specific issues. First, there is the problem of the close

connection between business cycles. Second, there is the failure to weigh the available

information concerning the economic factors that determine company profits. Third,

during the period under consideration, there was a psychologically determined

overestimation of good, along with bad, news.

In 1992, Butler and Malaikah studied the efficiency of stock markets in Kuwait and

Saudi Arabia using daily data on 35 Saudi stocks from June 1986 to September 1989,

and 36 Kuwait stocks from October 1985 to December 1988. They used serial

25

correlation and runs tests to inspect the nature and degree of serial dependence. The

mean lag one autocorrelation coefficient for Kuwait stocks was found to be 0.053, with

36 per cent of the stocks having a statistically significant lag one autocorrelation

coefficient at the 95 per cent confidence level. In contrast, all 35 Saudi stocks were

found to display negative and statistically significant autocorrelation, with a mean

coefficient equal to –0.471. This is opposite in sign and larger in magnitude compared

with the autocorrelation coefficients described in other studies. Inspecting the

information and operational inefficiency in Saudi Arabia, Butler & Malaikah (1992)

identified trading delays, illiquidity, market fragmentation and the absence of official

market makers as major factors contributing to inefficiency.

Taken together, the experimental evidence suggests that stock returns in developed

markets appear to be consistent with the random walk model. Conversely, the random

walk model does not appear to be effective in emerging markets, although the outcomes

of these studies are by no means conclusive. As the random walk model infers that

stock prices cannot be forecast, an added common method for examining the validity of

the EMH is identifying predictable volatility patterns in stock prices. The most popular

volatility patterns studied include seasonal effects and volatility clustering. Generally,

there are many ways in which stock market volatility and market efficiency are

connected. This makes volatility a vital phenomenon to research.

2.3.3 Stylised Facts about Oil and Financial Market Volatility

Volatility practice is concerned with the development of the conditional variance of the

stock or/and oil price return over time. This is a topic of interest because, as shown in

Figure 2.3, the variability of the Standard and Poor’s Composite 500 (S&P 500 USA)

returns1 varies over time and appears in clusters (Tsay 2005). In comparison, Figure 2.4

shows the Europe Brent Oil Price.

1 Index return is calculated as the natural log difference in the closing sector index between two dates,

being the sector index at time . This is

26

Source: Own Results, DataStream.

Figure 2.3: S&P 500 USA Index Return

Source: US Department of Energy, Energy Information Administration.

Figure 2.4: Europe Brent Oil Price

There are numerous noticeable features about financial time series and financial market

volatility that have been well described in the large volume of literature examining

volatility. The key characteristics of volatility observed over time include fat tail

distributions of risky asset returns, volatility clustering, asymmetry, mean reversion and

co-movements of volatilities across assets and financial markets (Tsay 2005). In their

research Poon and Granger (2003) found that that the correlation between the volatility

of the return on assets is stronger than the correlation between the return on assets.

However, both are inclined to increase during bear markets and financial crises.

-24

-20

-16

-12

-8

-4

0

4

8

12

S & P 500 Composite-USA Index Return (1964-2009)

0

20

40

60

80

100

120

140

160

88 90 92 94 96 98 00 02 04 06 08 10

D a i l y E u r o p e B r e n t S p o t ( 1 9 8 7 - 2 0 1 1 )

27

First, it is well known that the unconditional distribution of asset returns has a heavy tail.

Typical kurtosis estimates range from 4 to 50, indicating extreme non-normality (Engle

& Andrew 2001). A second noticeable feature is the clustering of large and small

movements, of either sign, in the price process. This was one of the first recognised

features of the volatility process of asset prices. Mandelbrot (1963) and Fama (1965)

both reported indications that large variations in the price of an asset are often followed

by other large variations, while small variations are followed by small ones. This

conduct has been reported in many studies (see e.g., Schwert 1989; Brooks & Lee 1997),

and such ‘clustering’ can be seen in Figure 2.3. The implication of volatility clustering

is that volatility shocks today will influence the expectation of volatility several periods

in the future. Third, it is also accepted that volatility is mean reverting. It has been

shown that the level of volatility usually reverts to its expected level, and most experts

agree that volatility forecasts should converge to the same normal level of volatility.

Also, mean reversion in volatility implies that current information has no effect on the

long-run forecast (Engle & Andrew 2001). Fourth, innovations may have an asymmetric

impact on volatility. However, in the case of equity returns, the effects of positive and

negative shocks have different impact on volatility. It has usually been found that

negative price shocks have a stronger effect on volatility than similar positive shocks.

Many researchers, starting with Black (1986), have shown that stock returns are

negatively correlated with changes in returns volatility (see e.g., Christie 1982; Nelson

1991; Glosten, Ravi & Runkle 1993). That is, volatility increases with bad news but

decreases with good news. This form of asymmetry has been attributed to the leverage

effect, where at the same time as the price of a stock falls, its debt-to-equity ratio rises,

increasing the volatility of the returns to stockholders.

As well, volatility may be influenced by exogenous variables. Additional variables may

contain relevant information for the volatility of a series. This may include company

announcements, macroeconomic announcements and time of day effects (Engle &

Andrew 2001). Furthermore, even though it is generally accepted that volatility is mean

reverting, other characteristics of the volatility process include the fact that volatility

generally evolves over time in a continuous manner. This implies that volatility does not

show evidence of jump diffusion; that is, volatility jumps are rare (Bakry 2006).

28

2.3.4 Standard Cash-Flow/Dividend valuation Model

Detailed investigation of the reaction of stock market to oil shocks shows that stock

prices rationally reflect the impact of news on current and future real cash flows. We

find no evidence of fads and/or market over-action. While the Canadian stock market

appeared to react rationally to oil shocks, the experiences of Japan and the United

Kingdom were different (Jones and Kaul 1996).

Oil Shocks and the Rationality of the Stock Market: to tests of whether stock prices

react rationally, or overreact, to changes in oil prices using the standard cash-

flow/dividend valuation model. Following (Campbell 1991), the log real return on a

stock in period , can be expressed as see also (Campbell and Shiller 1988).

(2.3.1)

Where denotes the expectation formed at time is the log of the real cash flow in

period , and is a parameter close to but less than one. Equation (2.3.1) simply states

that stock returns vary through time due to changes in expected and unexpected returns.

The unexpected return in period has two sources of variation: (a) changes in current

and expected future cash flows (given by the second term on the right hand side of

equation (2.3.1)), and (b) changes in expected future returns (the last term in equation

(2.3.1)) (Jones and Kaul 1996).

2.3.5 Definition of Volatility and its Measurement

Volatility stands for the spread of all likely outcomes of an uncertain variable, which as

far as financial markets are concerned is the spread of asset returns. Statistically,

volatility is often measured as sample standard deviation, , or variance, (Peon

2005). The unconditional population mean for the random variable return, is:

(2.3.2)

29

The unconditional variance is:

(2.3.3)

The sample variance is estimated as:

(2.3.4)

Here is the sample mean return and is the sample size. The sample standard

deviation statistic is a distribution-free parameter representing the second moment

characterstic of the sample. Only when is attached to a specific distribution can the

required probability density and cumulative probability density be derived analytically

(Poon & Granger 2003). Moreover, the predictable component of volatility in a series is

the conditional variance of that series,2

t. The conditional variance is:

(2.3.5)

Where is the information set at time and is the conditional mean return.

The study of stock market volatility has appeared earlier as empirical models of the

relationship between risk and return. The models of Sharpe (1964), Lintner (1965) and

Mossin (1966) have shown that asset price is directly related to either its own variance,

or to the covariance between its return and the return on a market portfolio. Therefore,

volatility models have been established based on the association of variance with risk,

and the fundamental interchanges between risk and return. Hence, it is common for

studies to use the variance of asset returns as proxies for market volatility.

Using the standard deviation as a proxy for market volatility, Goetzmann and Philippe

(1999) studied the performance characteristics of emerging and developed markets to

gain an additional understanding of the developed versus emerging markets. They found

that the average standard deviation of dollar returns for the emerging markets sample

was 34.8 per cent, but the average standard deviation of dollar returns for the developed

markets was only 19.8 per cent.

30

Despite the ease of use of the standard deviation as a substitution for market volatility,

there are obvious limitations. The most common of these identified in the literature is

that the standard deviation ignores relevant information that affects the random process

generating the variable in question (Engle 1982), and it distorts the volatility pattern due

to smoothing (Bini-Smaghi 1991). Also, the existence of heteroscedastic errors is a

serious problem in cross-sectional and time series data (Darrat & Mahmoud 2002). For

these reasons the development of stronger measures of volatility was necessary.

Exapnding the standard deviation measure, Shiller (1979, 1981) and LeRoy and Richard

(1981) were pioneers in researching volatility and developing volatility tests (known as

variance bound tests). These volatility tests were based on the dividend discount model

and decomposed the variance of the market price and forecast error. If the variance of

stock prices was bigger than the ex-post present values, then the variance bound was

violated. If a variance bound violation existed, then this was regarded as evidence

against the EMH.

The accumulated studies of stock market volatility have advanced in terms of the

complexity of the methods used to model volatility. The basic volatility measure of

standard deviation has been replaced by more advanced econometric techniques built on

modelling of the conditional variance. These conditional heteroscedastic models can be

categorised into two general classes. The first class, known as the ARCH class of

models, specifies an exact function to govern the evolution of over time. The second

class uses a stochastic equation to describe . The stochastic volatility (SV) model

belongs to this category (Tsay 2002).

2.4 Causes of Volatility in the Oil and Stock Markets

2.4.1 Causes of Volatility in Oil Prices

The oil manufacturing industry was established with the drilling of the world’s first oil

well in 1859 in Titusville, Pennsylvania. Ever since then, the supply of petroleum has

always been uncertain and demand robust and inelastic. Therefore, price volatility has

always been a fact of life in the oil industry (Lynch 2003). The volatility of crude oil

prices generates vagueness, and therefore an unstable economy for both oil exporting

and oil importing countries. Higher prices result in an increase in inflation and a

31

consequent recession in oil consuming nations, as oil prices are negatively correlated

with economic activities (Ferderer 1996). Record post-war recessions were preceded by

oil price shocks, such as the 1974, 1980 and 1990 economic recessions in the US

(Huntington 1998). Signs that a recession depresses prices have occurred as recently as

1998, when oil prices declined to approximately $10 a barrel as a result of the Asian

economic setback that began in 1997. Lower oil prices inhibit economic development

and may generate political instability and social unrest in some oil producing countries.

The sharp fluctuations in oil prices have been remarkable over the last three decades

(Yang et al. 2002). Other researcher (Alsahlawi 2009) argued that oil prices are affected

more by inflation than by the exchange rate value of the dollar, however, OPEC

members would recognize significant financial increase if their exports were not

controlled in US dollar. An alteration from US dollar pricing would improve non- US

trade by exporting in currencies dependable with currencies used for imports purchases

and by moving away from the US dollar, which is perhaps artificially supported in its

role as a trade exchange intermediate. Thus, it is of great importance to analyse the

important factors affecting the volatility of crude oil prices.

Certain facts about the oil industry are widely accepted by academics and researchers.

First, the short-term demand and supply of crude oil is tremendously inelastic to price

changes. Oil prices have traditionally been both highly volatile and subject to

exogenous supply shocks from natural disasters and political events. Second, the

demand for oil products is expected to increase for the predictable future, driven by

demand from emerging economies. The world aggregate oil demand in the year 2004

was 82.3 mbd, compared to 79.4 mbd in 2003. Third, information on crude oil demand,

supply and inventory levels is neither timely, nor reliable. Moreover, as a consequend of

this, reported figures for demand, supply and inventory can be, and often are, revised,

occasionally more than a year after the fact. In addition, the quality of data varies

considerably across regions (Lynch 2003). Fourth, crude oil is not a tangibly quantity.

There is significant variation in quality and therefore price among the standard baskets.

The West Texas Intermediate (WTI) is considered as the highest quality basket,

followed by the Brent Blend, and the Dubai Blend. Fifth, high oil price will also

encourage the development of high cost regions and the move to renewable forms of

energy which will extend the life of oil reserves and deferral its depletion. With lower

oil price, oil supply will decline and will be less projected availability of oil. According

32

to this argument, the future prospect of oil supply is mostly determined by price drifts.

Away from the complexity of oil industry, the new technologies in exploration and

production will allow more oil to be found and produced at even lower cost (Alsahlawi

2010).Finally, there are several groups of players with various degrees of cartelisation,

each with significant market power in the upstream or downstream segments of the oil

industry. The main upstream producers can be broadly classified into OPEC and non-

OPEC producers. Further downstream are the oil multinationals like Exxon-Mobil and

Shell. The world-wide consumer market can be classified as OECD (the Western

nations) and non-OECD (Mudumbai 2005).

Some innovative theories that have been proposed explain the increased volatility of oil

prices are as follows: first, organic demand growth. According to this theory, the strong

GDP growth rates of the Asian developing economies (mainly China and India) has led

to a growing demand for oil products, and this trend is likely to continue. Until supply

capacity can be expanded to cope with this demand growth, the oil market is likely to be

tight and high prices levels and price volatility are to be expected. A second theory

implicates oil industry practices, such as recent advances in computerised inventory

management, which have led numerous industries, including the oil majors, to the

notion of just-in-time inventory. Publicly traded companies have a strong motivation to

minimise their inventories in order to increase their profitability and shareholder value.

Conversely, fewer inventories automatically mean more price volatility. A third factor

may be speculation in futures markets. One follower of this theory, Krapels (2011), has

suggested that trading in financial instruments such as oil futures contracts, while

increasing liquidity and potentially improving efficiency, also leads to greater volatility.

It is thought that trading activity in oil derivatives accounts for a substantial part of the

recently observed volatility. A fourth factor is good old-fashioned greed. Numerous

observers, particularly in the popular press, attribute high oil prices directly to price

fixing by the participants. This is theoretically possible because of the presence of

formal (OPEC) and informal (the oil multinationals) cartels. Finally, another school

suggest that the peak oil may also contribute to volatility. This school of thought is

based on the belief that the world oil supply is controlled for severe shocks, with the

most productive reserves reaching their peak production capacity and going into decline.

33

This section has presented a range of explanations and current thinking on the causes of

volatility in crude oil prices. It appears that all of these theories have a certain credibility

in the marketplace; however, a reminder is necessary here—the fact that the market

cares only about the participant’s perceptions, not about the truth.

2.4.2 Causes of Volatility in the Stock Market

One of the key things that can be concluded from the literature on stock market

volatility is that there is a signal for high volatility. However, it is impossible for policy-

makers to set effective policies to reduce volatility without a clear understanding of its

causes. Unfortunately, very little is known about the elements that determine volatility.

Consequently, there is no well-accepted general structural model of volatility. The most

common clarification is that of the EMH, where the volatility of prices is directly

related to the rate of flow of information to the market (Ross 1989). Schwert (1989)

examined whether microeconomic and macroeconomic factors can explain stock market

volatility. He found that stock market volatility is not closely related to the volatility of

economic variables such as inflation, money growth or industrial production. While

financial leverage and trading activity seem to be related to stock price volatility, they

can only explain a small proportion of the change in stock volatility over time.

Other common factors used to explain stock market volatility include market

characteristics, such as asset concentration, market development, market integration,

and market microstructure (Bakry 2006). The following briefly discusses these

characteristics. First, the degree of asset concentration denotes the amount of

diversification and concentration in particular market indices for each country. If

economic activity is spread evenly through different sectors of the economy, the

volatility changes in each of the industries are likely to cancel each other out, therefore

resulting in less volatility in the aggregate market index (Bekaert & Harvey 1997).

Second, the development of a market also contributes to stock market volatility. It is

practically expected that a somewhat new market will be more volatile than a long-

established market, since the average experience and skills of the investors and market

regulators are likely to improve with market development (Cornelius 1993). For

example, as markets operate and market microstructure develops, emerging stock

markets are likely to become more efficient.

34

Market integration speaks of the independence between stock markets; that is to say

how much a local stock market is reliant on changes in foreign stock markets. When a

stock market is exposed to international financial investment, then the local stock

market volatility will be affected by the dealings of the foreign stock market. Thus,

world market features affect the volatility of local markets if markets are integrated.

Bekaert and Harvey (1997) have debated this, proposing that the higher volatility and

returns in emerging markets are due to local factors preceding the emergence and the

integration with international markets. In investigating the effect of local factors on

stock market volatility, Bekaert and Harvey (1997) discovered that local factors were

important factors of volatility and credit risk was the most dominant local factor

contributing to stock market volatility. Other researchers such as Erb, Campbell and

Tadas (1995) and Diamonte, John and Ross (1998) have also made similar findings.

Finally, market microstructure has been shown to be a major factor contributing to

market volatility. Market microstructure refers to the regulations and structures that rule

how the market operates. For example, Mecagni and Maged (1999) examined the

implications of the introduction of circuit breakers on the Egyptian Stock Market and

found that pricing limits imposed distortions in pricing, rather than helping to reduce

volatility. This finding is consistent with the view that market microstructure can

actually increase volatility.

In order to be able to predict stock market volatility, it is vital to have a good

understanding of the factors that determine it. Many common factors contribute to

volatility in both developed and emerging stock markets. Experience suggests that

emerging markets involve greater volatility than developed markets, and it will become

clear that each of these factors is potentially more noticeable in emerging markets. In

addition, emerging markets are subject to further factors that may contribute to market

volatility. A number of studies have examined this phenomenon and offered

explanations for the greater volatility experienced in emerging markets.

First, the accessibility and release of information in emerging markets is less than that in

developed markets, which causes greater volatility. Second, firms in emerging markets

have less investment research than firms in developed markets. This has been due to the

fact that data was either absent, or of doubtful reliability in emerging markets. Third, El-

35

Erian and Kumar (1995) proposed that the structural and institutional features of

emerging markets are usually split and therefore less efficient in detecting and

discriminating between investment opportunities. In brief, many potential and actual

deficiencies, such as poor information quality and disinterested shareholders, create

inefficiencies, even in the most researched and regulated stock exchanges. However,

these deficiencies are usually more noticeable in emerging markets, and this potentially

increases the probability of higher stock market volatility in emerging markets.

Overall, the factors contributing to market volatility relate to how fast the market

processes shocks and integrates relevant information into prices. The speed at which

financial transactions occur leads to changes in market volatility. Consequently, the

period of increased volatility due to the arrival of new information is shorter. In an

efficient market, volatility should show rapid mean reversion (Cunado, Javier &

Fernando 2004). Even though current theories modelling stock market prices are

incomplete, empirical models have been developed based on econometrics and these

have been used to analyse volatility in financial phenomena generally, and stock market

volatility specifically. These two empirical models are the GARCH model and the SV

model.

2.5 ARCH/GARCH Class Conditional Volatility Models

Bera and Higgins (1993) and Bollerslev, Chou and Kroner (1992) reviewed a group of a

widely used time series models of the ARCH class. ARCH class models make use of

sample standard deviations but formulate the conditional variance of time

series via a maximum likelihood (ML) technique.

According to Engle (1982) the first example of an ARCH model is the ARCH (q) where

is a function of lagged past square residuals. In GARCH (p,q) extra dependencies are

permitted on p lags of past realisations of the variance. The GARCH is a more frugal

model than ARCH, and GARCH (1,1) is the most common structure for many financial

time series (Poon & Granger 2003).

36

2.5.1 The ARCH Model

The first model that delivered a systematic framework for volatility modelling was the

ARCH model of Engle (1982). The key idea behind ARCH modelling is the following:

the forecast, based on past information, is presented as a conditional expectation

depending upon the values of past observations. Therefore, the variance of such a

forecast depends on past information as well, and may therefore be a random variable

(Gileva 2010). The basic evidence is that the mean asset return is serially uncorrelated,

but is dependent. This dependence is usually modelled as a simple quadratic function of

its lagged values (Tsay 2002).

The ARCH process enacts an autoregressive structure on the conditional variance that

allows volatility shocks to persist over time. It can therefore allow for volatility

clustering. The general form of the model, represented by ARCH (q), is:

(2.5.1)

(2.5.2)

(2.5.3)

(2.5.4)

Where:

is the dependent variable;

is a vector of explanatory variables;

is a vector of regression parameters;

is the conditional disturbance which is normally distributed with a mean of

zero and variance ;

is the information set conditioning the disturbance; and is the time

index.

37

The conditional variance, , is parameterised as a function of the information set,

which normally comprises the previous innovations, To ensure the conditional

variance is positive, an inequality restriction must be imposed on the variance equation

(2.5.3):

and

To ensure that the process is stationary, it is also required that:

The right side of Equation 2.5.3 contains two components, the expected volatility and a

random component, . The expected volatility of in Equation 2.5.3 is further

divided into two components, the time-varying component in the summed lagged terms

and the mean variance , to which the time-varying component reverts. That is, is a

stationary process.

A problematic issue with Equation 2.5.3 is that large values of q often lead to the

violation of the non-negativity and stationary conditions. The GARCH model pioneered

by Bollerslev (1986) is a solution to this problem and can also provide a more frugal

specification. The core advantage of this model is that it takes into account the fact that

conditional variance is significantly affected by the squared residual term (which may

be a result of significant changes on a market) in any of the previous periods.

Consequently, this approach allows capturing the conditional heteroscedasticity of

financial data and provides an explanation of the persistence in volatility. On the other

hand, this model assumes that positive and negative shocks have the same effects on

volatility because they depend on the square of the previous shocks. In practice, it is

well recognised that price of a financial asset responds in a different way to positive and

negative shocks. Further, the ARCH model does not provide new insight into our

understanding the source of variation in financial time series; rather it simply provides

an automated way to describe the behaviour of the conditional variance and gives no

indication about the causes of such behaviour. In addition, ARCH models are likely to

38

over-predict volatility because they respond slowly to large isolated shocks to the return

series (Gileva 2010).

2.5.2 The GARCH Model

In response to large number of parameters required to precisely model the ARCH

process, Bollerslev (1986) generalised the ARCH process by allowing the conditional

variance to be a linear function of p lagged conditional variances in addition to q past

squared errors. The GARCH (p,q) model suggests the following form of the conditional

variance :

(2.5.5)

The conditional variance equation given in Equation 2.5.5 is a function of three terms:

is a constant term;

(The ARCH term), which is news about volatility from the previous

periods, and is measured as the lag of the squared residual from the mean

equation; and

(The GARCH term), which is the variance from previous periods.

To make sure the conditional variance is positive an inequality restriction must be

imposed on the variance equation, Equation 2.5.5:

To make sure that the process is stationary, it is also required that:

39

It is easy to show that the GARCH specification is parsimonious and requires fewer lags.

Taking the GARCH (1,1) specification:

A valuable exploration in the GARCH outline is to find persistence in variance. That is,

how permanent is a shock to volatility (Lamoureux & Lastrapes 1990). This is

calculated by the sum of parameters in the variance equation; that is,

(2.5.6)

The closer the sum of Equation 2.5.6 is to unity, the larger the persistence of shock to

volatility. A related characteristic to the concept of persistence is the concept of half-life

(HL) of volatility shocks, which calculates the number of days over which a shock to

volatility diminishes to half its original size. HL is measured as:

(2.5.7)

Where is the sum of the GARCH parameters in the variance equation, Equation 2.5.5.

Despite the fact that GARCH models with a conditional normal distribution permit

unconditional error distributions to be leptokurtic2, they might not fully describe the

high level of kurtosis3 in experimental distributions of return series.

2 A distribution with kurtosis exceeding 3 is called leptokurtic, or more simply heavy-tailed. The

kurtosis of a distribution is a measure of how much mass is in its tails and therefore is a measure of how

much of variance of Y arises from extreme values. Kurtosis = . If the distribution has a

large amount of mass in its tails, then some extreme departures of Y from its mean are likely (Stock &

Watson 2007).

3 The skewness of a distribution provides a mathematical way to describe how much a distribution

deviates from symmetry. Skewness = , where is the mean of Y and is the

standard deviation of Y. For a symmetric distribution, a value of Y a given amount above its mean is just

40

It can be concluded that the ARCH model introduced by Engle (1982) formulates time-

varying conditional variances in time series. The GARCH model delivers a more elastic

basis to capture various dynamic structures of conditional variance. This is due to the

GARCH model integrating the time-varying conditional variance and the covariances of

the stochastic process. Consequently, the conditional variance of the time series depends

upon the squared residuals of the process, which is the square of the lagged innovation.

It can thus be considered as a reduced formula of a more complicated dynamic structure

for the time-varying conditional second order moments. In implementation, GARCH

models have gained approval because they often give a realistic fit to financial data and

can clarify some of the stylised facts. Still, the model encounters the same weaknesses

as the ARCH model. For instance, it responds equally to positive and negative shocks.

Regardless of their limitations, the superiority of the ARCH and GARCH models has

been demonstrated by Pagan and Schwert (1990) and Pagan (1996), who show that the

GARCH models perform well in comparison with alternative methods for modelling

conditional volatility of stock returns. A GARCH (1,1) model is sufficient to account

for the volatility dynamics of financial time series.

2.6 Multivariate GARCH Models

This section will generalise the univariate volatility models to the multivariate one and

explain some methods for simplifying the dynamic relationships between the volatility

processes of multiple market returns. Multivariate volatilities have several significant

financial applications. As revealed in the literature, the multivariate volatility models

play in an important role in portfolio selection and asset allocation, and they can be used

to measure the value at risk of a financial position consisting of multiple assets.

The main examinations of the constancy of the dependence parameters were grounded

on examining the equality of linear correlation coefficients computed before and after a

as a value of Y the same amount below its mean (Stock & Watson 2007). The skewness value can be

positive or negative, or even undefined. Qualitatively, a negative skew indicates that the tail on the left

side of the probability density function is longer than the right side and the bulk of the values (including

the median) lie to the right of the mean. A positive skew indicates that the tail on the right side is longer

than the left side and the bulk of the values lie to the left of the mean. If the skewness of a symmetric

distribution is zero, this indicates that the values are relatively evenly distributed on both sides of the

mean.

41

boom. This methodology has been found to be ambiguous, as conditioning the

estimation of the correlation co-efficient on the sample period prompts an estimator bias.

Consequently, most tests of the constant correlation theory have been based on

estimating the joint dynamics of stock market returns and then explaining how

conditional correlations vary over time. There are a number of ways to model the joint

dynamics of a number of series. The most extensively used approach is the multivariate

GARCH (MGARCH) model. The dominant idea behind the first generation of these

models was that covariance had to be modelled using the same type of specification of

variances as those in the multivariate GARCH model (Kraft & Engle 1982; Bollerslev

et al. 1992).

MGARCH features have been recommended for modelling asset interdependence and

the dynamics of volatility and covariance/correlation (Gregoriou 2009). The application

of MGARCH models is very broad. Some of the important applications are portfolio

optimisation, pricing of assets and derivatives, volatility transmission, non-linear

programming, computation of the value at risk, futures hedging and asset allocation,

estimation of systemic risk in banking, determination of leverage, estimation of the

volatility impulse response function, hedging the currency exposure risk, computing the

minimum capital risk requirements for portfolio of assets, determining misspecification

tests for MGARCH models, modelling of the changing variance structure in an

exchange rate regime and applying MGARCH models in the analysis of individual

financial markets (Minović 2009).

Several methods have been used to generalise univariate volatility models to the

multivariate one, but the problem of dimensionality rapidly becomes a major hindrance

in these applications because there are k(k+1)/2 numbers in the conditional covariance

matrix for a k-dimensional return series. To demonstrate, there are 15 conditional

variances and covariances in the conditional covariance matrix for a five-dimensional

return series. The objective of this section is to introduce some quite simple multivariate

volatility models that are valuable in so far as they remain manageable in real

applications. Specifically, this section will discuss some models that allow for time-

varying correlation coefficients between asset returns. Time-varying correlations are

useful in finance. For example, they can be used to estimate the time-varying beta of the

market model for a return series. First, we start by explaining the Diagonal VEC

42

(DVEC) model of Bollerslev, Engle, and Wooldridge (1988), then to guarantee the

positive-definite constraint, Engle and Kroner (1995) have proposed the BEKK model,

which will be discussed below.

2.6.1 MGARCH-DVEC

In order to reduce the number of parameters, Bollerslev, Engle, and Wooldridge (1988)

have proposed the DVEC model, in which the matrices and

are all taken to be

diagonal matrices: each element of the covariance matrix only depends on

the corresponding past elements and :

(2.6.1)

Where is a positive definite and symmetric matrix;

and

are symmetric matrices; here present the ARCH term while

present the GARCH term and denotes the Hadamard product.

This model has a natural interpretation, because covariances as well as variances have a

GARCH-type specification (Jondeau et al. 2007). In addition, it reduces the number of

unknown parameters considerably to:

; is the number of series or markets returns examined.

Example: in the case this model reduces to nine unknown

parameters:

43

Where

2.6.2 MGARCH-BEKK

Engle and Kroner (1995) presented different MGARCH models with variations to the

conditional variance-covariance matrix of equations. This study employs the BEKK

method, whereby the variance-covariance matrix of equations depends on the squares

and cross-products of innovation, , and volatility, , for each market lagged one

period (Worthington & Higgs 2004).

(2.6.2)

Where is a constant positive definite symmetric matrix, the elements of

the symmetric matrix measure the degree of innovation from market to market

, and the elements of the symmetric matrix indicate the persistence in

conditional volatility between market and market (Jondeau et al. 2007).

The specification involves

unknown parameters, so for example,

the total number of unknown parameter is 24.

44

Where

is a constant positive definite symmetric

matrix, while

are the matrices transposing for

respectively.

The main advantage of this specification is that the conditional covariance matrix is

positive definite as long as also is.

The ML method is used to estimate the parameters of the model; the likelihood function

is maximised with respect to the unknown parameters of elements of the

symmetric matrix that is to be estimated. Engle has shown that ordinary least

squares (OLS) estimation does not give superior results to ML, which is asymptotically

superior and more efficient. Engle and Kroner (1995) and Kroner and Ng (1998) stated

that the BEKK and DVEC systems can be estimated using the full information ML

method. The log-likelihood function of the joint distribution is the sum of all the log-

likelihood functions of the conditional distribution, that is, the sum of the logs of the

multivariate normal distribution.

45

Letting be the log-likelihood of observation be the number of stock exchanges

and the joint log-likelihood:

(2.6.3)

A numerical procedure, such as the BHHH algorithm, is often used to maximise the log-

likelihood function. The ML estimate is then applied to obtain the estimate of unknown

parameters. In this study, I have chosen the first derivative method of Marquardt as the

optimisation algorithm. The Marquardt algorithm is a modification of the BHHH

algorithm. The starting values of the parameters in the mean equations and constants in

the conditional variance-covariance equations are obtained from their corresponding

univariate GARCH models by a two-step estimation approach. This statistic is given by

(2.6.4)

Where , the sample autocorrelation at lag , is calculated from the noise terms and

is the number of observations, is asymptotically distributed as with

degrees of freedom and is the number of explanatory variables. The test statistic in

Equation 2.6.4 is used to test the null hypothesis that the model is independent of the

higher order volatility relationships (Worthington & Higgs 2004).

2.7 Empirical Evidence of Volatility with the ARCH/GARCH and

MGARCH Models

2.7.1 Volatility in Oil Markets

In this section the empirical research examining the volatility of crude oil will be

summarised. Understanding the volatility of oil prices is vital because persistent

changes in volatility can expose producers and consumers to risk, therefore affecting

investments in oil inventories and facilities for production and transportation (Pindyck

2004a). Volatility also limits the value of commodity-based contingent claims; thus, the

behaviour of volatility is important for derivative valuation, hedging decisions, and

decisions to invest in physical capital tied to the production or consumption of natural

gas and oil (Pindyck 2004a). Additionally, Pindyck (2004b) argues that volatility can

46

affect the total marginal cost of production, thus affecting the value of the firms’

operating options and the opportunity cost of current production. The more volatile

crude oil prices become the more uncertainty this creates, leading to economic

instability for both oil exporting and oil importing countries. Higher crude oil prices

contribute to inflation; the result is recession in oil-dependent countries. In addition, oil

prices have a negative impact on economic growth (see e.g., Ferderer 1996; Jimenez-

Rodriguez & Sanchez 2005). Table 2.3 reviews the empirical studies of oil price

volatility.

47

Table 2.3: Summary of Empirical Studies on Oil Price Volatility

Author(s) Study Sample size and

period Estimation method Main findings

Ferderer

1996

Oil prices and

microeconomy

Daily price data

from 1/1/1970–

31/12/1990

VAR model 1. Oil price disruption may

affect the macro-economy not

only due to changes in level

of oil prices but also because

it increases oil price volatility

2. Non-borrowed reserve

growth fell and Federal funds

rates rose following oil price

increases

3. The Federal Reserve raised

the Federal funds rate in

response to oil price increases

by approximately as much as

they lowered it in response to

oil price increases

Huang,

Masulis &

Stoll 1996

Oil futures

contracts, S&P

500, three

individual oil

company stock

price series and

12 stock price

indices

Daily closing

prices for the

nearby oil futures

contract on the

NYMEX for the

period starting

9/10/1979 for

heating oil and

11/4/1983 for

crude oil, through

to 16/3/1990

Multivariate VAR

model

1. Oil futures returns are not

correlated with stock market

returns

2. There is little evidence of

such a link in the prices of

stocks other than those of oil

companies

3. This lack of correlation

suggests that oil futures, like

other futures contracts that

also appear to have little

correlation with stocks, are a

good vehicle for diversifying

stock portfolios

Yang,

Hwang &

Huang 2002

Investigates

price volatility

of the crude oil

market by

examining the

market structure

of OPEC, the

stable and

unstable

demand

structure, and

related elasticity

of demand

Monthly crude oil

price data from

January 1975 to

September 2000.

The data are

taken from

international

financial statistics

published by the

International

Monetary Fund

(IMF)

GARCH model 1. A cartel like OPEC tends

to promote higher prices with

lower production. An

excessively high price would

generally create conditions

where there is a potential for

inflation and economic

recession

2. Excessive price volatility

spells uncertainty for both oil

exporting countries and major

consuming nations, such as

the US and Japan

Huang,

Hwang &

Pen 2005

Impacts of oil

price volatility

on economic

activities –

Industrial

Production (IP)

and real stock

returns)

Daily data of the

US, Canada, and

Japan during the

period from 1970

to 2002

Multivariate

Threshold

Autoregressive

(MVTAR) model

proposed by Tsay

(1998)

Oil price change seems to

have better explanatory

power on economic activities

than oil price volatility. In

general, oil price volatility

explains stock returns better

than a change in industrial

output

Sadorsky

2006

To estimate

forecasts of

daily volatility

Daily closing

futures price

returns on WTI

TGARCH, GARCH

and VAR models

1. The TGARCH model fits

well for heating oil and

natural gas volatility and the

48

in petroleum

futures price

returns

crude oil. The

dataset for crude

oil, heating oil

and unleaded

gasoline covers

the period 5/2/

1988 to 31/1/2003

for a total of

3,911

observations. The

natural gas

dataset covers the

period April 3/4/

1990 to January

31/1/2003 (3,349

observations)

GARCH model fits well for

crude oil and unleaded

gasoline volatility

2. Despite the increased

complexity, models like state

space, VAR and bivariate

GARCH do not perform as

well as the single equation

GARCH model

3. The results are useful for

anyone needing forecasts of

petroleum futures volatility

Narayan,

Kumar &

Narayan

2007

Crude oil price

volatility

Daily price data

from 3/9/1991–

15/9/2006

EGARCH model Over the full sample period,

evidence suggested that

shocks have permanent

effects on volatility and

asymmetric effects on

volatility

Askari &

Krichene

2008

Investigates

high volatility,

high intensity

jumps, and

strong upward

drift in oil

markets

Daily oil futures

prices 1/1/2002–

7/7/2006. Data

source: Reuters

Jump-diffusion

model

Oil price dynamics are

relevant for hedging,

forecasting and policy-

making. These dynamics are

dominated by strong upward

drift and frequent jumps,

causing oil markets not to

settle around a mean

Wencheong

2009

Investigates the

time-varying

volatility of two

major crude oil

markets, the

WTI and

Europe Brent

Two crude oil

spot price

datasets, namely

the WTI and

Europe Brent;

The datasets

consisted of 3,761

and 3,805 points

for the WTI and

Brent,

respectively, from

4/1/1993 to

31/12// 2008.

Data collected

from the US

Energy

Information

Administration

(EIA)

ARCH model 1. For the Brent series, the

specification of conditional

variance gains additional

accuracy over the conditional

standard-deviation 2. The Brent series

encounters the leverage effect

on crude oil price shocks 3. Crude oil price volatility

persistence is observed in

both the Brent and WTI

series

Agnolucci

2009

Compares the

predictive

ability of two

approaches that

can be used to

forecast

volatility

Daily returns

from the generic

light sweet crude

oil future based

on the WTI from

31/12/1991 to

02/05/2005

GARCH-type

models and an

implied volatility

model

1. Shocks to the conditional

variance of the series were

found to be highly persistent 2. No leverage effect was

observed in the oil future

series 3. GARCH-type models seem

to perform better than the

implied volatility

49

Kang, Kang

& Yoon

2009

Investigates the

efficacy of a

volatility model

for three crude

oil markets:

Brent, Dubai,

and the WTI

Daily closing

prices over the

period from 6/1/

1992 to

December

29/12/2006.

Three crude oil

spot prices were

obtained from the

Bloomberg

CGARCH,

FIGARCH, GARCH

and IGARCH

models

1. The CGARCH and

FIGARCH models are better

equipped to capture

persistence than are the

GARCH and IGARCH

models 3. The FIGARCH model for

the Brent and Dubai crude

oils provides superior performance in out-of-sample

volatility forecasts

Wei, Wang

& Huang

2010

Capture the

volatility

features of two

crude oil

markets: Brent

and WTI

Daily price data

6/1/1992–

31/12/2009

Linear and non-

linear (GARCH)

models

The non-linear GARCH

models are capable of

capturing long-memory

and/or asymmetric volatility,

exhibit greater forecasting

accuracy than the linear ones,

especially in volatility

forecasting over longer time

horizons, such as 5 or 20

days

Mohammadi

& Su 2010

Modelling and

forecasting the

conditional

mean and

volatility

Weekly crude oil

spot prices in 11

international

markets over the

period between

1/2/1997–

10/3/2009. Data

were from the

official website of

the EIA

GARCH, EGARCH,

APARCH and

FIGARCH models

Forecasting results are

somewhat mixed, but the

APARCH model outperforms

the others. In addition,

conditional standard

deviation captures the

volatility in oil returns better

than the traditional

conditional variance. Shocks to conditional

volatility dissipate at an

exponential rate, which is

consistent with the

covariance-stationary

GARCH models rather than

the FIGARCH alternative

2.7.2 Volatility in Stock Markets

Financial market volatility has an impact on financial regulation, monetary policy and

the macro economy. One aim of this thesis is to employ several different models to

examine daily stock market volatility and volatility spillovers in the GCC countries to

identify the best volatility models.

Numerous financial time series, such as stock returns and exchange rates, show changes

in volatility over time. These changes tend to be serially correlated and in the GARCH

model, developed by Engle (1982) and Bollerslev (1986), such effects are captured by

letting the conditional variance be a function of the squares of the previous returns and

50

past variances. A wide range of GARCH models have appeared in the literature; see for

example, Bollerslev et al. (1992). These models are able to capture many important

features of a univariate return time series. However, many questions remain about

which models are suitable for capturing the dynamics of multivariate return time series.

Hwang and Steve (2005) examined cross-sectional volatility, a special case of common

heteroscedasticity in asset returns. Engle (1999) and Engle and Sheppard (2001)

introduced an MGARCH model with time-varying correlations. Theodossiou and Lee

(1993) employed MGARCH in a mean model in their research into the mean and

volatility spillovers between the US, Japan, UK, Canada and Germany, and found that

there is no relation between conditional market volatility and expected return. They

found that the volatility transmission between the UK and Canadian markets is

insignificant and all markets had significant volatility spillovers transmitted from the

US stock markets.

Karolyi (1995) applied MGARCH to evaluate the international transmission of stock

returns between the US and Canada. In this study, Karolyi introduced the bivariate

GARCH model; the main finding was that the magnitude and persistence of return

innovations that began in either market or their transmission to the other market

depended significantly on how the cross-market dynamics in volatility were modelled.

Karolyi’s study analysed the volatility spillovers from the US markets to Canadian

market in more detail than that of Theodossiou and Lee (1993), and he argued that the

magnitude and persistence of S&P 500 shocks is greater for successive returns of non-

interlisted shocks than interlisted ones. This suggests that investment barriers related to

differential accounting disclosure standards, foreign ownership restrictions and tax

considerations may be important for understanding the dynamics of co-movements in

stock prices around the world. These findings were later supported by Racine and

Ackert (2000), who found that these markets are strongly integrated and although

volatility is highly correlated across the US and Canadian stock and futures markets,

these correlations have declined over time.

Worthington and Higgs (2004) studied the transmission of return and volatility in Asian

developed markets (Hong Kong, Japan and Singapore) and emerging markets

(Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand). Their findings

51

indicate that the mean spillovers from the developed markets to the emerging markets

are not harmonised across the six emerging markets. They also found that own volatility

spillovers are generally higher than cross volatility spillovers for all markets, but are

particularly obvious in the emerging markets. On the other hand, Caporale et al. (2006),

in his study of three pairwise models for US, European, Japanese and South East Asian

daily stock market returns for the pre- and post-1997 crises, found that the South East

Asian emerging markets’ conditional variance depends positively on shocks originating

in the European developed markets. Shocks that occurred in the Japanese market had a

positive effect on the South East Asian conditional variance over the full sample, while

their influence was negative and smaller in the pre-crisis period. This means that Asian

stock markets respond differently during financial crises.

Wang et al. (2005) went further in their study, finding evidence of significant return

spillovers from the US and Japan to all three small markets during the pre-Asian crisis

era. During the Asian crises no volatility spillovers were found between the developed

markets (US and Japan) and the emerging markets of South Asia.

Consistent with previous studies in the Asian area, Li (2007) examined the linkages

between the two emerging stock exchanges in mainland China and the established

markets in Hong Kong and the US using the MGARCH approach. While he did not find

any evidence of a direct linkage between the stock exchanges in mainland China and the

US market, he found evidence of unidirectional volatility spillovers from the stock

exchange in Hong Kong to those in Shanghai and Shenzhen. This result was in

agreement with previous studies, indicating that the emerging markets in Asia are

susceptible to conditions within their region; therefore, international investors could

seek increased diversification in the Asian markets and exploit the opportunities for

high returns due to higher risk-return trade-off.

Koulakiotis et al. (2009) examined volatility and the error transmission of news within

three different European financial areas; Scandinavia (Denmark, Sweden, Finland and

Norway), the Germanic countries (Austria, Switzerland and Germany) and the French

region (Brussels, France, Italy, Holland and Spain). They found that the Finnish and

Danish portfolios of cross-listed equities are those that transmit volatility to the Swedish

portfolios of cross-listed equities. In the Germanic stock market area, they found that

52

the Swiss portfolio of cross-listed equities is the major exporter of volatility and error to

other portfolios, and finally, with respect to the French stock market area, they observed

that the Paris, Amsterdam and Brussels stock exchanges are the major exporters of

volatility and error to the portfolios of cross-listed equities traded on the Milan and

Madrid stock exchanges. An earlier study by Kanas (1998) examined the volatility

spillovers across the three largest European stock markets, namely London, Frankfurt

and Paris, using the EGARCH model. Mutual spillovers were found to exist between

London and Paris, and between Paris and Frankfurt, and unidirectional spillovers from

London to Frankfurt. In almost all cases, these spillovers were asymmetric. An analysis

for the pre-crash (01/01/84–15/09/87) and post-crash (15/11/87–07/12/93) periods

suggested that more spillovers and spillovers with higher intensity occurred during the

latter period. These findings suggest that these markets had become more

interdependent during the post-crash period.

In a recent study of volatility asymmetry in 49 countries Talpsepp and Oliver (2010)

found that economic development and market capitalisation/GDP are the most

important factors that increase volatility asymmetry. Taken together, these results

indicate that regulators should focus on cross-listed equities as they may produce

different interactions between stock. As noted earlier in this literature review, much

work has been done on modelling equity volatility and estimating the volatility spillover

mechanisms that exists between the main mature financial markets and their

transmission effects on emerging markets such as those in the East, South Asia and the

Pacific area.

53

2.7.3 Volatility in the GCC Stock Markets

Few researchers have investigated the persistence of volatility and volatility

transmission in the GCC countries. Examples include the studies of Ewing et al. (2002),

Assaf (2003), Haque et al. (2004), Al-Deehani and Moosa (2006) and Hammoudeh and

Yuan (2009). Like their Asian counterparts, the six GCC countries have become the

latest ‘emerging markets’ in the Middle East. The importance of studying the dynamic

conditional correlation and volatility spillover between the GCC countries is essential

for future planning. The stock markets of the GCC countries are relatively new

comparing to the advanced markets. The oldest regulated market in the Gulf area is the

Kuwait stock market, which commenced regulated operations in 1983, followed by the

Saudi market in 1984, while the UAE market was officially launched in 2000. These

governments wish to integrate into the emerging system of global governance, and also

aspire to adapt their policies to meet the requirements of best international practice,

within the limits imposed by certain cultural requirements (Hanelt 2002).

Figure 2.5 shows the performance of the GCC markets compared to international

markets in 2002, at a time when the world’s major markets experienced a steep fall.

These countries have rapidly growing stock markets and some markets have doubled

their investors’ money between 2001 and 2003 (Malik & Hammoudeh 2007).

54

Source: Malik and Hammoudeh (2007).

Figure 2.5: Comparison of Stock Market Performance between the GCC Markets

and Other Major Markets in 2002

Assaf (2003) examined the dynamic interactions among stock market returns for the six

GCC countries. On the basis of his empirical findings he suggested that there is

substantial evidence of interdependence effects among the GCC stock markets. He

found that the Saudi Arabian market reacted relatively slowly to shocks initiated in

other markets, and that these markets are not efficient when responding to regional news,

providing an opportunity for portfolio diversification at the regional level.

In addition to Assaf’s (2003) findings of low domestic correlation, Bley and Chen (2006)

showed that there is a low correlation between the GCC markets on one side and the US

and UK markets on the other, which reveals diversification opportunities for

international investors. However, investigation of co-integration has revealed an

increase in the number of co-integrating vectors. This is likely to be an indication of

ongoing attempts to co-ordinate market economies in preparation for an economic union

and eventually the introduction of a single currency.

Haque et al. (2004) studied volatility, time-varying risk premiums and persistence of

shocks to volatility in the ten Middle Eastern and African emerging stock markets.

Their findings on volatility in these emerging markets revealed that eight out of the ten

markets showed evidence of volatility clustering.

9.90%

-0.10%

32.30% 25.60%

26.90%

4.70%

-43.94%

-24.48%

-23.37%

9.40%

-50.00%

-40.00%

-30.00%

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

PERFORMANCE

Market Performance 2002 UAE SAUDI ARABIA QATAR OMAN KUWAIT BAHRAIN DAX FTSE S&P 500 GCC

55

Al-Deehani and Moosa (2006) explored volatility spillovers in three regional Gulf

emerging markets (Kuwait, Bahrain and Saudi Arabia) by estimating a SUTSE

(Seemingly Unrelated Time Series Equation) model, in which the volatility in each

market is described by the volatility in the other two markets and by other variables

represented by a time-varying trend. These authors found that the Kuwait market

induces a strong volatility spillover in the other two markets, while the Saudi market

exercises a strong spillover effect on the Kuwait market, but has no effect on the

Bahrain market. In addition, the Bahrain market has a positive effect on the Kuwait

market but not on the Saudi market. These results contradict the findings of Assaf (2003)

and Bley and Chen (2006), as the volatility in each of these markets can be explained by

global effects rather than regional effects alone.

Rao (2008) found that the emerging markets in Gulf Region gain more of their volatility

persistence from the domestic market rather than international markets. Hence,

international investors could increase their diversification in the GCC markets and

utilise the opportunities for high returns due to the higher risk-return trade-off. Another

study by Hammoudeh and Yuan (2009) estimated own-market volatility, shocks and

persistence of volatility and volatility spillovers in three equity sectors of four GCC

countries. The results suggested that past own-market volatility and not past shocks is

the stronger force in determining future volatility in the GCC stock markets. Further, in

a recent study that computed the systematic risk values with and without the S&P 500

index within GCC markets Onour and Sergi (2010) found that the S&P 500 has a

minimal effect on the GCC markets’ risk, which implies that a substantial portion of the

volatility in GCC markets is attributable to internal factors rather than external spillover.

These findings can be summarised as follows: both regional and international factors

affect the GCC markets, but the regional factors dominate the international factors. This

conclusion been supported by previous researchers in other regions, such as

Worthington and Higgs (2004) and Li (2007).

56

2.7.4 Volatility in Oil and Stock Markets

Jones and Kaul (1996) led the way by testing the reaction of advanced stock markets

(those of Canada, the UK, Japan, and the US) to oil price shocks, using the standard

cash flow dividend valuation model. They found that for the US and Canada, the

reaction can be wholly explained by the impact of oil shocks on cash flows, while the

outcomes for Japan and the UK were uncertain. Huang et al. (1996) applied unrestricted

VAR analysis, which proved a significant relationship between certain US oil company

stock returns and oil price changes. Conversely, they found no evidence of a

relationship between oil prices and market indices such as the S&P 500. On the contrary,

Sadorsky (1999) applied an unrestricted VAR with GARCH effects to US monthly data

and showed a significant relationship between oil price changes and aggregate stock

returns. Subsequently, El-Sharif et al. (2005) examined the links between oil price

movements and stock returns in the UK oil and gas sector, and found strong

interrelation between these two variables.

Ewing et al. (2002) examined the volatility spillovers between the oil and natural gas

markets using daily returns data. They found indications of volatility persistence in both

markets and showed that volatility in natural gas returns is more persistent than

volatility in oil returns. They also found that current oil volatility depends on past

volatility rather than specific events or economic news. In contrast, natural gas return

volatility reacts more to unanticipated events (e.g., supply interruptions, changes in

reserves and stocks), regardless of which market they originated in. The main security

traded in these markets is the common stock of companies and mutual funds.

In comparison to the work of Ewing et al. (2002), Maghyereh and Al-Kandari (2007)

found that oil prices impact the stock price indices in the GCC countries in a non-linear

manner. Thus, the statistical analysis in this study supports a non-linear modelling of the

relationship between oil and the economy.

Malik and Hammoudeh (2007) examined the transmission of volatility in the oil and

equity markets of the US, Saudi Arabia, Kuwait and Bahrain, applying MGARCH

models. Their findings imply that in all cases the three Gulf country equity markets

obtain volatility from the oil market. Remarkably, only the Saudi equity market induced

57

significant volatility spillovers in the global oil market. In addition, they found that

shocks in the US equity market indirectly affected volatility in the three Gulf country

equity markets.

Abu Zarour (2006) applied the VAR model to investigate the relationship between oil

prices and five stock markets in the Gulf countries during the period between May 2001

and May 2005. This study found that the response of these markets to shocks in oil

prices has increased and became faster during the rise in oil prices, while only the Saudi

and Omani markets are able to predict oil prices.

Maghyereh and Al-Kandari (2007) found that oil prices impacts the stock price indices

in the GCC countries in a non-linear fashion, and they supported the statistical analysis

of a non-linear modelling relationship between oil and the economy, which is consistent

with certain authors, such as Mork et al. (1994) and Hamilton (2000). Figure 2.6 shows

how the stock market in the GCC countries and oil prices are interrelated; it is apparent

that they show a common trend.

Source: Own results, based on data from MSCI Barra (2005). Note that oil prices have

been rescaled to render them comparable with the average of the GCC stock market

indices.

Figure 2.6: Five GCC Stock Market and Brent Oil Prices for the Period

21/04/2006–5/10/2009

Miller and Ratti (2009) analysed the long-run relationship between the world price of

crude oil and international stock markets over the period from January 1971 to March

2008. They found an obvious positive and statistically significant co-integrating long-

run relationship between real stock prices for six OECD countries and world real oil

200

400

600

800

1000

1200

1400

1600

100 200 300 400 500 600 700 800 900

OILKUWAITOMAN

UAEBAHRAINQATAR

58

price from January 1971 until May 1980, and again from February 1988 and September

1998. This means that stock market prices increase as the oil price decreases and

decrease as the oil price increases over the long run. On the other hand, they found

insignificant relationship between May 1980 and February 1988.

Arouri and Rault (2010) used the panel data approach of Kónya (2006), which is based

on seemingly unrelated regression (SUR) systems and Wald tests with Granger

causality to study the sensitivity of stock markets to oil prices in the GCC countries for

the periods from 7 June 2005 to 21 October 2008 and January 1996 to December 2007.

Their results showed strong statistical evidence that the causal relationship is

consistently bidirectional for Saudi Arabia. In the other GCC countries, stock market

price changes do not Granger-cause oil price changes, whereas oil price shocks

Granger-cause stock price changes. This study suggested that investors and policy-

makers in the GCC stock markets should be aware of changes in oil prices because

these changes drastically affect stock market returns. In addition, GCC markets are

potential areas for international portfolio diversification. Studying the influence of oil

price shocks on GCC stock market returns can help investors make necessary

investment decisions.

Recently, Fayyad and Daly (2011) performed an empirical investigation into the

relationship between oil price and stock markets returns for seven countries (Kuwait,

Oman, the UAE, Bahrain, Qatar, the UK and the US) during the global financial crisis

(GFC) using VAR analysis. They found that the predictive power of oil for stock returns

increased after a rise in oil prices, and the impulsive response of a shock to oil increased

during the GFC. They also found that Qatar, the UAE and the UK showed more

responsiveness to oil shocks than the other markets. Table 2.4 summarises some of the

empirical studies of oil and stock market volatility.

59

Table 2.4: Summary of Empirical Studies on Oil Price Volatility

Author(s) Empirical study Sample size and period Estimation

method Main findings

Jones & Kaul

1996

Crude oil shock,

international stock

markets and current

and future change

in real cash flows

Postwar data from 1947–

1991

OLS

estimator

For the US and Canada

stock prices and oil

shocks are related by the

impact of oil shocks on

cash flows, while the

outcome for Japan and

the UK were uncertain

Sadorsky

1999

Crude oil prices

and volatility with

stock market

returns

Monthly data covering the

period from January 1947

to April 1996. All data

come from the

DRI/McGraw-Hill

database

VAR and

GARCH

US monthly data shows a

significant relationship

between oil price

changes and aggregate

stock returns.

Abu Zarour

2006

The effect of the

sharp increase in

oil prices on stock

market returns for

five GCC countries

Daily data from

25/05/2001 to 24/05/2005

collected directly from

individual stock markets,

Oil spot prices, and the

WTI price

VAR The predictive power of

oil prices has increased

after the rise in oil prices

and the impulse response

function of these markets

to shocks in oil prices

has also increased. The

Saudi market is more

responsive to shocks in

oil prices and vice versa

Malik &

Hammoudeh

2007

The transmission of

volatility in the oil,

US and Gulf equity

markets of Saudi

Arabia, Kuwait,

and Bahrain

Daily data from

14/02/1994 to 25/12/2001.

Oil spot prices and the

WTI price and GCC stock

markets

MGARCH Gulf equity markets

receive volatility from

the oil market but only

Saudi Arabia showed a

significant volatility

spillover from the Saudi

market to the oil market

Maghyereh &

Al-Kandari

2007

The linkage

between oil prices

and stock market in

GCC countries

Daily data from

01/01/1996 to 31/12/2003.

Prices of three types of

crude oil: Brent, WTI and

Dubai.

Non-linear

co-

integration

analysis

Nonlinear relationship

was detected between oil

prices and the GCC stock

market returns

Miller &

Ratti 2009

The long-run

relationship

between the world

price of crude oil

and international

stock markets

Data are monthly from

01/1971 to 09/1999.

Crude oil price of the US

Producer Price Index

(PPI) and the UK Brent.

Real stock market prices

for six OECD countries.

All series are normalised

so that the initial values

are zero

A co-

integrated

VECM with

additional

regressors

An obvious positive

statistically significant

co-integrating long-run

relationship was found

between real stock prices

for six OECD countries

and world real oil price

from January 1971 until

May 1980 and again

from February 1988 and

September 1998.

Arouri &

Rault 2010

The relationship

between oil prices

and stock markets

in net oil importing

countries

Two different (weekly and

monthly) datasets

covering the periods from

7/06/2005 to 21/10/2008

and from 01/1996 to

12/2007, respectively

SUR systems

and Wald

tests with

Granger

causality

Investors and policy-

makers in the GCC stock

markets should be aware

og changes in oil prices

because these changes

drastically affect the

60

stock market

Fayyad &

Daly 2011

The relationship

between oil prices

and stock market

returns for seven

countries (Kuwait,

Oman, the UAE,

Bahrain, Qatar, UK

and the US)

Daily data for the period

21/09/2005–12/02/2010.

Stock market data was

obtained from MSCI

while the daily data for

crude oil price was

sourced from the US

Department of Energy

EIA

VAR The predictive power of

oil for stock returns

increased after a rise in

oil prices and during the

GFC. The impulsive

response of a shock to oil

increased during the

GFC

2.8 GCC Countries and Economic Growth

GCC countries share a number of specific fundamental economic features, while also

exhibiting some significant differences. Their main common features are: a high

dependency on hydrocarbons as expressed in the share of oil (and gas) revenues in total

fiscal and export revenues, and the share of the hydrocarbon sector in their GDP; see

Table 2.5.

Table 2.5: Hydrocarbon GDP as a Percentage of Overall GDP

Year Bahrain Kuwait Oman Qatar Saudi Arabia UAE

1980 29.20 83.27 64.69 85.39 84.36 80.60

1985 24.44 54.60 52.03 59.33 36.47 51.25

1990 18.11 46.68 52.85 57.45 54.42 62.36

1995 12.35 50.52 40.67 44.26 41.52 38.27

2000 20.75 64.13 55.86 66.62 55.81 45.73

2005 27.65 68.43 67.20 76.09 75.93 50.33

Source: IMF; Coury and Dave (2009).

Oil profits have been used to modernise infrastructure, create employment, and improve

social indicators, while the GCC countries have been able to accumulate official

reserves, maintain relatively low external debt and remain important donors to poor

countries. Average per capita income in the GCC countries was estimated at about

$31,000 in 2009. Figure 2.7 shows the GCC country GDP per capita for the period from

1995 to 2010, as year 2009 estimated and 2010 forecast.

61

Source: IMF (2010).

Figure 2.7: GCC GDP Per Capita

Life expectancy in the GCC region increased by almost 10 years to 74 years between

1980 and 2000, and literacy rates increased by 20 percentage points to about 80 per cent

over the same period. Average per capita income in the GCC countries was estimated at

about $12,000 in 2002 and $31,000 in 2009, with their combined nominal GDP

reaching close to $880 billion; see Figure 2.7. There has been significant economic

growth over the past three decades, with the importance of non-oil economic

performances having grown progressively, reflecting the GCC countries’ efforts at

economic diversification. This development has been achieved with an open exchange

and trade system and liberal capital flows, as well as open borders for foreign labour.

The GCC region has become an important centre for regional economic growth (Asano

& Iqbal 2003). In addition the GCC countries have a young and rapidly growing

national labour force, with a heavy reliance on expatriate labour in the private sector.

These features also bring common structural policy challenges to GCC economies,

particularly economic diversification to reduce the dependency on the hydrocarbon

sector and to develop the private non-oil sector (Turm et al. 2008).

From a global perspective, the GCC is still a small, but vital player, with a nominal

GDP of 1.5 per cent of the world total, comparable to the ASEAN countries, at 1.8 per

cent. With a total population of around 40 million (according to the IMF estimate in

0.00

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00

80,000.00

90,000.00

Bahrain Kuwait Oman Qatar SA UAE

62

2010), the GCC remains small among its economic peers. Nonetheless, in terms of

growth rates, the GCC has surpassed Middle Eastern and Latin American growth

averages. In addition, GDP per capita income, especially in the UAE and Qatar, is

among the highest in the world. Inflation is the major challenge encountered by the

GCC countries (Table 2.6). Inflation stresses are headed by the extra amount of liquidity

brought in through large current account surpluses, the maintenance of USD pegs and

the expansionary fiscal policies of the GCC governments. Inflation has also risen due to

the increase in national population and expatriate workers and the subsequent supply-

side constraints, especially in the housing sector. As the GCC is unable to reduce these

domestic constraints immediately, inflation stresses are likely to increase. With inflation

in the UAE and Qatar at high levels, these countries are the most likely candidates to

revalue their currencies (Kudatgobilk & Saxena 2008).

Table 2.6: GCC Country Selected Economic Indicators, Estimated in 2009

Gross product

pricesUSD, billions

Inflation, consumer

prices (2008), annual

percentage

General government

revenue,per cent of

GDP

Current account

balance,USD,

billions

Bahrain 20.59 3.53 22.55 0.56

Kuwait 98.416 11 52.734 28.688

Oman 13.803 12.1 40.722 –0.818

Qatar 98.313 15.1 40.329 –2.525

Saudi

Arabia 376.268 9.8 42.173 –5.325

UAE 223.874 12.9 25.773 0.108

Source: IMF (2010).

2.9 The Resource Curse and Dutch Disease

2.9.1 The Resource Curse

Natural resource abundance can act as a blessing and a curse. It is a blessing because the

discovery of natural resources or an increase in the world market price of a domestic

resource increases income and thereby also the consumption possibilities in the

economy. However, it is also a curse as it can cause ‘Dutch disease’, which can have

severe economic effects in the short as well as the long run and can act as a hindrance to

development within the country. In the end it all depends on how the incoming revenues

63

are used. The importance of wise revenue management cannot be stressed enough. To

spend and manage large revenues wisely can be more complicated than it may first

seem. There are endless ways to utilise them and no uniform solutions to cure Dutch

disease if, against all odds, the country is infected by it (Rodriguez 2006).

The Dutch disease theory was proposed after the Netherlands found large sources of

natural gas in the North Sea in the 1960s. As a result of the large capital inflows, which

followed from increasing export revenues, the demand for the Dutch florin increased,

which in turn resulted in an appreciation of the Dutch exchange rate. This led to greater

difficulty for Dutch manufacturing goods to compete on the international markets. The

Dutch disease theory continues to be significant to this day, and Dutch disease is still

affecting countries all over the world. With today’s increasing world market prices for

raw materials we are likely to find other countries being affected in the future as well.

Dutch disease theory is now also used to explain negative effects from capital inflow

caused, for example, by aid payments, beneficial terms of trade shocks or sharp

productivity increases in export production.

2.9.2 Dutch Disease

The theory of Dutch disease proposed by Corden and Neary (1982) states that an inflow

of capital, for example, caused by an oil boom, causes the real exchange rate to

appreciate. The reason for this appreciation is that domestic prices in the tradable and

non-tradable sector will be affected asymmetrically with the prices of the non-tradable

sector rising at a faster rate. This further implies that the competitiveness of the tradable

goods depreciates in international markets as the opportunity cost of producing tradable

goods has increased. In Corden and Neary’s model, the small open economy is assumed

to consist of three sectors. Tradable goods are produced by the booming and the lagging

sectors while the third sector produces non-tradable goods. The booming sector can, for

example, be the oil, gas or mineral industry and the lagging sector the manufacturing

industry. The non-tradable sector is usually defined as services. The three sectors use a

common factor of production, labour, and a sector specific factor, capital. The most

important mechanism underlying Dutch disease is the real exchange rate (Rodriguez

2006).

64

2.10 Volatility and GDP

So here’s my prediction: You tell me the price of oil, and I’ll tell you what

kind of Russia you’ll have. If the price stays at $60 a barrel, it’s going to be

more like Venezuela, because its leaders will have plenty of money to indulge

their worst instincts, with too few checks and balances. If the price falls to $30,

it will be more like Norway. If the price falls to $15 a barrel, it could become

more like America—with just enough money to provide a social safety net for

its older generation, but with too little money to avoid developing the leaders

and institutions to nurture the brainpower of its younger generation.

(Will Russia Bet on Its People or It’s Oil Wells? Thomas L. Friedman, New

York Times, February 16, 2007)

One channel for the adverse linkage between resource dependence and growth is

volatility. Resource prices are very volatile, particularly for oil, where the coefficient of

variation of prices is 0.7. Prices are also very difficult to predict. Since the start of the

1970s none of the major turning points in the oil market has been widely predicted.

Hamilton (2008) provides a careful study of the statistical properties of oil price series.

He finds that the random walk hypothesis cannot be rejected and that, starting from a

price of $115 per barrel, four years into the future we should not be too surprised to find

the price of oil as high as $391 per barrel or as low as $34. The destructiveness of these

cycles is clear from many examples. Mexico borrowed against expectations of

increasing real oil prices after 1981 and suffered badly when these expectations turned

out to be far off track. Before 1980 Venezuela had been one of the fastest-growing Latin

American economies, with long-term growth averaging 6.4 per cent. However,

following several enraptured years after 1974 it experienced a sharp decline (Gelb 2010).

Government revenues as a percentage of output are highly volatile in the GCC countries

(Table 2.7). The average coefficient of variation of the current revenues for the GCC

countries is 46 per cent compared to 11 per cent for the OECD.

65

Table 2.7: Volatility Measures in the GCC

Volatility Measures GCC OECD

Coefficient of variation of GDP growth (per cent) 244 50

Coefficient of variation of current revenues ( per cent GDP) 46 11

Pro-cyclicality of government consumption (per cent) 12 –16

Source: Shamloo (2005).

Furthermore, government revenues have a nearly perfect correlation (90 per cent) with

the price of oil. The heavy dependence of government revenues on one commodity

indicates the vulnerability of the economy to exogenous fluctuations in the global oil

market (Shamloo 2005). The volatility of the GDP growths for the GCC countries is

shown in Figure 2.8.

Source: IMF (2010).

Figure 2.8: GDP Growths of the GCC Countries

2.10.1 Oil Prices and Economic Activity

Oil prices have an impact on economic activity through various transmissions channels

(Lardic & Mignon 2008). First, there is the classic supply-side effect, in which rising oil

prices are indicative of the reduced availability of a basic input to production, leading to

a reduction of potential output (see e.g., Brown & Yücel 1999; Abel & Bernanke 2001).

Consequently, there is a rise in the cost of production, and the growth of output and

-10

-5

0

5

10

15

20

25

30

35


66

productivity are slowed. Second, an increase in oil prices deteriorates the terms of trade

for oil importing countries. Thus, there is a wealth transfer from oil importing countries

to oil exporting ones, leading to a fall of the purchasing power of firms and households

in oil importing countries. Third, according to the real balance effect (Pierce & Enzler

1974), an increase in oil prices will lead to increased money demand. Due to the failure

of monetary authorities to meet this growing demand with an increase in supply, there

will then be a rise in interest rates and a slowdown in economic growth. Fourth, a rise in

oil prices generates inflation. The latter can be accompanied by indirect effects, called

second round effects, giving rise to price-wage loops. Fifth, an oil price increase may

have a negative effect on consumption, investment and stock prices. Consumption is

affected through its positive relationship with disposable income, and investment by

increasing firms’ costs. Sixth, if the oil price increase is long-lasting, it can give rise to a

change in the production structure and have an impact on unemployment. Indeed, a rise

in oil prices diminishes the rentability of sectors that are oil-intensive and can incite

firms to adopt and construct new production methods that require less intensive oil

inputs. This change generates capital and labour reallocations across sectors that can

affect unemployment in the long run (Loungani 1986). For all of these reasons, oil

prices can affect economic activity.

2.10.2 Oil Price Volatility and Global financial crises (GFC)

Between January 2002 and August 2008, the nominal oil price rose from $19.7 to

$133.4 a barrel. This steered to a large rise in oil revenues for oil exporters and a

worsening of the current account for oil importers (Figure 2.9). Between 2002 and

2006, net capital outflows from oil exporters grew by 348%, becoming the largest

global source of net capital outflows in 2006.

Capital outflows from oil exporters played an essential role in the global liquidity

surplus during the accumulation to the US subprime crisis. Analysis of direct capital

flows is hampered by the absence of reporting transparency and the use of foreign

financial intermediaries. Indirect recycling also took place, i.e. direct oil-revenue

investment in a given financial market led to corresponding knock-on flows towards the

ultimate net borrower.

67

Source: Oil Drum (2012).

Figure 2.9: US and Major Oil and Gas Exporters (MOGE)

Such capital flows were invested in US treasuries, corporate bonds, equities, and asset

markets. As a result, this employed plunging pressure on US interest rates and helped

supply further borrowing. In total, the direct and indirect recycling of oil revenues was a

factor in the global liquidity surplus that helped to accelerate the US subprime mortgage

crisis.

Bursting the bubble

Oil prices also played a role in ultimately bursting the US subprime bubble. This

happened via a number of channels which are difficult to untangle. It steered to the

meltdown in the subprime market and then global financial markets. Individual can

observe the different channels through which oil prices contributed:

• Direct effects on discretionary spending. Between 2002 and 2008, average

household expenditure on gasoline rose by 2 percentage points of overall household

expenditure.

• Indirect impacts of interest rate increases. The US federal fund rate increased from

1% in May 2005 to 5.26% in March 2007.

68

• Labor market interactions. Inflationary impact of the oil price shock from 2004-8

was reduced in the US due to the structure of the labor market. Producers used a strong

bargaining position to pass the cost burden onto consumers through a reduction in real

wages.

• Distributional impact of energy prices. Energy price shocks have strong

distributional effects, mostly impacting energy expenses of suburban households and

low-income households spending a greater income share on energy.

Finally, increasing oil prices had an impact on aggregate demand. This operates via a

number of channels – reduced discretionary income, increased precautionary savings,

and operating cost effects, whereby consumers are deterred from purchasing energy-

intensive goods, and reallocation effects. In particular, the auto sector played an

important role in transmitting the shock (Oil Drum 2012).

2.10.3 Empirical Findings—the Impact of Oil Prices on Economic Activity

The relevant empirical studies began by finding a linear negative relationship between

oil prices and real activity in oil importing countries. These studies include those of

Darby (1982), Hamilton (1983), Burbidge and Harrison (1984), and Gisser and

Goodwin (1986). While all these contributions consider the case of the US, Darby (1982)

and Burbidge and Harrison (1984) also analysed other developed countries (Japan,

Germany, the UK, Canada, France, Italy and the Netherlands in the first case, and Japan,

Germany, the UK and Canada in the second).

However, by the mid-1980s, the estimated linear relationship between oil prices and real

activity began to lose significance. In fact, the declines in oil prices that occurred over

the second half of the 1980s were found to have smaller positive effects on economic

activity than had been predicted by linear models. In consequence, Mork (1989) found

that the effects of oil price increases are different from those of decreases, and that oil

price decreases are not statistically significant in the US. This implied a departure from

the linear specifications, in which oil price rises and falls have equal and symmetrical

impacts on real activity. Mork’s contribution has proven influential in that many

subsequent authors have not even considered the possibility of effects derived from a

decrease in oil prices.

69

Lee et al. (1995) and Hamilton (1997) introduced non-linear transformations of oil

prices to re-establish the negative relationship between increases in oil prices and

economic downturns, as well as to analyse Granger causality between both variables.

More recently, Jimenez-Rodriguez and Sanchez (2005) also found evidence of a non-

linear relationship between the two variables for the US economy.

For selected GCC countries (Saudi Arabia, Kuwait and the UAE) Al-Otaibi and

Sylwester (2007) found no general evidence of asymmetries in that asymmetries are not

present; this then suggests that the effects of oil price movements on GDP growth are

not only opposite, but qualitatively different, between oil exporting and importing

countries. One possible reason is that oil production and sales for these GCC countries

cover larger relative segments of their economies than does the oil consumption and

imports for most, and this make the GCC countries more vulnerable to Dutch disease,

due to their relatively less diversified economies. Conversely, the UAE could be an

important exemption to positive asymmetry findings, as negative changes in oil prices

have larger effect than do positive changes. Accordingly, the UAE would prefer to see

more stable oil prices, and this could lead to dissimilar favourites in policy within the

GCC.

Jimenez-Rodriguez and Sanchez (2005) empirically assessed the effects of oil price

shocks on the real economic activity of the main industrialised OECD countries

(individual G7 countries, Norway and the Euro area as a whole). Their results are

broadly consistent with the expectation that the real GDP growth of oil importing

economies suffers from increases in oil prices in both linear and non-linear models.

With regard to the two net oil exporters in this study, Norway benefits from oil price

hikes. It is noticeable that Norway, the world’s most diversified economy (Shediac et al.

2008), responds better to price rises than the GCC countries (Al-Otaibi & Sylwester

2007), while a significant negative impact on GDP growth has been noticed for UK. As

oil prices rise these contrasting results for the oil exporting countries (Norway and UK)

can be traced to a sharper real exchange rate appreciation in the case of the UK.

70

2.10.4 National Unemployment in the GCC Countries

Unemployment for nationals is a phenomenon across all GCC labour markets, given the

strong presence of non-national workers in the region. Figure 2.10 records trends in

unemployment growth rates over periods of up to 30 years. These range between 2.1 per

cent per year in Saudi Arabia to 7.5 per cent in the UAE. Unemployment signals a lack

of human capital engagement for these states. Strong national growth rates across the

region create a continuing social challenge for government and administrators in the

provision of maternity and infant health facilities, early childhood development

opportunities and schooling (Al-Ali 2008).

71

Source: Al-Ali (2008).

Figure 2.10: National Unemployment Growth Rates in the GCC Countries 1974–

2002

Taken together with the average real GDP per capita (Table 2.8) for the same period,

this information illustrates that the jobless rate exceeded real economic growth in the

GCC countries over this period. This confirms the observation by Al-Qudsi (2006) that

labour supply in the region grew at higher rates than the long-term growth of jobs, and

that a comprehensive strategy on the part of the GCC authorities is required to

encourage young nationals to acquire technical skills in occupations with adequate

labour demand.

Table 2.8: Average Real GDP Per Capita Performances 1971–2001

Country

Average real GDP per capita (per cent)

1971–1980 1981–1990 1991–2001 1971–2001

Bahrain 8.2 –2.8 1.7 2.4

Saudi Arabia 6.7 –9.1 –1.2 –1.2

Kuwait –7.4 –6.2 –2.0 –2.5

Oman 2.1 –25.0 0.8 1.7

UAE 24.4 –6.7 2.7 4.8

Source: Al-Ali (2008).

0

2

4

6

8

10

12

14

16

18

Bahrain Kuwait Oman Saudi Arabia UAE

UN

EMP

LOY

MEN

T R

ATE

%

1974

2002

72

2.10.5 Building Human Capital

If there is a resource curse, it seems to affect certain types of countries more than others.

Bravo-Ortega and Gregorio (2007) found that the larger the stock of human capital, the

more positive the marginal effect of natural resource abundance is on growth. Lederman

and William (2007) repeat this message, noting that rich countries that have

successfully used their natural resources to further developmental outcomes, such as

Australia and Norway, have done so on the basis of high and growing levels of human

capital. Lessons also provide strong evidence of the importance of human capital for the

structure of exports. In their classic study, Maier and Wood (1998) distinguish districts

on the basis of two ratios: skills per head and land (resource) per head. As population

increases, the ratio of land/head declines over time; as countries invest in human capital

the skills/head ratio increases.

Maier and Wood (1998) show that there is a close relationship, both across regions and

over time, between factor ratios and export composition. Regions with high ratios of

land to skills tend to specialise in primary products. As the land/skills ratio falls, the

export mix shifts, first toward processed primary products, then to simple manufactures,

and then toward more complex and technologically demanding manufactures. This

suggests that countries that fail to invest heavily in their human capital will find it

difficult to move away from primary dependence and toward more sophisticated

products.

2.10.6 Institutions and Governance

Mehlum et al. (2006) find that the quality of institutions is critical in determining

whether countries avoid the resource curse. Natural resources are only found to have a

negative impact on growth performance in countries with inferior institutions. What

kinds of institutions are important? Collier (2007) suggests that the issue is not simply

whether countries are democratic. Jeffrey et al. (2003) distinguish ‘factional’

democracies from ‘mature’ democracies. How does the quality of institutions affect the

potential for diversification? Recent research on long-term growth has increasingly

emphasised the importance of institutions. There is a strong and logical relationship

between a variety of measures of institutions and level of income per head, particularly

73

for countries not especially rich in natural resources. Institutional quality therefore

appears to have a close relationship with the potential of an economy to deliver high

incomes by functioning at a high level of productivity.

2.10.7 Vertical Policies and Effective Public Spending

Diversification will also be influenced by how governments choose to spend resource

rents. Any spending with a domestic component will tend to draw resources to the non-

traded sectors, appreciate the real exchange rate and weaken the non-resource traded

sectors. This effect can be at least partially offset by spending that reduces production

costs in these sectors, raises their efficiency, and encourages the entry of investors with

new capabilities and knowledge. Well-designed and implemented investments in

infrastructure, human capital or improving institutions can have this effect, even if

applied in a sector-neutral way. Nevertheless, some level of vertical policy and spending

that targets non-resource traded sectors on a broader or more focused basis is probably

inescapable in resource-rich countries. It is very difficult to promote all such sectors at

the same time against the pull toward non-traded sectors that result from increased

domestic demand powered by public spending funded by resource taxes. Given that

diversification is a national priority, vertical policies can be seen as efforts to

compensate for market incentives distorted by the spending of resource rents.

Governments also have greater possibilities for financing such measures, whether

tailored infrastructure, tax rebates, investment incentives or other inducements to invest

(Gelb 2010).

2.11 Summary

The objective of this chapter was to point out the contradictory views regarding the

effect of international oil prices on the stock market, with reference to the different

empirical analysis approaches of cross-sectional and time series data on a

macroeconomic level. The literature on oil and stock market volatility has produced a

large amount of theoretical and empirical research, especially since the development of

the ARCH/GARCH models of Engle (1982) and Bollerslev (1986). This theoretical

research has not been definite on how oil and stock market volatility should be

74

modelled and, as such, the development of empirical models to explain and predict oil

and stock market volatility remains an active area of research.

This chapter began with an introduction to the importance of oil as an exhaustible

resource, the importance of peak oil and some empirical work of world oil peak

production. It then highlighted the history of the stock markets in the GCC countries

Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. The importance of volatility

for forecasting and predictions that could be used in investment, options pricing,

hedging, market making, portfolio selection and many other financial and economic

activities was also discussed.

Next, the chapter then outlined the concept and measures of volatility. One of the main

reasons that make stock market volatility an important topic of interest is its relationship

to the EMH. This section of the chapter therefore introduced the concept of market

efficiency and showed how evidence of volatility clustering is in contradiction with the

EMH. Furthermore, for the purposes of developing a model for stock market volatility,

the factors that cause oil and stock market volatility are an important consideration.

Hence, the literature on the causes of oil and stock market volatility was discussed.

The next two sections of the chapter described the theoretical analysis using the

ARCH/GARCH and MGARCH conditional heteroscedasticity models, and it was

shown that the MGARCH classes of models are the most appropriate choice for

modelling volatility in oil and GCC country stock markets. The empirical evidence of

volatility with the ARCH/GARCH and MGARCH models was discussed within the

context of this region. In particular, the literature was grouped into the developed

markets and the emerging markets of the GCC countries. This thesis is innovative in its

attempt to model oil and stock market volatility with the MGARCH class models

applied to time series data. This is a relatively new area of research with limited

literature available. This chapter also highlighted the GCC region’s economic growth,

the importance of economic diversification and the ways to diversify an economy. It

also discussed the resource curse and Dutch disease and ways for the GCC countries to

avoid these phenomena. Finally, volatility and GDP are discussed. Since world markets

have moved toward globalisation, there is increasing evidence of the interlinkage

between oil price volatility and GDP. The empirical studies began by finding a linear

75

negative relationship between oil prices and real activity in oil importing countries

Therefore, it is important to describe the performance of these economies, because of

the implications this may have for the volatility of oil prices.

In summary, the literature review in this chapter indicates that there are contradictory

findings on the relationship between oil and stock markets on one side and oil and

economic growth on the other. This study, in contrast, will provide further evidence on

this relationship by introducing sophisticated econometric methods and the use of time

series data to perform a comparative analysis of oil and stock market volatility at a

regional level, and oil and economic growth at regional and global levels.

76

Chapter Three: Future Economic Sustainability of Oil Rich

Countries

3.1 Introduction

To gain insight into the factors that determine economic development in present day

Arab Gulf countries it is useful to have an understanding of the major events and that

have shaped the way of life in these countries. Thus, this chapter will present some

essential background on the region from several perspectives—cultural, political, and

economic. It also will focus on economic diversification in the GCC countries.

3.2 History of the GCC Countries

The Arab Gulf region has developed and faded during a history that is perhaps an old as

that of human civilisation itself. This review will focus on events in modern times,

beginning with the 1980s.

3.2.1 The 1980s

The Iran-Iraq war in 1980 imposed a huge cost on the whole region. Increased insurance

premiums relating to the shipment of oil during the conflict, as well as military

disruptions to oil tanker movements out of the Arabian Gulf, adversely impacted on the

industry. One estimate of the economic cost to Iran alone for the first five years of

conflict was around USD 309 billion (Mofid 1990). The Iran-Iraq war increased a range

of security concerns for the smaller Gulf countries, resulting in the acknowledgement

that there was an urgent need for closer ties between them. The leaders of the six Arab

Gulf countries (namely, Saudi Arabia, the UAE, Oman, Kuwait, Bahrain and Qatar) met

77

on 25 May 1981 in Abu Dhabi and established the GCC. Their main objective was to

launch a common market and defence system that would protect these countries from

everlasting threats (Davies 1990).

Throughout this decade the region experienced a major fall in oil prices, mainly in 1986,

for several reasons. Firstly, new large non-OPEC oil exporters such as Norway and

Venezuela entered the global market. Secondly, many OPEC members were exceeding

the production quotas set by that organisation during these events; the OECD countries

experienced a fall in demand for crude oil because of the development of new

technologies, vehicles that were more fuel efficient and alternative power generation

plants in response to the oil price shocks of the previous decade. World consumption of

oil fell from 51.2 mbd in 1979 to 45.7 mbd in 1985 (Alnasseri 2005).

This drop in oil prices had a major adverse effect on the Gulf economies because their

main source of income was oil revenue. This shock had the greatest impact on the Saudi

Arabian economy. Aside from the need to liquidate its foreign assets to finance ongoing

development projects, the Kingdom considered, for the first time since the discovery of

oil within its borders, an imposition of income taxes. However, this suggestion was

rejected before it could be implemented. At the same time, although to a lesser extent,

the economies of the smaller Gulf States were also negatively affected (Wilson 1990).

The summer of 1988 finally signalled an end to the hostility between Iran and Iraq. The

sudden reaction from all stockholders in the region was one of great relief. Oil prices,

which had collapsed in 1986 to below USD 10 a barrel for the first time since 1973,

immediately rallied. Unfortunately, this rally proved short-lived when it was recognised

that both Iraq and Iran, in their need to remedy the destruction of the war and to repair

their defences, would urgently need money that could only come from increased oil

production (Road 1990).

3.2.2 Post-1990

The year 1990 witnessed the incursion of Kuwait by Iraq on 2 August. The consequence

of this action was catastrophic for both the region as a whole and for Iraq itself after the

US-led coalition forces freed Kuwait from the Iraqi invaders. Next, after the agreement

of a ceasefire on 28 February 1991, Iraq was placed under United Nations (UN)

78

sanctions for more than ten years. This comprehensive economic embargo caused

severe penalties for both the Iraqi people and Iraq’s economy (Alnasrawi 2001). Kuwait,

the victim of the invasion agreed to contribute (in combination with a number of other

Gulf countries) billions of dollars toward the cost of the war. As said by Deen (2003),

the 1991 Gulf War was to be responsible for a loss of approximately USD 600 billion

from the combined GDPs of the Middle Eastern countries.

Between 1992 and 1997, the Gulf countries experienced slow but steady economic

growth, despite a number of external shocks that impacted upon the region. These

shocks included the downturn in oil prices that followed an OPEC increase of

production to 55.3 mbd in 1993. Regional tensions were increased by the nuclear tests

undertaken by India and then Pakistan in 1998, along with the presence of large

numbers of citizens from both of these countries residing in the Gulf region. Among

other regional security problems was the occupation by Iran of three UAE islands.

Currently, this vague matter is still before the International Court of Justice, although it

has not served to threaten the region to any great extent (Chabrier 1998).

A focus of this period was the commission of enormous economic development projects,

which constituted part of the GCC economies’ efforts to move away from their near-

total reliance on oil, especially after the fall of crude oil prices in 1998. Additionally, at

the start of 2003, many political issues within the region (such as the border disputes)

were resolved and greater economic ties between the members of the GCC were

established (Alnasseri 2005). As a consequence of these actions, relationships between

the member states of the GCC were strengthened and economic trade barriers were

eased through the unification of various perpetual customs tariffs.

The final external shock experienced by the GCC region in this period was prompted by

the 11 September 2001 attacks on the World Trade Centre in New York and the

Pentagon in Washington, DC. In response, US forces entered Afghanistan, dethroning

the ruling Taliban regime and seeking to capture Osama Bin Laden, who was killed in

Pakistan during the writing of this thesis. Shortly after US forces entered Afghanistan,

the US led a coalition of countries in an invasion of Iraq, leading to downfall of Saddam

Hussein’s regime, and at the time of writing, the continued presence of US forces in

Iraq and Afghanistan. Analysts advising the UN Secretary General have estimated the

79

current war will cost the Middle Eastern Economies at least one trillion US dollars

(Deen 2003).

3.3 GCC Countries in Focus

Among the six members of the GCC this study will focus on three; namely, the

Kingdom of Saudi Arabia, the UAE and Kuwait. Due to data restrictions and in the

interest of making the best use of the available resources, Qatar, Bahrain and Oman

been excluded from this study. These three countries share many common economic,

demographic, political, and social characteristics. First, they are all dependent on one

major source of income—the hydrocarbon sector of oil and gas. This dependence makes

them susceptible to variations in oil prices and to the ultimate depletion of oil reserves.

The second commonality is the absolute-monarchist structure of their political systems,

wherein the decision-making power is concentrated in the hand of a ruling family. The

role of the legislative assemblies, where they exist, is largely nominal or consultative.

3.3.1 Saudi Arabia

Saudi Arabia is the largest of all the Gulf States, and also the most important oil

exporting country in the world. It has a land mass of approximately two million square

kilometres and a population of about 26.2 million (CIA Fact Book 2011). The present

political system in Saudi Arabia has largely been formed by King Faisal, where the king

possesses unlimited power, but the support of the religious leaders (Ulama’) is essential

for the legitimacy of the king’s rule. Over the last three decades of the twentieth century,

the king’s position grew stronger relative to that of the religious leaders within this

system. A written constitution has been established and a Bill of Rights enacted since

1992. The Constitution provides for a 60-member consultative body. From the

beginning of the 1970s, Saudi Arabia’s economic strategy has been to diversify its

economy away from oil, and to keep up oil prices at a level that discourages the

consuming nations from substituting oil as a source of energy (Machey 2002). Even

though important steps have been taken to reduce the reliance on oil and encourage

investments in other sectors, the growing population and continuous provision of social

80

welfare for all citizens has placed huge constraints on the government’s budget

(Alnasseri 2005).

The majority of Saudis are of Arabic descent, but other ethnicities are present in Saudi

society, including Turks, Iranian, Indonesians, Indians, Pakistanis and Africans. Many

of those ethnic groups migrated to the Kingdom and reside there during pilgrimages.

Despite a lack of detailed government data on Saudi Arabia’s population, estimates of

more than 26 million people have been considered reasonable. This would give the

country a population growth rate of more than 1.54 per cent per annum, and the fertility

rate is 2.31 children/woman (CIA Fact Book 2011).

3.3.2 The UAE

The current population of the UAE is approximately 5.2 million people; it has a land

mass area of 82,880 square kilometres (CIA Fact Book 2011). The UAE consists of

seven emirates: Abu Dhabi, Dubai, Sharjah, Ajman, Um Al Qaiwain, Ras Al Khaimah,

and Fujairah. The Trucial emirates were of minor importance in the region until the

discovery of oil in Abu Dhabi in 1950s (Heard-Bey 1999). A provisional constitution

was launched in 1971 and modified in 1972. In the provisional constitution, it was

approved that Abu Dhabi would be the capital of the new country, and the President and

Vice-President of the country would be elected for five-year terms by the Supreme

Council of Rulers (which includes the rulers of the seven federating States). The late

Sheikh Zayed of Abu Dhabi was elected as the first president, while the late Sheikh

Rashid of Dubai was elected as the first vice-president.

Sheikh Zayed (1971–2004) played a vital role in the UAE’s political, economic, and

social development. He paid particular attention to the policy of intensive infrastructure

development in all seven emirates. A major obstacle that this policy confronted was the

country’s limited labour force. As a result, a large influx of foreign workers was

permitted to enter the country to contribute toward its development and benefit from its

wealth. At present, foreign workers account for at least 75 per cent of the total

population. Before the discovery of oil, the UAE had a population of less than 60,000

inhabitants, with most of the population earning a living from pearling and small-scale

agriculture (Alnasseri 2005). Oil exporting has totally changed the UAE, given that

81

UAE is now the fourth largest oil exporting country in the world (EIA 2009). This

newly discovered wealth has changed the lifestyle and living standards of the UAE’s

population beyond recognition. After about 51 years of oil exporting and despite broad

efforts to diversify their sources of income, the government of the UAE is still heavily

dependent on oil revenues for its financial plan.

3.3.3 Kuwait

Kuwait is a nation of only about 18,000 square kilometres and currently has a

population of around 2.6 million. It is bounded by Iraq to the north, the Arabian Gulf to

the south and Saudi Arabia to the south west (CIA Fact Book 2011). Kuwait’s history is

tightly interwoven with that of its governing family, the Al Sabahs. This family has

ruled Kuwait since 1756. Even though Kuwait was a nominally independent emirate

under Ottoman suzerainty, the Ottoman Empire attempted to raid Kuwait in 1899. In

reaction, the Sheikh successfully obtained British protection, thus preventing the

Ottoman intentions for Kuwait being realised. In 1961 Kuwait was one of the first small

states in the region to gain full independence when the British protectorate finally ended

(Congress Library 1993).

The oil industries in Kuwait were among the first and most rapidly developed in the

Arab Gulf region. Kuwait’s modern economy was launched when the first shipment of

oil was exported in 1946. Through continuous and rapid development of the Kuwaiti oil

industry, the Kuwaiti people gained the benefit of a high living standard at an earlier

time than the other GCC countries. Even with fluctuations in price and production

levels, the oil sector has remained the dominant component of Kuwait’s economy

(Khouja & Sadler 1979). By 1953 Kuwait had become one of the largest oil producers

in the Gulf, a notable fact given its small size. Kuwait is the sixth most important oil

exporting country in the world (EIA 2009).

Before the Iraqi invasion in 1990, Kuwait retained foreign assets valued at between

USD 80 and USD 100 billion. Half of these assets were depleted as the result of the

Gulf war and by the much-needed reconstruction that followed it. Over the period of

this conflict, many Kuwaiti government assets were seized by Iraq and transported to

that country. Other assets and much of the public infrastructure was damaged or

82

destroyed during the course of the invasion, particularly those relating to the oil industry.

As is the case in other Arab Gulf countries, the Kuwaiti government plays a central role

in the economy. It is the largest employer and the main distributor of wealth among its

citizens. As of the end of the fiscal year 2001, Kuwait had a total outstanding public

debt of USD 8 billion, although its official external debt was zero (Alnasseri 2005).

Kuwait has done little to diversify its economy, partly because of its positive fiscal

situation, and also because of the poor business climate and the hostile relationship

between the legislative and executive authorities. However, in May 2010 the Kuwaiti

government approved a privatisation bill that allows the government to sell assets to

private investors, and approved an economic development plan that pledges to spend up

to $130 billion in five years to diversify the economy away from oil, attract more

investment and increase private sector participation in the economy (CIA Fact Book

2011).

3.4 Standard of Living Indicators

The GCC countries represent a unique phenomenon with regard to their population

composition. In contrast to many other countries in the world, they contribute a minority

within their own countries. UAE nationals represent not more than 8 per cent of the

total UAE population, and not more than 60 per cent in the case of Saudi Arabia,

Bahrain, and Oman. The population of the GCC countries has jumped from 7,766

million in 1970 to 33,075 million in 2005, and to 39.2 million according to the Human

Development Report 2010. Figure 3.1 shows the population for the countries in this

study. The highest annual growth rate took place during the period between 1975 and

1985. The oil boom of the 1970s is a major factor behind the large influx of foreign

labour.

83

Source: UNDP (2010).

Figure 3.1: Demographic Trends—Population in Millions

3.4.1 Human Development Index

This section will commence with a consideration of the Human Development Index

(HDI) compiled by the United Nations (Human Development Report 2010).This index

takes into account a wide range of socio-economic indicators. The period of analysis is

1995 to 2010. As Figure 3.2 shows, all countries in this study have shown continuous

increases in this indicator over the period 1995–2010. In comparison with a developed

country, Australia, which ranked second in the world on the HDI, it is noticeable that

the GCC countries converged to the Australian level by 2010.

Source: UNDP (2010).

Figure 3.2: Human Development Index

16.3

26.2

36.5

1.9 4.7 6.6

2.1 3.1 4.3

0

10

20

30

40

1990 2010 2030E

Saudi Arabia UAE Kuwait

0

0.5

1

1995 2000 2005 2009 2010

HDI

Saudi Arabia

UAE

Kuwait

Australia

84

3.4.2 Health Indicators

As we can see in figure 3.3, all the countries experienced an overall rise in life

expectancy through the period; Saudi Arabia enjoyed the fastest rise while UAE enjoy

the highest life expectancy. From the figure 3.3, it can be seen in general that UAE has

the highest HDIs among the three countries while Kuwait come on the second followed

by Saudi Arabia in the third.

Source: World Bank Data.

Figure 3.3: Life Expectancy (2000–2009)

Table 3.1 shows that all countries have experienced a decline in their infant mortality

rates, with the fastest declines being in the UAE, followed by Kuwait then finally Saudi

Arabia. The HDI values for these countries are the lowest in Saudi Arabia, which

ranked 55th; however, this is better than industrial countries like Russia, Brazil and

Turkey which ranked at 65, 73 and 83 respectively.

Table 3.1: Mortality Rate (infants per 1,000 live births)

Country 2000 2005 2006 2007 2008 2009

Kuwait 10.5 9.1 8.9 8.6 8.4 8.2

Saudi Arabia 19.8 18.9 18.7 18.5 18.4 18.2

UAE 9.9 8 7.7 7.4 7 6.8

Source: World DataBank (2011).

76.8

71.3

76.8 78.1

73.4

77.9

Kuwait Saudi Arabia UAE

Life Expectancy

2000

2001

2002

2003

2004

2005

2006

85

3.4.3 Education Indicators

Rates of adult literacy have increased in all GCC countries with the most rapid rise

being experienced by Kuwait, followed by the UAE and Saudi Arabia (see Table 3.2).

However, Kuwait has maintained the highest literacy rates in the region since 1970. As

of 2002, the GCC countries’ literacy levels were higher than that of Egypt but lower

than Turkey’s, and certainly much lower than that of the US (Alnasseri 2005).

Table 3.2: GCC Country Adult Literacy Rates (percentage of people aged 15 and

above)


Year Literacy Rate Year Literacy Rate Year Literacy Rate

2005 93.27446 2000 79.35094 2001

2006 2004 85.38729 2005 90.03385

2007 94.45812 2007 2007

2009 2009 85.38729 2009


Tertiary enrolment data indicates that the GCC countries experienced a rapid increase

during the 2000s, with the exception of Kuwait (see Figure 3.4). However, as of the end

of the twentieth century, tertiary enrolment was generally lower than in Turkey and far

lower than in Egypt and US (Alnasseri 2005).

86

Source: World DataBank. (2011)

Figure 3.4: Tertiary Enrolment (Gross Percentage)

3.4.4 Information and Telecommunications Indicators

The number of internet users per 100 people is a measure of access to information.

Table 3.3 indicates a rising trend in all of the GCC countries, with more rapid rates of

growth in the UAE, followed by Saudi Arabia then Kuwait.

Table 3.3: Internet Users (per 100 people)

Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Kuwait 6.8 8.8 10.7 23.7 24.4 27.6 30.8 33.8 36.7 39.4

Saudi Arabia 2.2 4.8 6.5 8.2 10.5 13 19.8 26.3 31.3 38.6

UAE 23.6 26.3 28.3 29.5 30.1 32.3 40.4 51.8 72 82.2


As Figure 3.5 shows, the number of broadband subscribers rose in all three countries.

Within this group of countries, the UAE has the highest number of broadband

subscribers, followed by Saudi Arabia and finally Kuwait.


2000 21.75643 17.81479

2001 21.84322 22.2813 22.56104

2002 21.75233 22.30327 22.76411

2003 20.36495 25.94204 22.80426

2004 18.90409 28.07821

2009 32.77573 30.40308

Tertiary Enrolment (% Gross)

87


Figure 3.5: Fixed Broadband Internet Subscribers

As Table 3.4 shows, all countries have experienced substantial increases in the number

of mobile telephones per 100 people. As of 2003, access to telecommunications services

in some GCC countries, such as the UAE and Kuwait, was comparable to, or even

greater than, in the US (Alnasseri 2005).

Table 3.4: Mobile Phone Ownership (per 100 people)

Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Kuwait 21.7 38.6 52.6 59.3 81.3 89.8 97.3 104.2 106.6

Saudi Arabia 6.7 12 23.2 32.8 40.7 61.3 83.2 117.2 145.1 176.7

UAE 44.1 55.9 67.6 78.9 93.7 110.9 130.4 177.2 208.6 232.1


3.4.5 Transport Indicators

Table 3.5 indicates that the number of passenger cars have decreased compared to the

increase of the number of motor vehicles per 1,000 people. Car ownership in the GCC

countries was far high than in either Turkey or Egypt, but still much lower than in the

US (Alnasseri 2005), even though the GDP per capita of the UAE is greater than that of

Kuwait, 45000

Saudi Arabia, 1437718

UAE, 690424

0

500000

1000000

1500000

2000000

2500000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Fixed BroadBand Subscribers

88

the US, while for Kuwait and Saudi Arabia this ratio was not too far below that of the

US.

Table 3.5: Passenger Cars/Motor Vehicles (per 1,000 people)

Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Kuwait 330/397 349/422 282/507

Saudi Arabia 407 415

UAE 293/313


As Figure 3.6 shows, the number of airline passengers rose in all countries, with the

UAE the country with the highest number of passengers, followed by Saudi Arabia and

finally by Kuwait. Dubai International Airport has recorded a 25 per cent surge in

yearly passenger numbers and an 11.3 per cent rise in international freight volumes as

of September 2010. Clearly Dubai’s aviation model is working. Open skies, supportive

government policies, and Dubai’s attractiveness as a business and leisure destination, as

well as its ideal location as a convenient global transit point are behind this impressive

growth (Trade Arabia 2010).


Figure 3.6: Air Transport, Passengers Carried

Kuwait, 2597076

Saudi Arabia, 17508200

UAE, 31761631

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

2000 2001 2002 2003 2004 2009

Passengers Carried

89

3.4.6 Government Subsidies

As a result of the sudden increase in wealth that the GCC countries experienced due to

the oil price boom, many services and subsidies were provided by the governments of

Saudi Arabia, Kuwait and the UAE. A complete assessment of the magnitude of all

subsidies in each GCC country is difficult due to information constraints. Figure 3.7

shows the health expenditure per capita (current USD) for the governments of Saudi

Arabia, Kuwait and the UAE. It is noticeable that the UAE government is leading the

other GCC countries on health expenditure per capita.


Figure 3.7: Health Expenditure Per Capita (current USD)

As can be in Figure 3.8, all the GCC countries experienced fluctuation in their

education public spending. Saudi Arabia enjoyed the greatest rise, while the UAE came

at the bottom of the list. Since 2000, and even before this, the Arab Gulf countries had

experienced sharp fluctuations in their public education expenditure, caused mainly by

the variation in world oil prices.

713.8502298

1416.099904

1520.058545

0

200

400

600

800

1000

1200

1400

1600

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

US

$

Health Expenditure

Saudi Arabia

Kuwait

UAE

90


Figure 3.8: Public Spending on Education (per cent GDP)

3.4.7 GDP Per Capita

While wellbeing is not merely dependent upon income, GDP per capita is an

appropriate indicator that provides some information about living standards. As shown

in Table 3.6, since 1995 the Gulf countries have experienced a rise in their per capita

income, mainly as a result of surplus oil income.

Table 3.6: GDP Per Capita (USD), 2009 Expected and 2010 Forecast

Country Saudi Arabia Kuwait UAE

1995 7,855.13 17,251.97 16,891.58

2000 9,216.39 17,012.78 23,446.15

2005 13,657.95 27,014.52 33,607.69

2008 19,156.86 42,994.61 53,388.04

2009E 14,744.61 27,835.44 45,614.54

2010F 16,641.41 32,530.48 47,406.66

Source: IMF (2010a).

In the UAE real per capita income increased sharply from USD 16,891.58 in 1995 to

USD 53,388.04 in 2008. According to the Human Development Report (2010), the

UAE is one of the very high human development countries while Saudi Arabia and

Kuwait belong to the high human development category. It is apparent that the

strategies implemented by the GCC countries over the last three decades have generally

5.62125

3.76001

1.16654

0

1

2

3

4

5

6

7

8

9

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

%G

DP

Education Public Spending

Saudi Arabia

Kuwait

UAE

91

been successful in terms of their efforts to increase per capita income on a sustainable

basis.

3.5 Economic Sector Breakdown

Saudi Arabia’s industrial sectors are ruled by petrochemicals and oil-based products.

The main industries in Saudi Arabia include crude oil production, petroleum refining,

electricity, gas and water infrastructure construction, industrial gases, fertilizers, cement,

plastics, metals, construction, commercial shipping and aircraft repair. Table 3.7

illustrates Saudi Arabia’s GDP by type of economic activity from 2006 to 2010. Table

3.7 illustrates the steady progress the non-oil sector has made, to stand at around 54 per

cent of GDP by 2010 compared with approximately 22 per cent in 1974 at the beginning

of the oil boom era (Ramady 2010). Table 3.7 also shows that while private non-oil

GDP has been steadily rising in absolute terms over the years, the oil sector continues to

be characterised by volatility, due to fluctuations in both world demand for oil and the

oil price. Closer breakdown of the GDP by economic activity reveals the gradual rise in

the value of manufacturing and services sector in the Saudi economy.

92

Table 3.7: Saudi Arabia—GDP by Type of Economic Activity (million riyals)

Economic Activity 2006 2007 2008 2009 2010 per cent

GDP

Agriculture, forestry and fishing 39,373 40,154 41,136 41,419 42,016 2.57767187

Mining and quarrying: 668,421 732,654 1,025,169 605,184 759,456 46.5924498

a) Crude petroleum and natural

gas 665,276 729,361 1,021,714 601,593 755,703 46.3622041

b) Other 3,145 3,292 3,455 3,590 3,753 0.23024568

Manufacturing: 123,912 136,509 147,873 146,673 164,446 10.088724

a) Petroleum refining 43,710 46,691 45,975 46,874 57,430 3.52331721

b) Other 80,202 89,818 101,898 99,799 107,015 6.56534548

Electricity, gas and water 11,664 12,419 13,095 13,722 14,945 0.91687229

Construction 59,139 65,017 68,099 67,962 71,261 4.37184586

Wholesale and retail trade

Restaurants and hotels 67,868 73,990 81,263 85,261 89,041 5.46264474

Transport, storage and

communication 41,367 45,934 52,752 56,858 60,067 3.68509655

Finance, insurance, real estate and

business services: 104,798 111,438 119,063 126,965 132,838 8.14958055

a) Ownership of dwellings 52,223 54,776 58,915 63,545 68,594 4.20822602

b) Other 52,575 56,661 60,148 63,419 64,244 3.94135453

Community, social and personal

services 29,203 30,631 32,301 33,989 36,241 2.22337696

Less: Imputed Bank Services

Charge 17,575 18,280 18,825 19,299 19,595 1.20214871

SUBTOTAL 1,128,170 1,230,465 1,561,925 1,158,734 1,350,714 82.8659912

B: Producers of government

services: 196,386 200,306 209,278 225,857 265,284 16.275112

Total except import duties 1,324,556 ,1430,771 1,771,203 1,384,591 1,615,998 99.1411032

Import duties 1,1025 1,1801 1,4940 1,2897 1,4000 0.85889676

GDP 1,335,581 1,442,572 1,786,143 1,397,488 1,629,998 100

Source: SAMA (2011).

Kuwait’s economy is extremely dependent on oil. Petroleum accounts for around half of

the country’s GDP, 95 per cent of export revenues and 80 per cent of government

revenues. Kuwait’s economy has been progressing at a rapid pace recently, largely due

to rising oil prices. Kuwait has very limited agricultural potential and must import the

majority of its food products from other countries. There is some fishing, but this is

93

minimal in comparison to the amount of other food products Kuwait needs from foreign

countries. About 75 per cent of potable water must be distilled or imported. Kuwait’s

labour force consists mostly of foreigners, at around 80 per cent (The Eurasia Center

2011).

Table 3.8 shows the solid increase in the non-oil sector, with a 5 billion increase in GDP

in 2006 compared to that of 2003. Table 3.8 also reveals that while the private non-oil

GDP has been gradually rising between 2003 and 2006, the manufacturing sector is

characterised by a slow progression. Closer breakdown of the GDP by economic

activity reveals the gradual rising value of transport, communications and financial

services in the Kuwaiti economy.

94

Table 3.8: Kuwait: Sectorial Origin of GDP at Current Prices (million dinars)

Sector 2003 2004 2005 2006

Oil sector (crude oil, gas, and refining) 6,370 8,618 13,203 17,308

Crude oil and gas 5,798 7,822 12,233 16,478

Petroleum and refining 572 796 970 829

Non-oil sector 8,360 9,462 11,271 13,316

Agriculture and fisheries 65 71 71 70

Mining (non-oil) and quarrying 17 23 32 39

Manufacturing 554 660 745 785

Food, beverage and tobacco 80 81 74 77

Textile, clothing and leather products 43 45 47 48

Wood and wood products 22 21 23 24

Paper, printing, and publishing 41 50 55 59

Chemicals, fertilizers, and plastic 189 249 296 275

Non-metallic minerals 64 80 96 104

Basic metals 10 8 13 22

Fabricated metal products 102 120 135 171

Other manufacturing 4 5 5 5

Electricity, gas and water 299 307 319 335

Construction 349 402 437 434

Hotels and restaurants 174 171 168 206

Wholesale and retail trade 890 950 1,018 1,057

Transport, storage and communications 800 1,048 1,231 1,973

Financial institutions and insurance 1,237 1,559 2,571 3,272

Real estate and business services 1,249 1,322 1,451 1,506

Community, social and personal services 2,726 2,950 3,228 3,576

Public administration and defence 1,239 1,325 1,456 1,574

Personal and household services 260 309 337 352

Other 1,228 1,316 1,436 1,929

Imputed bank service charges –613 –724 –1,052 –1,341

GDP at factor cost 14,118 17,355 23,422 32,371

Import duties 135 162 174 187

GDP at current market prices 14,253 17,517 23,595 29,470

Source: IMF (2010b).

The UAE GDP stood at 600 billion dirham in 2006, with a high GDP growth rate of 8.8

per cent per year. Oil and gas accounts for about 30 per cent of GDP, while the non-oil

sector has shown exponential growth, reaching 70 per cent of GDP, which is why the

95

UAE’s economy has proven to be less influenced by fluctuating oil prices in

comparison to other GCC countries. The UAE government has made successful

attempts to develop substitutes for the oil industries. Policies have been made for land

recovery, development of industries, tourism and oil refining of oil, as well as creating

international free trade zones. These factors, combined with an easily accessible

location and an extremely pleasant and safe working environment augur well for foreign

investment in the country. The main industries in UAE include crude oil production,

industry, tourism, services, water, construction, and transportation. Table 3.9 illustrates

the UAE’s GDP by sector from 2003 to 2006.

Table 3.9: UAE Sectorial Origin of GDP at Current Prices (billion dirhams)

Sector 2003 2004 2005 2006

Crude oil production (including gas) 91.0 120.9 173.2 223.4

Other production 229.6 263.3 312.3 375.8

Agriculture 9.2 10.1 11.0 12.2

Industry 75.1 86.7 105.0 129.2

Mining and quarrying 0.8 0.8 0.9 1.1

Manufacturing 42.2 50.2 61.2 73.4

Refined products and gas liquidation 17.7 20.0 26.6 28.6

Non-petrochemical manufactures 24.5 30.1 34.6 44.8

Electricity and water 6.0 6.7 7.9 9.5

Construction 26.1 29 35 45.1

Services 145.4 166.5 196.3 234.4

Trade 42 50.8 61.9 73

Wholesale and retail trade 35.5 43.5 53 62.5

Restaurants and hotels 6.5 7.3 8.9 10.4

Transportation, storage, and communication 24.7 27.3 32.6 38.5

Finance and insurance 19.9 23.4 28.4 35.7

Real estate and business services 25.4 30 35.9 46.1

Government 30.7 32.5 34.7 39

Other services 8.6 9.2 10.0 11.3

Social and personal services 6.5 7.1 7.6 8.7

Domestic household services 2.1 2.1 2.4 2.6

Less: imputed bank charges 5.8 6.7 7.4 9.3

GDP 320.6 384.1 485.5 599.2

Source: IMF (2009).

96

3.6 Why Diversify?

Why might countries with rich oil, diamond or copper reserves want to diversify in the

first place? Does this make sense, given their particular comparative advantage? How

does investment in domestic economic diversification, as a strategy, compare with

alternatives such as portfolio diversification through saving a high share of resource

rents abroad to invest in a range of industries or simply slowing the rate of reserve

depletion to hold more assets under the ground? One argument is that diversified

economies perform better over the long term. There is strong experimental support for

this suggestion (see, among others Hesse 2008; Leiderman and William 2007).

Herzer and Nowark-Lehmann (2004) analysed the Chilean experience and found that

export diversification is linked to economic growth through the exteriorities of learning

activities related to exporting and concluded that export diversification on the basis of

natural resources has a positive impact on growth. Imbs and Romain (2003) found a U-

shaped pattern whereby countries in the earlier stages of development diversify

production but countries above a certain level of income tend to re-concentrate

production. Most developing countries are therefore in the former stage.

One explanation for this relationship is that engaging in manufacturing enables dynamic

learning-by-doing gains that raise productivity and income. A related argument is that

diversification exposes producers to a broader range of information, including about

foreign markets, and so raises the number of points for potential ‘self-discovery’.

Capability in one sector can open the way to others, especially those that use related

knowledge. Not all agree with an automatic strong focus on manufacturing industry or

on particular industrial sub-sectors. Some countries may have a strong secondary

comparative advantage in a range of resource-based sectors, including secondary

minerals, forestry or tree crops that are not necessarily ‘connected’, but which offer

good opportunities (Gelb 2010).

Sinnott et al. (2010) note that technical change in the manufacturing sector is not

necessarily greater than that in primary sectors and that the latter also offer

opportunities for learning-by-doing. Some commodity production is argued to be

97

equally valuable in terms of production linkages and spillovers to other types of

production. Moreover, they note that in contrast to earlier views, recent studies have not

shown support for the argument that in the long run primary commodity prices decline

relative to the prices of manufactured goods. They also note that some studies suggest

that what is important is concentration itself, rather than the nature of the dominant

product. Other factors may also be important for resource-rich countries. High rates of

population growth weaken the long-run level of rents per head. If population grows at

three per cent per year, the per capita contribution of a constant resource sector will

halve in 24 years.

3.6.1 The GCC and Economic Diversification

Developing countries have been remarkably effective in diversifying their economies

and their export structures. This process of diversification has taken many forms. The

most noticeable change has been the shift toward industry. In the 1960s, some 80 per

cent of developing country exports were primary commodities; today, almost 80 per

cent are industrial products. This enormous transformation in export structure has been

associated with the rise of major industrial powerhouses; China most prominently, but

also countries such as Korea, India, Brazil, Malaysia, Vietnam, Indonesia and Mexico.

Most of these new industrial powers were previously primary-based economies. Other

countries have not moved as far toward ‘footloose’ manufactures but have taken

advantage of the possible for upgrading their resource-based sectors. For instance,

between 1975 and 2004 Latin America’s share of global markets in metals expanded by

175 per cent. Throughout this period the share of ores and unwrought metals doubled,

but that of worked products increased eight times (Sinnott et al. 2010).

The GCC, a political and economic alliance and trade union, was established in 1981

with the aim of integrating and co-ordinating member states in all fields and formulating

similar regulations in their economic, financial, trade, legislative and administrative

domains. As part of the overall plan for greater economic integration, GCC members

implemented a Customs Union in January 2003, unifying tariffs throughout the GCC. In

addition, the six members of the GCC launched a Common Market in January 2008 and

are aiming for a single currency and monetary union. Since the beginning of the current

decade, the GCC countries have adopted investment and development plans different

98

from those seen during the previous oil booms of the 1970s and 1980s. Wary of boom-

bust cycles, the GCC countries have built up reserves, paid down their public debt and

accumulated surpluses that have been transferred to oil funds, sovereign wealth funds

(SWF) and other state-controlled investment institutions. Economic management has

considerably improved and asset placement has become more sophisticated in the

region. There is a clear shift from the public to the private sector as the main engine of

growth. Growing domestic investments and economic diversification should help

reduce dependence on hydrocarbons (Kudatgobilk & Saxena 2008).

While revenues from oil and natural gas are important in explaining growth patterns, the

distribution of hydrocarbon reserves among the GCC countries is rather uneven: Saudi

Arabia controls the bulk of oil reserves while the UAE and Kuwait come second. In

contrast, Qatar has the most significant reserves of natural gas. Figure 3.9 show the

GCC country GDP derived from hydrocarbon. These countries have used their

hydrocarbon revenues to expand their economies; because of relatively small national

populations, both the public and private sector have hired significant proportions of

foreign workers. By 2005, the foreign population as a percentage of the total workforce

was as high as 90 per cent for the UAE and 89 per cent for Qatar (Coury & Dave 2009).

99

3.87

72.21

24.39 43.65

292.19

88.09

0

50

100

150

200

250

300

350


Billions

Source: EIA, British Petroleum (2007).

Figure 3.9: GDP Derived from Hydrocarbon in 2007 (USD, billions)

Hydrocarbon revenue has generated the overall growth in GDP (see Table 3.10). The

highest growth rate for 2008 was 25.424 per cent for Qatar and the lowest was 4.23 per

cent for Saudi Arabia.

Table 3.10: Per Cent GDP Growth 2000–2010, 2009 Estimated and 2010 Forecast

Source: IMF (2010).


Arabia UAE

2000 5.23 0.127 4.645 10.939 4.865 12.383

2001 4.618 0.669 5.56 6.318 0.547 1.695

2002 5.193 2.845 2.075 3.2 0.128 2.649

2003 7.245 17.406 0.342 6.323 7.659 11.885

2004 5.644 11.169 3.424 17.723 5.268 9.691

2005 7.853 10.357 3.99 7.615 5.553 8.192

2006 6.653 5.263 5.52 18.603 3.158 8.717

2007 8.384 4.458 6.812 26.759 2.017 6.056

2008 6.308 5.533 12.842 25.424 4.23 5.14

2009E 3.105 –4.824 3.588 8.646 0.603 –2.472

2010F 3.955 2.326 4.722 15.964 3.424 2.427

100

Diversification is measured by evaluating the distribution of a nation’s GDP across its

various economic sectors, such as banking or manufacturing, to determine a

‘concentration ratio’ or a ‘diversification quotient’. The concentration ratio measures a

nation’s concentration in a given sector by taking the sum of squares of per cent

contribution to GDP. The diversification quotient is the inverse of the concentration

ratio. Essentially, the lower the concentration ratio and the higher the diversification

quotient, the more diversified a nation’s economy. The results of this analysis showed

that the level of diversification varied widely across the GCC countries (see Figure

3.10). The level of concentration for Norway and Canada was 15 per cent and 16 per

cent, respectively, while it was 26 per cent for the GCC countries. The diversification

quotient for Norway and Canada was 6.79 and 6.25 respectively, while for the GCC

countries it was 3.87. It is noticeable that Abu Dhabi and Dubai show the most different

levels of diversification within a single country, and also that Dubai shows the highest

level of diversification among the GCC countries.

101

Source: Shediac et al. (2008).

Figure 3.10: Economic Concentration and Diversification in the GCC countries,

Norway and Canada, Real 2005 GDP

These findings are not necessarily surprising. Historically, the economies of the GCC

countries have been dominated by the oil and gas sector, and although the relative

contributions of the GCC countries’ various economic sectors to GDP have shifted

noticeably over the years, the oil and gas sector has consistently represented the largest

share in these nations’ GDPs (Figure 3.9).

3.6.2 Government Diversification in the GCC Countries

Governments can diversify in two main ways: by investing offshore through SWFs,

which diversifies income or by investing domestically, which diversifies production.

39% 37%

33% 28%

26% 23%

19% 16%

14% 15% 16%

0% 10% 20% 30% 40% 50%

Qatar Abu Dhabi

Kuwait Saudi Arabia

GCC Oman

UAE Bahrain

Dubai Norway Canada

Economic Concentration

2.59

2.69

3

3.63

3.87

4.35

5.18

6.12

6.85

6.79

6.25

0 2 4 6 8

Qatar

Abu Dhabi

Kuwait

Saudi Arabia

GCC

Oman

UAE

Bahrain

Dubai

Norway

Canada

Economic Diversification

102

The former fosters fiscal stability because the investment income accrues to the

government, whereas the latter aids social stability by encouraging private sector job

creation. In the absence of non-oil taxation, it is difficult for GCC governments to

merge these two objectives—to generate attractive jobs while also preserving the long-

term health of the public finances.

How can governments diversify in a way that generates desirable jobs and increases

GDP yet also conserves the health of the public finances? The answer lies in taxation.

Currently, there is no personal income tax or consumption tax in any GCC state. It is

difficult for individual GCC states to increase the level of non-oil taxation on their own

because they risk triggering the departure of private sector workers to neighbouring

countries. Hence, a co-operative approach is needed. There is also a political aspect.

‘No taxation without representation’, a slogan of the American Revolution, illustrates

the political context. In the Gulf, citizens have been inclined to view the state as

primarily a redistribution mechanism for oil income and have limited their demands for

participation in government (Cooper 2011).

3.6.3 Non-Hydrocarbon Growth

Growth in non-hydrocarbon sectors has been mixed; for example 1995 saw positive

growth rates in excess of 10 per cent for the UAE but as low as 0.88 per cent for Kuwait

(see Table 3.11). Diversification into non-oil industries and generation of income-

yielding projects for a growing population are key trends. Property development and

construction is the driving force of the non-hydrocarbon economy in the GCC countries.

The Dubai property development model is being widely copied, which could create the

risk of a region-wide real estate bubble. Roughly USD 1 trillion in infrastructure

investments are in the pipeline, and by 2010 these are expected to increase to USD 3

trillion. Dubai has opened up to the international financial services industry and has

been promoted as an excellent example of economic diversification (Kudatgobilk &

Saxena 2008).

103

Table 3.11: Percentage Average Yearly Non-Hydrocarbon GDP Growth in the

Previous Five Years


Arabia UAE

1985 1.54 4.28 26.42 46.60 44.22 41.32

1990 4.28 16.08 8.87 4.83 1.29 –2.37

1995 7.92 0.88 8.93 0.66 5.17 10.39

2000 3.84 3.51 3.15 6.52 3.17 8.55

2005 3.64 2.44 –1.01 9.77 –5.98 3.36

Source: IMF; Coury and Dave (2009).

3.6.4 Sovereign Wealth Funds

With oil prices currently at record highs, the value of assets held by the GCC’s SWFs

has increased massively over the last few years. Petrodollars are among the fastest

growing investment source. The largest SWF in the GCC is the Abu Dhabi Investment

Authority (ADIA), with estimated total assets of up to USD 875 billion, followed by

various funds in Saudi Arabia and the Kuwait Investment Authority (KIA) (Figure 3.11).

GCC capital injections into recessed US assets and other major investments in

developed markets and in the Middle East region have been making headlines.

Citigroup was sustained up by a USD 7.5 billion investment from ADIA in 2008 and

the KIA invested both in Merrill Lynch and Citigroup.

104

Source: Kudatgobilk and Saxena (2008).

Figure 3.11: GCC’S Sovereign Wealth Funds (USD billions)

3.6.5 Examples of Economic Diversification

Malaysia, ranked 20 in ‘Doing Business’ in 2009, is prosperous with its rather

diversified resource endowments, which include a good geographic location and deep-

water ports, rubber and tin, as well as forest products, which preceded oil as export

staples. Even in 2010, resource-based products represent 42 per cent of manufacturing

value added. It sustains a high and relatively stable savings rate, and has made massive

investments in land development and replanting schemes to expand and modernise the

production of rubber and palm oil. It has also made heavy investments in technology

and infrastructure, especially in the areas of energy, communications and transport.

Although Malaysia began on a protectionist path in the 1960s, in 1973–1974 it shifted

toward an extensive export promotion drive based on cheap manufactures. Measures to

hold down costs included policies to reduce the costs of labour and manage industrial

relations. In the mid-1980s, Malaysia’s strategy shifted toward higher-technology

products and skills upgrading. Here its policies include liberalising skilled immigration,

a dramatic expansion in enrolment in polytechnics, exchange relationships with

universities in Australia and Canada and skills development programmes jointly

sponsored by the Federation of Manufacturing and the University of Science and

Technology (Gelb 2010).

875

300 250

40 8.2 0

100

200

300

400

500

600

700

800

900

1000

UAE-ADIA Saudi Arabia-Various Funds

Kuwait-KIA Qatar-QIA Oman-SGSF

Billion USD

105

Chile has not emerged as a major industrial exporter. However, it has developed into a

dynamic and more diversified commodity exporter, with an emphasis on high-value

primary-based products that draw on its diversified resource base. One central element

has been its successful implementation of countercyclical fiscal policy, stabilising the

economy by high savings during the copper boom years and dis-saving when prices

began to fall. Chile also concentrated on improving its business climate, to become the

highest rated Latin American country on the ‘Doing Business’ indicators. In addition,

Chile offers several examples of successful active vertical public roles in helping to

develop the salmon and wine industries. These include encouraging technical

development and adaptation, disseminating information on standards, providing

infrastructure and information and co-ordinating numerous small producers (Benaventa

2006; Katz 2006). Chile also established a Competitiveness and Innovation Fund in

2005, financing this through a levy on mining, and developed sector clusters with

private sector participation and partial funding. Some 50 centres of excellence are in

operation, with the majority being university-based, and all compete for funding. Chile

also sponsors investment in high-level human capital by funding scholarships for study

abroad (Sinnott et al. 2010).

Norway is a country that is rich in both its mineral and industrial base. Considerable

petroleum revenues, representing 25–30 per cent of its GDP, have provided Norway’s

already developed economy with added strength. In 1970 the Ekdfisk oil field in the

Norwegian area of the North Sea was discovered. At the time it was one of the 20

largest oil fields in the world. Following this initial discovery, oil production grew,

rapidly increasing by 300 per cent between 1980 and 2000. Today Norway is the

world’s fifth largest oil exporter (EIA 2009). By 1980 Norway’s GDP per capita had

exceeded that of the US by 10 per cent. In 1990, Norway established the State

Petroleum Fund (SPF). Through the SPF resources accumulating during periods of

stable or rising oil prices could be used as a buffer when oil prices fell. Additionally, in

the longer term when oil reserves are depleted such accumulated funds may be used to

extend high government expenditure (Kajaer 2001). The SPF operates under firm

guidelines regarding the management of official foreign exchange reserves. Funds are

held in diversified assets abroad to ensure a low level of risk overall and a reasonable

rate of return. In 2000 Norway initiated the promotion of a ‘knowledge economy’ by

formulating e-Norway, an information and communication technology (ICT) policy.

106

Other government action to develop a ‘knowledge economy’ has involved the education

system, including passing a new Education Act in 1999, with greater emphasis on adult

education as a mean to raise levels of competency. In addition, since 1992, there have

been measures to encourage innovation in activities such as product design,

organisational development and marketing. According to a recent study, Norway is the

most diversified economy in the world (Shediac et al. 2008).

The Dubai model aims to attract investment in infrastructure, property and a range of

services, as well as establishing a free zone to further build export capacity. Dubai is

unique, especially in its dependence on expatriate labour and skills: nationals constitute

only 10 per cent of the population. Dubai’s vision was not simply based on ‘build it and

they will come’. It was also based on providing incentives to attract foreign direct

investments and major multinational companies. These incentives included an efficient

bureaucracy with little corruption; a regime of no taxes and low tariffs that proved

extremely attractive to companies and expatriates; a free market economy with low

restrictions on movement of funds and transactions; high-tech state-of-the-art

infrastructure to sustain an electronic-based system and e-government; public support,

direct or indirect, to all major projects; easy and quick processes to issue visas to

businessmen and visitors; allowing foreigners to own property in free zone areas; and

investing heavily in security. Very open trade and labour policy, a very low tax regime

and a pegged exchange rate to the dollar have made Dubai a relatively stable and low-

cost base for business.

Dubai aims to create a new asset: a critical mass of world-class infrastructure, services

and business able to serve as a transport and logistics hub and to gain sufficient

accumulation and ‘network’ externalities to be self-sustaining. Together, Dubai

nationals will still enjoy rent-based income. Little of the benefit to nationals comes

through normal employment secured on competitive labour markets (Gelb 2010).

107

3.7 Summary

This chapter has provided an overview of the history and economic position of selected

GCC countries: Saudi Arabia, Kuwait and the UAE. The focus has been to describe the

key characteristics of these countries and link them to the standard of living indicators,

as well as describe the position of the economic diversification of these countries

compared to that of other countries.

Sections 1–3 briefly provided a general overview of these countries and their histories.

Section 4 describes standard of living indicators, starting with the HDI and ending with

the GDP per capita, as the performance of the GCC countries’ economies mirrors their

achievements in living standards. Next, the economic sector breakdown for these

countries shows the steady progress that the non-oil sector has made. Section 6

describes oil peak theory and oil life expectancy in the major producing countries and

the world, while Section 7 describes the economic diversification of the GCC countries,

with diversification measured by determining the concentration ratio and diversification

quotient.

The literature has shown that non-hydrocarbon growth and SWFs are mechanisms used

by governments to achieve economic diversification. Governments can diversify in two

main ways: by investing offshore through SWFs, which diversifies income, or by

investing domestically in non-hydrocarbon growth. The next chapter examines the

methodology for modelling stock market volatility and volatility spillovers in the GCC

counties.

108

Chapter Four: Volatility Spillovers in the Emerging Markets

of the GCC Countries and Oil—a Multivariate GARCH

Model

4.1 Introduction

Recent changes in oil prices in the global economy have been rapid and unprecedented.

This is partly due to the fact that short-term demand and supply of crude oil is

tremendously inelastic to price changes. Oil prices have traditionally been both highly

volatile and subject to exogenous supply shocks from natural disasters, political events

and financial crises. Furthermore, the demand for oil products is expected to increase

for the predictable future, driven by the increased demand for oil by China and India.

The GFC beginning in September 2008 was followed by a year of less acute financial

turmoil, which substantially reinforced the cyclical downturn in oil prices. At the

beginning of 2008 the basket prices of oil was less USD 100 per barrel, by the middle of

the year it was approximately USD 140 per barrel and by the year end the price was

below USD 40 per barrel. Figure 4.1 shows the trend price of the Brent Daily Spot Price

for the 24 years 1987–2011. This chapter will focus on three major oil exporting

countries in the Gulf area: Kuwait, Saudi Arabia and the UAE.

109

Source: US Department of Energy (EIA).

Figure 4.1: Europe Brent Daily Oil Price (1987–2011)

The GCC markets are important for several reasons. In 2007 the GCC countries

produced about 20 per cent of the world’s crude oil, controlled 36 per cent of the

world’s oil exports and held 47 per cent of the world’s oil verified oil reserves. Table

4.1 shows the GCC countries market capitalisation of listed companies and the

corresponding contribution of oil as a percentage of GDP for each country. Oil is

responsible for between 22 per cent of Bahrain’s total output to 44 per cent for the

Kingdom of Saudi Arabia. GCC markets differ from those of developed and other major

emerging market economies in that they are predominately segmented, largely isolated

from the international markets and are overly sensitive to regional political events.

(Arouri and Rault 2010).

0

20

40

60

80

100

120

140

160

88 90 92 94 96 98 00 02 04 06 08 10

Daily Europe Brent Spot (1987-2011)

110

Table 4.1: GCC Economies, Stock Markets and Oil in 2007

Market Number of companies* Market

capitalisation($ billion)

Market

capitalisation(per cent

GDP)*

Oil(per cent

GDP)+

Bahrain 50 21.22 158 22

Kuwait 175 193.50 190 35

Oman 119 22.70 40 41

Qatar 40 95.50 222 42

UAE 99 240.80 177 32

Saudi

Arabia 81 522.70 202 44

Source: Arab Monetary Fund and Emerging Markets Database.

* Numbers in 2006

4.2 Background

Early empirical studies by Gisser and Goodwin (1986) and Hickman et al. (1987)

confirmed an inverse relationship between oil prices and aggregate economic activity.

Burbidge and Harrison (1984) and Bruno and Sachs (1982) documented similar oil

price-economy relationships in cross-country analysis. Hamilton (1983) made a

definitive contribution by extending the analysis to show that all but one of the post-

World War 2 recessions was preceded by rising oil prices, which other business cycle

variables did not predict. Jones and Leiby (1996) found that the estimated oil price

elasticity of GNP in the early studies ranged from −0.02 to −0.08, with the estimates

consistently clustered around −0.05.

Apart from studies showing that oil price shocks have significant effects on an

economy’s performance, relatively few researchers have studied the relationship

between oil prices and stock markets In addition; most of these studies have

concentrated on developed oil importers with less focus on emerging markets or oil

exporting countries. This chapter focuses on oil exporting countries. Jones and Kaul’s

(1996) initial study focused on testing the reaction of advanced stock markets (Canada,

the UK, Japan, and the US) to oil price shocks on the basis of the standard cash flow

dividend valuation model. They found that for the US and Canada the reaction can be

determined by the impact of the oil shocks on cash flows, while the outcomes for Japan

and the UK were indecisive. Huang et al. (1996) applied unrestricted VAR, which

111

confirmed a significant relationship between some US oil company stock returns and oil

price changes. Conversely, they found no evidence of a relationship between oil prices

and market indices such as the S&P 500. In contact, Sadorsky (1999) applied an

unrestricted VAR with GARCH effects to US monthly data and found a significant

relationship between oil price changes and aggregate stock returns. In recent times, El-

Sharif et al. (2005) examined the links between oil price movements and stock returns

in the UK oil and gas sector. They found a strong interrelationship between the two

variables. Abu Zarour (2006) applied a VAR model to investigate the relation between

oil prices and five stock markets in Gulf Countries between May 2001 and May 2005.

They found the response of these markets to shocks in oil prices increased and became

faster during episodes of oil price increases. Malik and Hammoudeh (2007) examined

the spillover effects of volatility in oil prices on equity markets for the US, Saudi Arabia,

Kuwait and Bahrain by applying MGARCH models. Their findings confirmed that in all

cases the three Gulf equity markets were affected by the volatility experienced in oil

markets. This study also found significant volatility spillovers from the US equity

markets to the three Gulf equity markets.

Maghyereh and AL-Kandari (2007) found that oil price affects the stock price indices in

GCC countries in a non-linear fashion, and they supported the statistical analysis of a

non-linear modelling relationship between oil and the economy, which is consistent

with Mork et al. (1994) and Hamilton (2000). Rao (2008) found that the emerging

markets in GCC gain more of their volatility persistence from the domestic market.

Hence, international investors could increase their diversification in the GCC markets to

utilise opportunities for high returns due to the higher risk-return trade-off.

Figure 4.2 shows how the stock market in the GCC countries and oil prices are

interrelated, showing a common trend. Miller and Ratti (2009) analysed the long-run

relationship between the world price of crude oil and international stock markets

between January 1971 and March 2008. They found an obvious positive statistically

significant co-integrating long run relationship between real stock prices for six OECD

countries and world real oil price from January 1971 until May 1980, and again from

February 1988 to September 1998.

112

Source: Own results, based on data from the MSCI (2011) and S&P Saudi Arabia

(2011).

Note: Oil prices have been rescaled to be comparable with the average of the GCC stock

market indices.

Figure 4.2: Five GCC Stock Markets and Brent Oil Prices 25/05/2006–18/11/2009

Arouri and Rault (2010) used the panel data approach of Kónya (2006), which is based

on SUR systems and Wald tests with Granger causality to study the sensitivity of stock

markets to oil prices in GCC countries between 7 June 2005 to 21 October 2008, and

from January 1996 to December 2007. Their results showed strong statistical evidence

that the causal relationship is consistently bidirectional for Saudi Arabia. In the other

GCC countries, stock market price changes do not Granger-cause oil price changes,

whereas oil price shocks Granger-cause stock price changes. This study suggested that

investors and policy-makers in the GCC stock markets should be aware of changes in

oil prices as these changes drastically affect stock market returns. Further, GCC markets

are potential areas for international portfolio diversification. Studying the influence of

oil price shocks on GCC stock market returns can help investors make necessary

investment decisions.

0

200

400

600

800

1,000

1,200

1,400

1,600

100 200 300 400 500 600 700 800

KUWAIT OIL SA UAE

113

4.3 Data and Empirical Results

The daily data employed in this study are drawn from the weighted equity market indices

of four major markets and oil. Brent oil price had been chosen on this study because it is

one of the primary oil price benchmark on the worlds (WTI, Brent Blend, and Dubai).

Benchmarks are used because there are many different varieties and grades of crude oil.

Furthermore Brent Oil has a moderate price between the expensive WTI and the cheap

Dubai oil. The four major markets namely, the UAE, Kuwait and Saudi Arabia. Daily

data were analysed for the period between 21/09/2005 and 12/02/2010; the stock market

data obtained from the MSCI and S&P Saudi Arabia. The S&P Saudi Arabia is a

standalone index designed to capture 80 per cent or more of the local market

capitalisation. The daily data for crude oil price came from the US Department of Energy

EIA. All indices are based on USD and do not include dividends, and the indices include

small, medium and large caps except for Saudi Arabia, which captured 80 per cent of

this market. The return for each market plus oil is expressed as a percentage computed

by multiplying the first difference of the logarithm of the stock market by 100.

100)/( 1 ititi PPLOGPwhere iP

denotes the rate of change of itP.

Table 4.2 presents descriptive statistics for each market return for the period between

21/09/2005 and 12/02/2010. As shown in Table 4.2, the volatility (measured by the

standard deviation) for the oil and UAE markets (0.0247, 0.0248) are higher than all

other markets, as oil tripled during the study period from a minimum value of $49.95 to

$143.95.

The distribution properties of the return series appear to be non-normal, since all the

markets show negative skewness except for the UAE. The kurtosis in all markets

exceeds 3, indicating a leptokurtic distribution. The final statistics given in Table 4.2 are

the calculated Jarque-Bera statistics and corresponding p value used to test the null

hypotheses that the daily distributions of returns are normally distributed. With all p

values equal to zero, the Jarque-Bera statistics reject the null hypotheses that the returns

for the developed and emerging markets are well approximated by a normal distribution.

114

Table 4.2: Summary Statistics of Daily Returns for Seven Stock Markets and Oil

Kuwait Oil Saudi Arabia UAE

Mean –0.0004 0.0008 –0.0007 –0.0009

Median 0.0000 0.0006 0.0000 0.0000

Maximum 0.0711 0.1222 0.1601 0.3465

Minimum –0.1064 –0.1683 –0.1354 –0.1623

Std. Dev. 0.0159 0.0247 0.0214 0.0248

Skewness –1.3193 –0.1259 –1.0076 1.9610

Kurtosis 12.098 7.223 13.501 42.168

Jarque-Bera 4,288.2 855.3 5,464.6 7,4052.8

Probability 0.0000 0.0000 0.0000 0.0000

Observations 1,147 1,147 1,147 1,147

The following conditional expected return equation accommodates each market’s own

returns and the returns of other markets, lagged one period:

ttt ARR 1 (4.1)

Where tR is the 1n vector of daily return at time t for each market and

).,0(~1 ttt HNI t is the innovation for each market at time t with its corresponding nn

conditional variance covariance matrix,t . The market information available at time

1t is represented by the information set 1tI. The 1n vector represents long-term

drift coefficients. The elements ija of the matrix A can provide measures of the

significance of the own and cross-mean spill-over of daily return (Worthington & Higgs

2004). Figure 4.3 shows the markets daily returns.

115

Figure 4.3: Market Daily Returns Between 21/09/2005 and 12/02/2010

4.4 Methodology

4.4.1 Unit Root Test

Each series were tested for unit roots (first difference of raw data). The null hypothesis

of the existence of a unit root (non-stationary) was tested against the alternative

hypothesis of stationary variables using the augmented Dickey–Fuller (ADF) statistic

(Dickey & Fuller 1981). Automatic selection of lags was employed based on the

Schwarz information criterion (SIC). Table A.1 in the Appendix reports the results,

which show that all series are stationary.

Autocorrelation refers to the correlation of a time series (here, the first difference of raw

data) with its own past and future values. Autocorrelation is also sometimes called

‘lagged correlation’ or ‘serial correlation’, which refers to the correlation between

members of a series of numbers arranged in time. Positive autocorrelation might be

considered a specific form of ‘persistence’, a tendency for a system to remain in the

same state from one observation to the next. The plot shown in Figure 4.4 represents the

autocorrelation function (ACF) and cross-correlation between markets and shows that

the autocorrelation between markets is insignificant from Lag 1, with a correlation of

less than 0.1. However, there was a tendency toward a positive correlation between

-.12

-.08

-.04

.00

.04

.08

250 500 750 1000

KUWAIT

-.20

-.15

-.10

-.05

.00

.05

.10

.15

250 500 750 1000

OIL

-.15

-.10

-.05

.00

.05

.10

.15

.20

250 500 750 1000

SA

-.2

-.1

.0

.1

.2

.3

.4

250 500 750 1000

UAE

116

financial markets, and mixed positive and negative correlations between financial

markets and oil.

KUWAI T

AC

F

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.2

0.

40

.6

0.

81

.0

KUWAI T and UAE

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.1

0.

20

.3

KUWAI T and SA

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.1

0.

2

KUWAI T and O I L

0 5 1 0 1 5 2 0 2 5 3 0

-0

.0

50

.0

0.

05

0.

10

UAE and KUWAI T

AC

F

- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

0.

00

.1

0.

20

.3

UAE

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.2

0.

40

.6

0.

81

.0 UAE and SA

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.1

0.

20

.3

UAE and O I L

0 5 1 0 1 5 2 0 2 5 3 0

-0

.0

50

.0

0.

05

0.

10

SA and KUWAI T

AC

F

- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

0.

00

.1

0.

2

SA and UAE

- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

0.

00

.1

0.

20

.3

SA

0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.2

0.

40

.6

0.

81

.0

SA and O I L

0 5 1 0 1 5 2 0 2 5 3 0

-0

.0

50

.0

0.

05

O I L and KUWAI T

L a g

AC

F

- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

-0

.1

0-

0.

05

0.

00

.0

5

O I L and UAE

L a g- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

-0

.0

50

.0

0.

05

0.

10

O I L and SA

L a g- 3 0 - 2 5 - 2 0 - 1 5 - 1 0 - 5 0

-0

.0

50

.0

0.

05

0.

10

O I L

L a g0 5 1 0 1 5 2 0 2 5 3 0

0.

00

.2

0.

40

.6

0.

81

.0

Figure 4.4: ACF of Observations

4.4.2 MGARCH-BEKK Methodology

One of the major breakthroughs in financial economics has been in the modelling of

variable variances (conditional heteroscedasticity) and volatility clustering in equity

returns. The GARCH framework builds on the conception of volatility dependence to

measure the impact of the last period’s forecast error and volatility in determining

current volatility. The simplest GARCH specification is formulated as:

ttr ),0( 2

tt N (4.2)

2

1

2

110

2

ttt (4.3)

The ARCH term measures the effect to which a volatility shock today impacts on

tomorrow’s volatility and represents the short-run persistence of shocks on return

variance. The GARCH term is the involvement of older shocks to the long-run

117

persistence. Akgiray (1989) finds that a GARCH (1, 1) model is sufficient to capture all

volatility clustering. A degree of volatility persistence )( 1 close to 1 means

volatility shocks are felt further into the future, at a gradually reducing extent.

More recently, the univariate GARCH model has been extended to the MGARCH

model, with the recognition that MGARCH models are potentially useful developments

in the parameterisation of conditional cross-moments. Engle and Kroner (1995) present

various MGARCH models with variations to the conditional variance-covariance matrix

of equations. For the purposes of this analysis, the BEKK model is employed, whereby

the variance-covariance matrix of equations depends on the squares and cross-products

of innovation t and volatility t for each market lagged one period. One important

feature of this specification is that it builds in sufficient generality, allowing the

conditional variances and covariances of the stock markets to influence each other, and

at the same time, does not require the estimation of a large number of parameters. The

model also ensures the condition of a positive semi-definite conditional variance-

covariance matrix in the optimisation process, and is a necessary condition for the

estimated variances to be zero or positive.

The BEKK parameterisation for the MGARCH model is written as:

q

jjt jjiitit

p

i

it BBAA11

~~~~~

(4.4)

Where ~

is a constant )( nn positive definite upper triangular matrix, the elements

iA~

(ARCH parameters) of the symmetric )( nn matrix (A is a symmetric matrix if and

only if AAT ) measure the degree of innovation from market i to market j , and the

elements jB~

(GARCH parameters) of the symmetric )( nn matrix indicate the

persistence in conditional volatility between market i and market j .

The specification involves 2)(]2/)1([ nqpnn unknown parameters, so for 3n

time series and 1 qp the total number of unknown parameter is 24. For example,

118

in the case of 1 qp and ,2n there will be 11 parameters, while in the case of

1 qp and ,3n there will be 24 parameters.

332313

322212

312111

2

1,31,231,13

1,23

2

1,21,12

1,131,12

2

1,1

333231

232221

131211

332313

322212

312111

2

1,31,31,21,31,1

1,31,2

2

1,21,21,1

1,31,11,21,1

2

1,1

333231

232221

131211

33

2322

131211

~~~

~~~

~~~

~~~

~~~

~~~

~~~

~~~

~~

~~~

~~~

~~~

00

0

bbb

bbb

bbb

bbb

bbb

bbb

aaa

aaa

aaa

aaa

aaa

aaa

w

ww

www

ttt

ttt

ttt

ttttt

ttttt

ttttt

t

(4.5)

333231

232221

131211

~~~

~~~

~~~

aaa

aaa

aaa

and

333231

232221

131211

~~~

~~~

~~~

bbb

bbb

bbb

are the matrices transpose for the symmetric

matrices of

332313

322212

312111

~~~

~~~

~~

aaa

aaa

aaa

and

332313

322212

312111

~~~

~~~

~~~

bbb

bbb

bbb

respectively.

The main advantage of this specification is that the conditional covariance matrix is

positive definite as long as ~

also is (Jondeau, Poon & Rockinger 2007). The

conditional variance as specified in Equation 4.5 is a function of three terms: (1) ijw,

elements of constant intercepts. This is the upper diagonal nn matrix (~

), presented

as Panel C in Table A.4 in the Appendix; (2) ija, elements of the symmetric nn

matrix A, which measures the degree of innovation from market i to market j (the

ARCH term), presented as Panel A in Table A.4 in the Appendix; and (3) the ijb

elements of the symmetric nn matrix B, which is presented as Panel G in Table A.4

in the Appendix. This indicates the persistence in conditional volatility between market

i and market j (the GARCH term). Engle and Kroner (1995) and Kroner and Ng (1998)

state that the BEKK system can be estimated using a full information ML method. The

log-likelihood function of the joint distribution is the sum of all the log-likelihood

functions of the conditional distribution; that is, the sum of the logs of the multivariate-

119

normal distribution. Letting tL be the log-likelihood of observation nt, be the number

of stock exchange and L be the joint log-likelihood gives:

T

t

tLL1

ttttt

nL 1

2

1ln

2

1)2ln(

2 (4.6)

A numerical procedure, such as the BHHH algorithm, is used to maximise the log-

likelihood function. The ML estimate is then applied to obtain the estimate of the

unknown parameters. Overall, the proposed model has 42 elements in the estimation of

the covariance process: 10 intercepts were obtained, with 16 ARCH parameters and 16

GARCH parameters. The Ljung-Box (LB) Q statistic can be used to test for

independence of higher relationships, such as those manifested in volatility clustering,

by the MGARCH model (Huang and Yang 2000). This statistic is given by:

p

j

jrjTTTQ1

21 )()()2(

(4.7)

Where )( jr the sample autocorrelation at lag j calculated from the noise terms and T is

the number of observations Q is asymptotically distributed as 2X with )( KP degrees

of freedom and K is the number of explanatory variables. The test statistic in Equation

4.7 is used to test the null hypothesis that the model is independent of the higher order

volatility relationships.

120

4.5 Results

4.5.1 Volatility

The BEKK model provides us with results of the conditional variance for the countries

under study. Figure 4.5 represents the variance of the markets. The contagious effects of

the GFC that began in the USA/UK appear to have been transmitted to the GCC

countries simultaneously, as shown by the spikes in the GCC country stock markets and

the oil market. The leverage effect is clearly operating here, as shown by the increase in

the conditional variance across the GCC countries and international oil market. It is also

obvious that the volatility observed in the multivariate BEKK model estimation for the

four markets changes over time. The volatility for the UAE and Saudi markets was less

than that of the Kuwaiti market and the oil market during the period covered by this

study. This indicates that the Kuwait market was more volatile than the other GCC

country markets during the GFC, suggesting that the Kuwaiti market is more vulnerable

to international market volatility compared to other domestic markets, this results could

be interpreted by referring to table 2.2, Kuwait stock market allowed up 100% foreign

investment while other markets were limited to 49%, this makes this market more

vulnerable to international shocks than other GCC markets.

Figure 4.5: Estimated Conditional Variance for Daily Returns for Oil and Stock

Markets

1.01.5

2.02.5

3.03.5

4.0

0 200 400 600 800 1000

KUWAIT

2.02.5

3.03.5

4.04.5

OIL

12

34

56 SA

24

68

10

0 200 400 600 800 1000

UAE

Cond

itiona

l SD

MGARCH Volatility

121

4.5.2 ARCH: Own and Cross Innovation

ARCH: Own Innovation

The conditional variance covariance equations applied in this chapter successfully

capture the volatility and cross volatility spillovers among oil and GCC country

emerging markets. The intercepts in Panel (Cij) in Table 4.3 quantify the constant

intercepts in the variance and covariance matrix specified in Equation 4.4. Panel (Aij)

quantifies the effects of the lagged own and cross innovations. Panel (Gij) quantifies

lagged own and cross volatility persistence of the GCC markets and the oil market.

The coefficients of the lagged own innovations were significant for all markets,

including oil. These were 0.2052 for Kuwait, 0.3352 for the UAE, 0.2368 for Saudi

Arabia and 0.1276 for oil. The own volatility persistence implied that the risk-return

trade-off (or risk premium in the CAPM framework) in terms of lagged own

innovations is greater in the UAE market than in all other markets tested here. These

results are in agreement with those of Rao (2008) in terms of countries, and also the

findings of Choudhry (1996), who showed that emerging market returns, follow an

ARCH process.

ARCH: Cross Innovations

Panel (Aij) in Table 4.3 shows the set of lagged cross innovations that significantly

spilled over across the GCC and oil markets, indicating the presence of ARCH effects.

The positive cross innovation for Kuwait came from the UAE (0.3352) and Saudi

Arabia (0.0185), while negative cross innovation came from oil (–0.0745). The UAE

experienced volatility spillover from all markets, with the highest negative one from

Kuwait (–0.1812). Saudi Arabia had only one volatility spillover, from oil (0.0677);

while oil had two negative volatility spillovers, from Kuwait (–0.0693) and Saudi

Arabia (–0.0591). Saudi Arabia and oil spilled positive volatility to all markets, with the

exception of a negative spillover from Saudi Arabia to oil (–0.0591) and from oil (–

0.0745) to Kuwait.

It is clear in this study that the Saudi Arabian and oil markets spilled innovations to all

markets. One important finding is that oil received negative innovations from Saudi

122

Arabia, which concurs with the findings of Malik and Hammoudeh (2007). In addition,

oil was found to receive negative innovations from Kuwait. These results imply that

emerging markets in the GCC countries primarily derive their volatility persistence

from within their domestic markets.

Table 4.3: Estimated Coefficient for Variance-Covariance Equations

Coefficient, Std

Error

Coefficient, Std

Error

Coefficient, Std

Error

Coefficient, Std

Error

Cij: intercepts Kuwait (i=1) UAE (i=2) Saudi Arabia (i=3) Oil (i=4)

Ci1 ***0.1446, 0.0275 **–0.2134, 0.0953 ***–0.1332, 0.0481 –0.1367, 0.0888

Ci2 ***0.4778, 0.0628 0.0097, 0.0550 *–0.1427, 0.0813

Ci3 *0.1050, 0.0777 –0.2253, 0.2982

Ci4 N/A

Aij are parameters on the lagged disturbance squares or cross-products (ARCH innovations)

Ai1 ***0.2052, 0.0154 ***–0.1812, 0.0304 –0.0066, 0.0186 *–0.0693, 0.0391

Ai2 –0.0002, 0.0116 ***0.3352, 0.0176 –0.0029, 0.0157 –0.0037, 0.0264

Ai3 *0.0185, 0.0114 ***0.0980, 0.0215 ***0.2368, 0.0133 ***–0.0591, 0.0211

Ai4 ***–0.0745, 0.0092 ***0.0836, 0.0170 ***0.0677, 0.0118 ***0.1276, 0.0206

Gij are coefficient on lagged variance and covariance equations (GARCH volatility cross-persistence)

Gi1 ***0.9661, 0.0048 ***0.0432, 0.0101 0.0002, 0.0067 0.01156, 0.0119

Gi2 0.0018, 0.0043 ***0.9034, 0.0102 –0.0078, 0.0064 0.0117, 0.0103

Gi3 *–0.0008, 0.0027 –0.0078, 0.0067 ***0.9723, 0.0038 **0.0112, 0.0050

Gi4 –0.0046, 0.0035 -0.0003, 0.0060 *–0.0060, 0.0037 ***0.9795, 0.0049

Note: Significance at the *0.10, **0.05 and ***0.001 levels.

4.5.3 GARCH: Own and Cross Volatility Persistence

GARCH: Own Volatility Persistence

An examination of the diagonal values, or the own volatility persistence, reveals that all

the estimated coefficients shown in Panel (Gij) in Table 4.3 are significant; these values

are 0.9661 for Kuwait, 0.9034 for the UAE, 0.9723 for Saudi Arabia and 0.9795 for oil.

Those findings coincide with previous studies by Worthington and Higgs (2004) and

Rao (2008) that support the contention that the emerging markets have significant own

volatility persistence. It is noticeable that in all markets the own volatility spillover

effects are generally within the same range. The own volatility persistence implies that

all markets share the same low risk-return trade-offs in terms of lagged own variance

persistence. This would suggest that the emerging markets and oil markets are all derive

123

their volatility persistence from the same source; which in this case is primarily the

GFC.

GARCH: Cross Volatility Persistence

Panel (Gij) in Table 4.3 shows the cross volatility persistence in all markets. It is

obvious here that Kuwait received cross negative volatility persistence from Saudi

Arabia (–0.0008), while the UAE obtained its cross volatility persistence from Kuwait

(0.0432 per cent) and Saudi Arabia oil (–0.0060). Finally, oil received its cross volatility

persistence from Saudi Arabia (0.0112). In terms of cross volatility persistence in the

GCC markets, the most influential market would appear to be that of the UAE, while

the least important is Kuwait.

It is an important finding here that the oil market received volatility persistence from

Saudi Arabia. These findings coincide with those of Malik and Hammoudeh (2007) also

those results could be interpreted grounded on the correlation between equities and oil

in the GCC markets is essentially based on the belief that rising crude prices will boost

government revenues and therefore expenditure on infrastructure and development

projects, enabling listed firms to benefit (Nekhili & Muhammad 2010).

Hence, it can be concluded that none of the GCC markets receive their volatility

persistence (GARCH term, or long-run persistence) from oil except for Saudi Arabia,

while all markets receive their volatility spillover (ARCH term, or short-run persistence)

from oil and Saudi Arabia.

Comparing the magnitude of the own volatility with that of the cross volatility it is

evident that the own volatility is larger than the cross volatility. This suggests that past

volatility shocks in individual GCC emerging markets have a greater effect on their own

upcoming volatility compared to past volatility shocks in other GCC markets. A closer

examination of Panels (Aij) and (Gij) in Table 4.3 illustrates the cross innovation and

cross volatility persistence and reveals that the spillovers of the GCC markets are

asymmetrical, which confirms that equity returns exhibit asymmetrical conditional

variance behaviour. Hence, the positive values of the innovations have a different effect

than the negative innovations, which means that the markets follow leverage effect

behaviour. Further, the presence of ARCH and GARCH effects in the GCC markets

suggests that emerging markets in the GCC countries derive more of their volatility

124

persistence from within the domestic market. That is, these emerging markets are

susceptible to conditions within the GCC region. Thus, the decision-makers in the GCC

countries should seek increased diversification within the GCC markets to decrease the

opportunities for risk-adverse investors in their markets.

4.5.4 Ljung-Box Test for Standardised Residuals

Finally, the LB Q statistics for the standardised residuals in Table 4.4 reveal that all the

markets are significant (p values of less than 0.05 and 0.001) with the exception of the

UAE. The significance of the LB Q statistics indicates linear dependencies due to the

strong conditional heteroscedasticity. The three markets with LB statistics at 12 degrees

of freedom are significantly greater than the LB statistic for the UAE. These LB

statistics suggest a strong linear dependence in three out of four markets estimated by

the MGARCH model, but when checking the fitted model individually, one cannot

reject the BEKK model.

Table 4.4: LB Test for Standardised Residuals

The Q statistics show that the models are adequate for describing the conditional

heteroscedasticity of the data. From Figure 4.6 it is obvious that all the curves have a

convex shape, except that of oil. This indicates that the distributions of the standardised

residuals are negatively skewed with a long left tail, except that of the UAE, which is

positively skewed. Thus, there is a significant deviation in the tail from the normal QQ

line for all four standardised residuals, and estimates are still consistent under quasi-ML

assumptions.

Kuwait UAE Saudi Arabia Oil

Statistic 105.30 2.16 72.13 20.75

p value 0.00 0.99 0.00 0.05

125

Figure 4.6: QQ Plots of Standardised Residuals

4.6 Summary and Conclusions

Volatility plays an essential role in controlling and forecasting risks in various financial

operations. In this study, volatility is mainly represented in terms of conditional

variances or conditional standard deviations. This chapter examined the transmission of

volatility among four markets during the period from 21/09/2005 to 12/02/2010. A

MGARCH-BEKK model was used to identify the source and magnitude of spillovers

and estimate the own/cross spillovers for the conditional mean returns and own/cross

volatility.

Overall, the results indicate that the model performs well statistically. It is obvious that

over the study period the volatility for the emerging markets of the GCC countries was

relatively similar, with the UAE and Saudi market being less volatile than that Kuwait

and the oil market. In addition, as shown in Figure 4.3, the daily market returns indicate

volatility clustering and leverage effects since the correlations between the regional

markets of Kuwait, the UAE, Saudi Arabia and the oil markets increased significantly

during the GFC.

-5

0

5

10

-2 0 2

KUWAIT

313778988

OIL

707828

837

SA

778113

88

-5

0

5

10

-2 0 2

UAE

931093

9

Quantiles of gauss ian dis tribution

Sta

nd

ard

ize

d R

esi

du

als

QQ-Plot of Standardized Res iduals

126

The coefficients of the lagged own innovations (ARCH terms) are significant in all

markets, and highest in the UAE market. It is clear in this study that the Saudi Arabian

market and the oil market spilled over innovations to all markets. An additional

important finding is that oil received negative innovations from Saudi Arabia, which is

in keeping with other research findings, as well negative innovations from Kuwait.

The own volatility persistence (GARCH terms) imply that all markets share the same

low risk-return trade-off in terms of lagged own variance persistence. It is noticeable

here that oil obtained volatility persistence from Saudi Arabia, while Saudi Arabia did

not receive volatility persistence from any of the markets, apart from a trivial extent

from oil.

These results imply that the emerging markets in the GCC countries mostly derive their

volatility persistence from within their domestic market. Therefore these emerging

markets are susceptible to conditions within the GCC region; and hence international

investors could seek increased diversification within the GCC markets and exploit

opportunities for high returns due to the higher risk-return trade-off.

127

Chapter 5: World Oil Prices and Emerging Stock Markets in

the GCC Countries: VAR Analysis

5.1 Introduction

Many studies have examined the links between oil prices and macroeconomic variables

(e.g., Papapetrou 2001; Brown & Yücel 2002; Cunado & Perez de Garcia 2005; Balaz

& Londarev 2006; Gronwald 2008; Cologni & Manera 2008; Kilian 2008; Lardic &

Mignon 2006, 2008; Lescaroux & Mignon 2008). This work has highlighted the

significant effects of oil price volatility on economic activity in both the advanced and

emerging markets.

Regardless of the studies showing that oil price shocks have significant effects on the

economy, relatively little research has investigated the relationship between oil prices

and stock markets. Further, most of this work has focused on the developed oil

importers; very little has examined emerging markets or oil exporters. In this chapter

the focus will be on three oil exporting countries (Kuwait, Saudi Arabia and the UAE).

GCC countries have many common structural patterns. Portion of the Gulf Cooperation

Council (GCC) countries to the world oil reserves is expected to rise from the current 45

percent to 70 percent during the first decades of the current century. In addition, the

GCC countries captured 25 percent of total world oil crude exports and retain 17 percent

of world proven gas reserves. Based on earlier data filed by Crescent Petroleum, the

Gulf region, comprised of the six countries of the GCC plus Iran and Iraq, holds 56

percent and 40 percent of the world's conventional oil and gas proven reserves. In 2010,

the Gulf Region produced over 25.2 million barrels of oil per day, and 44.6 billion cubic

128

feet of natural gas per day, accounting for over 30 percent of the world's oil production,

15 percent of gas production, and 32 percent of liquefied natural gas (LNG) exports.

Country-wise, Saudi Arabia retains 38.7 percent of world oil reserves whereas shares of

Kuwait, the United Arab Emirates (UAE) and Qatar stand at 14.8 percent, 14.3 percent

and 3.7 percent respectively. With regards to gas reserves, Qatar ranks the third globally

at 46.3 percent followed by Saudi Arabia, the UAE, Kuwait, and Oman at 14.5 percent,

11.1 percent, 3.3 percent, and 1.7 percent respectively (ArabNews 2013).

The GCC markets are important for several reasons; the GCC countries’ markets have

attracted increasing attention over the last decade. In the wake of high oil prices since

2003 and more recently, in 2008, they have achieved high economic growth rates, in

addition the GCC markets differ from those of both the developed economies and the

major emerging countries in that they are largely segmented from the international

markets and are overly sensitive to regional political events (Arouri & Rault 2010).

Jones and Kaul (1996) tested the reaction of advanced stock markets in Canada, the UK,

Japan and the US to oil price shocks on the basis of the standard cash flow dividend

valuation model. They found that for the US and Canada the reaction can be wholly

explained by the impact of oil shocks on cash flows, while the outcome for Japan and

the UK were indecisive. Huang et al. (1996) applied unrestricted VAR analysis, which

proved a significant relation between some US oil company stock returns and oil price

changes. Conversely, they found no evidence of a relationship between oil prices and

market indices such as the S&P 500. In contrast, Sadorsky (1999) applied an

unrestricted VAR with GARCH effects to US monthly data and showed a significant

relationship between oil price changes and aggregate stock returns. Recently, El-Sharif

et al. (2005) examined the links between oil price movements and stock returns in the

UK oil and gas sector. They found strong interrelation between these two variables.

Abu Zarour (2006) applied the VAR model to investigate the relationship between oil

prices and five stock markets in Gulf Countries during the period between May 2001

and May 2005. This study found that the response of these markets to shocks in oil

prices has increased and became more rapid during the rise in oil prices, while only the

Saudi and Omani markets have the power to predict oil prices.

129

Maghyereh and Al-Kandari (2007) found that oil prices impact the stock price indices in

the GCC countries in a non-linear fashion. Their results supported the statistical analysis

of a non-linear modelling relationship between oil and the economy, which is consistent

with certain authors, such as Mork et al. (1994) and Hamilton (2000). Figure 5.1 shows

how the stock market in the GCC countries and oil prices are interrelated and show a

common trend.

Note: Oil prices have been rescaled to be comparable with the average of the GCC stock

market indices.

Figure 5.1: Five GCC Stock Markets and Brent Oil Prices Between 25/05/2006 and

18/11/2009

Miller and Ratti (2009) analysed the long-run relationship between the world price of

crude oil and international stock markets over the period from January 1971 to March

2008. They found an obvious positive statistically significant co-integrating long-run

relationship between real stock prices for six OECD countries and world real oil prices

from January 1971 until May 1980, and again from February 1988 and September 1998.

This suggests that stock market prices increase as the oil price decreases, and decrease

as the oil price increases over the long run. On the other hand, they found an

insignificant relationship between May 1980 and February 1988. Figure 5.2 shows a

time series plot of the real stock market prices and real crude oil prices for six countries

from January 1971 through March 2008, with crude oil measured on the reversed right

hand side axis.

0

200

400

600

800

1,000

1,200

1,400

1,600

100 200 300 400 500 600 700 800

KUWAIT OIL SA UAE

130

Source: Miller and Ratti (2009).

Note: The left axis represents stock prices and the right axis oil, with the right hand axis

reserved. All series (log prices) are normalised to begin at zero.

Figure 5.2: Stock Market Prices and the Crude Oil Price January 1971–March

2008

In order to study how the rise in oil prices affected the stock markets and the dynamic

interrelation between them before and during the GFC, the whole period of the study

was divided into three sub-periods and a VAR system was estimated for each period.

The first period (normal period- before the GFC) was from 21 September 2005 to 6

October 2006; the second period (rise period) was from 9 October 2006 to 13 October

2008, which included the remarkable rise in oil prices when oil tripled in value from a

minimum of $49.95 per barrel to $143.95 per barrel as consequence of the GFC; while

the third period (GFC, Fall) was between 14 October 2008 to 11 February 2010.


The daily data employed in this study is drawn from the weighted equity market indices

of four major markets and oil (the Europe Brent Spot Price); namely, the UAE, Kuwait,

and Saudi Arabia. Daily data were employed for the period between 21/09/2005 and

11/02/2010; the stock market data were obtained from the MSCI and the S&P Saudi

Arabia. The S&P Saudi Arabia is a standalone index designed to capture 80 per cent or

more of the local market capitalisation. The daily data for crude oil price came from the

US Department of Energy EIA. All indices are based on US dollars and do not include

dividends. The indices include small, medium and large caps, except for Saudi Arabia,

131

which captured 80 per cent of their market. The return for each market plus oil is

expressed as a percentage computed by multiplying the first difference of the logarithm

of the stock market by 100. 100)/( 1 ititi PPLOGP

where iP denotes the rate of

change of itP.

Table 5.1 presents descriptive statistics for each market return for the period between

21/09/2005 and 11/02/2010, while Figure 5.3 shows markets daily returns for the same

period. Table 5.1 shows sample means, medians, maximums, minimums, standard

deviations, skewness, kurtosis and the Jarque-Bera statistics and p values for the daily

dollar return. As shown in Table 5.1, the volatility (measured by standard deviation) for

the oil market (2.4741) is the second highest after the UAE market, since oil tripled

during the study period from a minimum value of $49.95 to $143.95.

The distributional properties of the return series appear to be non-normal, since all the

markets have negative skewness except for that of the UAE. The kurtosis in all markets,

both developed and emerging, exceeds 3, indicating a leptokurtic distribution. The final

statistic in Table 5.1 is the calculated Jarque-Bera statistic and its corresponding p value

used to test the null hypotheses that the daily distributions of returns are normally

distributed. With all p values equal to zero, the Jarque-Bera statistics reject the null

hypotheses that the returns for developed and emerging markets are well approximated

by normal distribution.

Table 5.1: Summary Statistics of Daily Returns for Three GCC Country Stock

Markets and Oil

KUWAIT OIL SAUDI ARABIA UAE

Mean –0.0374 0.0082 –0.0696 –0.0880

Median 0.0000 0.0629 0.0000 0.0000

Maximum 7.1108 12.2218 16.0124 34.6479

Minimum –10.6353 –16.8320 –13.5398 –16.2348

Std. Dev. 1.5897 2.4741 2.1443 2.4823

Skewness –1.3192 –0.1258 –1.0075 1.9609

Kurtosis 12.0975 7.2229 13.5015 42.1677

Jarque-Bera 4,288.225 855.321 5,464.692 7,4052.81

Probability 0.0000 0.0000 0.0000 0.0000

Observations 1,147 1,147 1,147 1,147

132

Figure 5.3: Market Daily Returns Between 21/09/2005 and 11/02/2010

5.3 Methodology


Each series was tested for unit roots (markets return). The null hypothesis of the

existence of a unit root (non-stationary) was tested against the alternative hypothesis of

stationary variables using the ADF statistic (Dickey & Fuller 1981). Automatic

selection of lags based on the SIC was employed; Table A.2 in the Appendix reports the

results, which show that all series were stationary.

-12

-8

-4

0

4

8

250 500 750 1000

KUWAIT

-20

-15

-10

-5

0

5

10

15

250 500 750 1000

OIL

-15

-10

-5

0

5

10

15

20

250 500 750 1000

SA

-20

-10

0

10

20

30

40

250 500 750 1000

UAE

133

5.3.2 VAR Methodology

VAR methodology is commonly used for forecasting systems of interrelated time series

and for analysing the dynamic impact of random disturbances on the system of variables.

The VAR approach bypasses the need for structural modelling by treating every

variable as endogenous within the system as a function of the lagged values of all

endogenous variables in the system. The term autoregressive is due to the appearance of

the lagged values of the dependent variable on the right-hand side and the term vector is

due to the fact that a vector of two (or more) variables is included in the system model

(Hung 2009). The mathematical representation of a VAR system is:

tttt eYAYAY .....*......* 11 (5.1)

Or

pt

t

t

t

p

kt

kt

kt

kt

ppppp

p

p

p

p

t

t

t

t

ppppp

p

p

p

P

t

t

t

t

e

e

e

e

Y

Y

Y

Y

AAAA

AAAA

AAAA

AAAA

Y

Y

Y

Y

AAAA

AAAA

AAAA

AAAA

Y

Y

Y

Y

......

'...'''

...............

'...'''

'...'''

'...'''

...

...

...

...............

...

...

...

...

3

2

1

2

2

1

321

3333231

2232221

1131211

1

2

1

2

1

1

1

321

3333231

2232221

1131211

3

2

1

Where p is the number of variables be considered in the system, k is the number of lags

be considered in the system, kttt YYY ,......., 1 , are the p1 vector of variables, and

the AA &,...... are the pp matrices of coefficients to be estimated,

te is a p1

vector of innovations that may be contemporaneously correlated but are uncorrelated

with their own lagged values and uncorrelated with all of the right-hand side variables.

Since there are only lagged values of the endogenous variables appearing on the right-

hand side of the equations, simultaneity is not an issue and OLS analysis yields

consistent estimates. Moreover, even though the innovations may be

contemporaneously correlated, OLS is efficient and equivalent to generalised least

squares since all equations have identical repressor.

For this study, suppose that stock market (SK) and oil prices (O) are jointly determined

by a VAR and let a constant be the only exogenous variable. Assuming that the VAR

contains two lagged values of the endogenous variables, it may be written as:

134

t

t

t

t

t

t

t

t

e

e

C

C

O

SK

bb

bb

O

SK

aa

aa

O

SK

2

1

2

1

2

2

2221

1211

1

1

2221

1211 (5.2)

Or

t

k

i

k

i

tiitit eObSKaCSK 1

1 1

1111

t

k

i

k

i

tiitit eObSKaCO 2

1 1

1222

where ijijij cba &, are the parameters to be estimated, the thji ),( components reveal

the response of the ith market return to a unit random shock in the jth market return

after k periods. This represents the impulse response of the ith market in k periods

after a shock of one standard error in the jth market. sei ' are the stochastic error terms

and are called innovations or shocks in the language of VAR. Additionally, the variance

decomposition of the forecast error gives the percentage of unexpected variation in each

market’s return that is produced by shocks from other returns in the system.

The VAR model requires the determination of the appropriate lag structure in the

system. In this study four lags have been chosen, as oil is the main reference. Lag

structure is chosen based on the smallest value of the Akaike Information Criterion

(AIC) or SIC of the VAR to determine the appropriate lags (Quantitative Micro

Software 2007).

135

5.3.3 VAR Estimation

Table A.3 in the Appendix presents the estimation results of the VAR system for three

GCC countries, Saudi Arabia, Kuwait and the UAE, and oil returns for the first period

(Normal). The results indicated that there was no significant relationship between oil

prices and the three countries on a daily basis, but that there was a bidirectional

prediction between oil and Saudi Arabia, as oil prices were able to predict the Saudi

Arabian stock market, and oil prices were predicted by the Saudi Arabian stock market.

Further, a one-way directional predictive relationship existed from the Kuwaiti market

to the Saudi Arabian stock market.

During the second period (Rise), and after the rise in oil prices, the results changed.

Table A.4 in the Appendix presents the VAR estimation for this period. The results

indicate that oil prices were able to predict the UAE, Kuwait and Saudi Arabia stock

markets, while oil prices could be predicted by the stock markets of Kuwait and the

UAE.

Finally, Table A.5 in the Appendix presents the VAR estimation for the third period

(GFC, Fall), which show that oil prices were able predict the stock markets of Kuwait,

the UAE and Saudi Arabia, while oil prices could be predicted by the Kuwaiti and UAE

stock markets. The results of the VAR system for the second and third periods reflect

the significant role played by the sharp increase and decrease in oil prices, which has, in

turn, brought a predictive power of oil prices on those markets in comparison with the

first period, during which oil prices were normal. Additionally, it is notable that these

periods have this predictive power despite the sharp fluctuations that occurred

throughout them. This indicates the existence of an endogenous variable/s involved

during the GFC. These results do not seem when one takes into consideration that the

GCC countries essentially depend, to varying degrees, on oil, and are the world’s largest

oil exporter, with largest oil reserves in the world. Further, these results are in harmony

with those of Sadorsky (1999), Hammoudeh and Aleisa (2004), Abu Zarour (2006),

Miller and Ratti (2009) and Arouri and Rault (2010), who found that there is a long-

term relationship between oil prices and GCC stock markets.

136

5.4 Variance Decomposition

Variance decomposition analysis measures the percentage of the forecast error of a

market return that is explained by another market or oil returns. It indicates the relative

impact that one market has upon another and the oil return within the VAR system.

Variance decomposition enables the assessment of the economic significance of this

impact as a percentage of the forecast error for a variable sum to one. The

orthogonalisation procedure of the VAR system decomposes the forecast error variance,

the component that measures the fraction in stock return of a particular market

explained by innovations in each of the four indices (Abu Zarour 2006).

Table (5.2) provides the variance decomposition of the five-day forecast error of each

index, accumulated by innovations in each of the four indices for the first period. Each

row indicates the percentage of forecast error variance explained by the market

indicated in the first column; for instance, at 5 period horizons for (KUWAIT) the 2.0

per cent of forecast error variance in Kuwait is explained by the oil market. The results

indicate that most markets and oil returns are strongly exogenous, in the sense that the

percentage of the error variance accounted by the Kuwaiti market is around 94 per cent

at time horizon 5 while the percentage of the foreign explanatory power, as indicated by

the foreign column, is insignificant, reaching in the best case 6 per cent at time horizon

5. The UAE market is the least exogenous of the GCC markets, with 11 per cent error

variance explained by other markets, with 3.9 per cent of this variance explained by the

Kuwaiti market. The oil market was the next least exogenous, with 8.0 per cent of the

error variance explained by other markets, and 6.4 per cent of this variance explained by

the Kuwaiti market. Thus, we say that the oil market plays a minor role in the forecast

of error in the GCC markets’ variance except for Saudi Arabia; these results concur with

the findings in the VAR estimation discussed above.

137

Table 5.2: Variance Decomposition for the Forecast Error of Daily Market

Returns for GCC and Oil Markets During the First Period (Normal)

KUWAIT

Period Std. Error KUWAIT OIL SAUDI

ARABIA UAE

1 1.139847 100.0000 0.000000 0.000000 0.000000

2 1.149970 98.24816 0.972759 0.658091 0.120986

3 1.157013 97.16740 1.025940 1.481000 0.325664

4 1.163289 96.70330 1.106884 1.750082 0.439735

5 1.177148 94.67929 2.007711 2.840206 0.472791

OIL


ARABIA UAE

1 1.903694 0.629903 99.37010 0.000000 0.000000

2 1.954489 5.273194 94.28235 0.223534 0.220923

3 1.970671 5.826345 92.86603 0.892691 0.414930

4 1.977350 6.339541 92.35605 0.891726 0.412687

5 1.980021 6.373565 92.22235 0.895210 0.508876

SAUDI ARABIA


ARABIA UAE

1 2.752680 2.712345 0.008747 97.27891 0.000000

2 2.756845 2.753276 0.056534 97.13714 0.053051

3 2.808901 2.668216 2.528412 94.58006 0.223309

4 2.809301 2.667889 2.527744 94.55384 0.250528

5 2.834249 2.772499 3.196208 93.45232 0.578969

UAE


ARABIA UAE

1 3.005112 1.851486 1.973082 1.508563 94.66687

2 3.040383 3.040140 1.959688 2.516417 92.48376

3 3.053824 3.111747 2.092574 3.030534 91.76514

4 3.077497 3.799581 2.184268 3.009557 91.00659

5 3.106525 3.947780 3.022588 3.089383 89.94025

Cholesky Ordering: KUWAIT OIL SAUDI ARABIA UAE

138

Table 5.3 presents the variance decomposition for the second period. After the sharp

rise in oil prices, it was found that Saudi Arabia and the UAE had more endogenous

power than during the first period, while Kuwait and oil had less. The percentage of the

foreign explanatory power is relatively strong; it exceeds 35 per cent for the UAE and

18 per cent for Saudi Arabia. The Kuwait market played an important role within the

GCC markets during this period, while Saudi Arabia was second and oil ranked third,

since 16 per cent of the forecast error variance in the Saudi market was explained by the

Kuwaiti market, and 21 per cent in the UAE market. In addition, the markets of Saudi

Arabia and Kuwait showed a bidirectional role.

139


Returns for GCC and Oil Markets During the Second Period (Rise)

KUWAIT


ARABIA UAE

1 1.208220 100.0000 0.000000 0.000000 0.000000

2 1.213769 99.59790 0.003930 0.352785 0.045383

3 1.224536 97.90216 0.522248 0.864870 0.710725

4 1.229846 97.55892 0.672973 1.042424 0.725687

5 1.232223 97.18313 0.672071 1.069441 1.075359

OIL


ARABIA UAE

1 1.961461 0.807489 99.19251 0.000000 0.000000

2 1.974714 1.080658 98.41040 0.486316 0.022628

3 1.983530 1.291431 97.53821 0.552262 0.618095

4 1.987916 1.370289 97.10920 0.877750 0.642761

5 2.021033 3.550788 94.07428 1.083782 1.291147

SAUDI ARABIA


ARABIA UAE

1 1.885469 15.39750 0.129478 84.47303 0.000000

2 1.913639 16.39029 0.160644 82.60154 0.847531

3 1.916526 16.36080 0.352747 82.37570 0.910752

4 1.924574 16.40173 0.380253 82.04185 1.176159

5 1.937346 16.21420 1.049867 81.45008 1.285853

UAE


ARABIA UAE

1 1.802912 20.39602 1.513252 10.64736 67.44337

2 1.836764 21.73675 1.458728 10.78764 66.01689

3 1.842966 21.77795 1.500689 11.09365 65.62772

4 1.856009 22.13382 1.482207 11.05475 65.32923

5 1.875664 21.86540 2.980586 10.97351 64.18051


140

Table 5.4 presents the variance decomposition for the third period. It is noticeable that

fall and rise in oil prices had almost the same effect on the markets in the GCC

countries. Again, the Kuwaiti market played a major role during this period while the

Saudi Arabian ranked second and oil ranked third. Sixteen per cent of the forecast error

variance in the Saudi market was explained by the Kuwaiti market, and 21 per cent of

UAE market. In addition, a one way prediction effect was found on the UAE market,

caused by the Saudi Arabian and Kuwaiti markets. In general, the results achieved in

this study will help to identify the dominant market system that manipulates all other

markets and links their interdependence, which in this case is the Kuwaiti market. Those

results are justified by demonstrating that the GCC Countries stock markets have higher

correlations in the regional aspect than the global position and the stock markets of the

Kuwait is the oldest regulated mature market in the Gulf area which make Kuwait stock

market a major dominator on explaining those results (Fayyad, A. & Daly, K. 2010).

141


Returns for GCC and Oil Markets During the Third Period (Fall)

KUWAIT


ARABIA UAE

1 2.213549 100.0000 0.000000 0.000000 0.000000

2 2.226793 99.05483 0.750300 0.112767 0.082099

3 2.257320 97.69495 1.066185 0.309792 0.929078

4 2.273641 97.13500 1.556492 0.392129 0.916378

5 2.279923 97.13313 1.548091 0.401246 0.917534

OIL


ARABIA UAE

1 3.319874 0.286457 99.71354 0.000000 0.000000

2 3.343865 0.302539 98.30025 0.623949 0.773257

3 3.357747 0.305893 97.50192 1.423937 0.768249

4 3.376429 0.396275 96.42592 2.405923 0.771879

5 3.394031 0.528217 95.43636 3.233705 0.801717

SAUDI ARABIA


ARABIA UAE

1 1.883778 7.366008 4.265877 88.36811 0.000000

2 1.912146 7.248181 6.529024 85.82302 0.399779

3 1.942851 8.545699 7.570213 83.21943 0.664661

4 1.955583 8.659159 8.399909 82.26547 0.675465

5 1.963792 8.656197 8.723369 81.91988 0.700558

UAE


ARABIA UAE

1 2.663403 12.20014 3.550643 14.13702 70.11220

2 2.760925 12.10473 8.376069 14.00541 65.51380

3 2.840716 13.57305 8.493457 13.53614 64.39735

4 2.852434 13.50506 8.600494 13.45228 64.44217

5 2.865784 13.41135 9.209231 13.45847 63.92095


142

5.5 Impulse Response

The estimated impulse response of the VAR system enables an examination of how

each of the four variables responds to innovations from other variables in the system.

Tables 5.5–5.7 and Figures 5.4–5.6 summarise the accumulated responses of all markets

to a one standard deviation shock in each of the GCC markets for the three periods of

the study.

Table 5.5 and Figure 5.4 presents the accumulated response of all markets’ returns to a

one standard deviation shock for the first period (Normal). In general, the responses

were small and declined very slowly, indicating that these markets are not efficient in

responding to shocks generating from oil returns. However, it is noticeable that a shock

originating in oil returns had a major and persistent impact on the Kuwaiti and Saudi

Arabian markets rather than the other GCC markets. It took the Saudi Arabian market

three days to begin to respond to the oil return shock, while Kuwait responded from the

first day. In addition, all markets responded negatively, while the oil market responded

positively to shocks in oil returns. It is also notable that a shock originating in UAE

returns had a major impact on the Saudi Arabian and Kuwaiti markets, which responded

from the first day. In addition, all markets responded negatively to oil shocks, while all

markets, except for oil, responded positively to shocks in UAE returns.

143

Table 5.5: Accumulated Response of All Markets to One Standard Deviation

Innovation for the First Period (Normal)

KUWAIT

Period KUWAIT OIL SAUDI ARABIA UAE

1 1.139847 0.000000 0.000000 0.000000

2 1.136333 –0.113420 0.093289 –0.040000

3 1.175012 –0.083924 0.198755 0.012533

4 1.263722 –0.048643 0.260859 0.052421

5 1.321350 0.064678 0.386052 0.076930

6 1.312236 0.080087 0.396367 0.102590

OIL


1 0.151089 1.897689 0.000000 0.000000

2 –0.271533 1.877991 –0.092407 0.091866

3 –0.113954 1.808132 –0.254052 0.004261

4 –0.260930 1.740683 –0.268111 –0.000401

5 –0.305695 1.673451 –0.252917 –0.062164

6 –0.323047 1.647764 –0.304906 –0.080127

SAUDI ARABIA


1 0.453344 0.025745 2.714970 0.000000

2 0.392244 0.086027 2.822426 0.063498

3 0.356666 0.527833 2.540145 0.180061

4 0.362517 0.529888 2.547795 0.226463

5 0.472788 0.769171 2.759086 0.389976

6 0.382727 0.760147 2.767002 0.427013

UAE


1 0.408904 –0.422117 0.369099 2.923881

2 0.746285 –0.476606 0.679553 2.931194

3 0.650534 –0.358293 0.903176 3.024686

4 0.914469 –0.466559 0.854064 3.272379

5 0.769133 –0.175315 0.968545 3.518256

6 0.730048 –0.097413 1.063947 3.603047


144


Innovation for the First Period (Normal)

For the second period, which witnessed the sharp rise in oil prices, there was a different

picture for the relationships between the GCC stock markets and oil returns. Table 5.6

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of KUWAIT to KUWAIT

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of KUWAIT to OIL

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of KUWAIT to SA

-0.5

0.0

0.5

1.0

1.5

2.0

1 2 3 4 5 6 7 8 9 10

Accumulated Response of KUWAIT to UAE

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10

Accumulated Response of OIL to KUWAIT

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10

Accumulated Response of OIL to OIL

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10

Accumulated Response of OIL to SA

-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10

Accumulated Response of OIL to UAE

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Accumulated Response of SA to KUWAIT

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Accumulated Response of SA to OIL

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Accumulated Response of SA to SA

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Accumulated Response of SA to UAE

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

Accumulated Response of UAE to KUWAIT

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

Accumulated Response of UAE to OIL

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

Accumulated Response of UAE to SA

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10

Accumulated Response of UAE to UAE

Accumulated Response to Cholesky One S.D. Innovations ± 2 S.E.

145

and Figure 5.5 presents the accumulated response of all markets’ returns to one standard

deviation shock for the second period (Rise).

The Kuwaiti market was influenced the most, followed by the Saudi Arabian market

and then the oil market. These markets reacted quickly and relatively efficiently to

shocks originating in oil returns, since their reactions tapered off and started declining

after Day 7. However, the Saudi Arabian market showed a small and slow response to

oil shocks, while the Kuwaiti and UAE markets showed large and rapid responses.

Table 5.6: Accumulated Responses of All Markets to One Standard Deviation

Innovation for the Second Period (Rise)

KUWAIT


1 1.208220 0.000000 0.000000 0.000000

2 1.294922 0.007610 0.072093 0.025857

3 1.268118 –0.080556 –0.016062 0.125800

4 1.181117 –0.129010 –0.068961 0.107941

5 1.183023 –0.134079 –0.047253 0.034785

6 1.179513 –0.119241 –0.033177 0.019086

7 1.157950 –0.119239 –0.027963 0.008904

10 1.152294 –0.131796 –0.029150 0.008334

OIL


1 0.176258 1.953525 0.000000 0.000000

2 0.281488 2.099284 0.137709 –0.029705

3 0.374599 2.094158 0.190286 0.123383

4 0.432403 2.087990 0.076449 0.156284

5 0.733872 2.158443 –0.021433 0.321623

6 0.796470 2.174321 0.003409 0.354485

7 0.779835 2.174378 –0.030838 0.363444

10 0.771787 2.170721 –0.057793 0.343867

SAUDI ARABIA

Period KUWAIT OIL Saudi Arabia UAE

1 0.739851 0.067845 1.732919 0.000000

2 0.969709 0.103620 1.880801 0.176172

3 0.996720 0.019514 1.909806 0.225324

4 1.077798 0.053098 2.024250 0.325883

146

5 1.045372 0.212221 2.159362 0.257347

6 1.047754 0.255817 2.189450 0.243657

7 1.052823 0.266641 2.227382 0.252093

10 1.101018 0.315226 2.250425 0.276005

UAE

Period KUWAIT OIL Saudi Arabia UAE

1 0.814230 0.221784 0.588295 1.480622

2 1.079489 0.226784 0.721904 1.667630

3 1.159240 0.184854 0.608522 1.710618

4 1.310123 0.175508 0.671865 1.856838

5 1.227719 0.407460 0.744320 1.770229

6 1.201485 0.443559 0.744093 1.734930

7 1.214134 0.441421 0.772752 1.748463

10 1.263369 0.476768 0.774151 1.769333


147


Innovation for the Second Period (Rise)

Table 5.7 and Figure 5.6 present the accumulated responses of all markets’ returns to

one standard deviation shock for the third period (Fall). Despite the falling period, a

-0.5

0.0

0.5

1.0

1.5

1 2 3 4 5 6 7 8 9 10


-0.5

0.0

0.5

1.0

1.5

1 2 3 4 5 6 7 8 9 10


-0.5

0.0

0.5

1.0

1.5

1 2 3 4 5 6 7 8 9 10


-0.5

0.0

0.5

1.0

1.5

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


0

1

2

1 2 3 4 5 6 7 8 9 10


0

1

2

1 2 3 4 5 6 7 8 9 10


0

1

2

1 2 3 4 5 6 7 8 9 10


0

1

2

1 2 3 4 5 6 7 8 9 10



148

significant positive response was seen for all markets, except the Saudi Arabian market,

in response to shocks from the Kuwaiti market and oil. However, these results were

obtained during the height of the GFC. For the GCC markets and oil it is notable that oil

has a major positive and persistent response from the UAE market, followed by a

response from Saudi Arabia, while Kuwait and Saudi Arabia have a major impulse

response from the UAE. Regardless of the different magnitudes of the impulse response

values, some useful observations can be made from Tables 5.5–5.7.

Table 5.7: Accumulated Response of All Markets to One Standard Deviation

Innovation for the Third Period (Fall)

KUWAIT


1 2.213549 0.000000 0.000000 0.000000

2 2.322822 0.192885 –0.074777 0.063804

3 2.580309 0.323739 –0.175741 0.271819

4 2.788392 0.485401 –0.108768 0.266295

5 2.621983 0.482472 –0.084558 0.248344

6 2.662162 0.474176 –0.126068 0.216741

7 2.641128 0.484360 –0.129054 0.183205

9 2.624573 0.464863 –0.144716 0.188553

10 2.617648 0.458635 –0.139396 0.191873

OIL


1 0.177685 3.315115 0.000000 0.000000

2 0.225185 3.277842 0.264133 0.294043

3 0.199503 3.315755 –0.037155 0.281605

4 0.302889 3.315984 0.300099 0.244451

5 0.428073 3.285606 –0.013306 0.310459

6 0.502147 3.331890 0.006338 0.328116

7 0.491782 3.322201 –0.019464 0.362846

8 0.514801 3.325856 –0.005259 0.350959

10 0.510440 3.308565 –0.031207 0.337019

SAUDI ARABIA


1 0.511265 0.389076 1.770833 0.000000

2 0.451067 0.684611 1.725006 –0.120901

3 0.690977 0.901474 1.782550 –0.018570

149

4 0.598345 1.089856 1.851998 0.008689

5 0.546656 1.213049 1.966626 0.043113

6 0.581000 1.224187 1.974547 0.036608

7 0.565297 1.241324 1.968758 0.031711

9 0.584142 1.260042 1.959999 0.042551

10 0.582770 1.258967 1.961805 0.046206

UAE


1 0.930292 0.501869 1.001419 2.230148

2 1.169594 1.123649 1.255882 2.372862

3 1.585035 1.340237 1.413140 2.823113

4 1.644386 1.460133 1.366165 2.607227

5 1.593264 1.697955 1.262348 2.527308

6 1.585484 1.676019 1.209252 2.460211

7 1.525970 1.692197 1.157914 2.431670

10 1.549158 1.614385 1.166683 2.460707


150


Innovation for the Third Period (Fall)

For the first period (Normal) the response of the GCC country stock markets to a

shock in oil returns seemed to be small, negative, and slowly increasing from

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


-1

0

1

2

3

1 2 3 4 5 6 7 8 9 10


0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


0

1

2

3

4

1 2 3 4 5 6 7 8 9 10


0

1

2

3

4

1 2 3 4 5 6 7 8 9 10



151

Day 8. For a shock from the Saudi market the response was positive for all

markets, including oil.

For the second period (Rise) and after oil prices dramatically increased, the

interaction between oil returns and all stock markets increased, particularly for

the Kuwaiti and UAE markets, which exhibited large, rapid responses to oil

shocks within a three to four day horizon. In contrast, Saudi Arabia showed

slower, smaller responses than the other markets.

The Kuwaiti market exerted the greatest effect after oil prices roise in the second

period. This was not unexpected, since 35 per cent of Kuwait’s GDP comes

from oil, ranking Kuwait second after Saudi Arabia (44 per cent) (Arab

Monetary Fund 2007).

For the third period (GFC, Fall), the relationships between oil returns and all

stock markets increased. They exhibited large and rapid responses to oil shocks

within a four-day horizon. The results for the UAE results were not surprising,

since the UAE market is more liberalised than the other GCC markets, with half

of its listed companies allowing non-GCC stock ownership (Bley & Chen 2006).

Kuwait showed a greater response within the eight-day horizon compared to the

six-day horizon in the rise period.

It is notable that during the first period that the Saudi Arabian market received

the largest shock of all markets, from the UAE, while the UAE market received

the smallest shock, from oil. For the second period, Kuwait received the largest

shock of all markets, from the UAE, while Saudi Arabia received the smallest

shock, from Kuwait. Finally, during the third period, oil received the biggest

shock of all markets, from the UAE, while the UAE received the smallest shock,

from Saudi Arabia.

The UAE had the most endogenous power in all the other markets during all

three periods, which may be explained by the highly liberalised power this

market possesses.

These results reflect the significant impact of increases in oil prices on GCC stock

markets. This is not unexpected, since the GCC countries produce about 21 per cent of

the world’s daily oil production, and they possess about 43 per cent of the world’s oil

reserves.

152

From a political point of view, the results suggest that there is a significant relationship

between the GCC markets, which points to these countries’ predictive power for oil

prices. This also indicates that their political and economic stability has a direct impact

on oil price stability. Oil prices do affect the GCC markets, and the GCC markets affect

oil prices, with the most significant effect on oil prices being seen during the third

period and originating from the UAE market. A highly priority for decision-makers in

GCC countries must be to secure diverse income resources and try to increase the

contribution of the non-oil sector to national GDP. The GCC economies are dominated

by oil and gas sector which contributed 50.6% to the GCC GDP in 2011. The revenue

from oil and gas has contributed an enormous boost to the rest of the economy via

government spending (BCIO 2011). This leads to risk as a result of linking these

countries’ major economic sectors to oil prices, which then leads to risks in the oil

market, creating a cycle that influences the performance of their stock markets. This is

especially pertinent as these countries prepare for a single currency and other integration

mechanisms in the near future.

5.6 Conclusion

This chapter used VAR analysis to examine the effect of oil price fluctuations on

selected GCC country stock markets, as well as the influence of these stock markets on

oil prices, by examining the dynamic structure between three member countries of the

oil-rich GCC and oil prices. In addition to Brent Spot Oil prices, this study included the

UAE, Kuwaiti and Saudi stock markets, analysing 100*log of daily indices from 21

September 2005 to 11 February 2010. To achieve the primary objective of the study, the

whole period was divided into three sub-periods; the first from 21 September 2005 to 6

October 2006; the second, encompassing a steep rise in oil prices, from 9 October 2006

to 13 October 2008; while the third, from14 October 2008 to 11 February 2010,

included the GFC.

The empirical results suggest the following:

1. For the first period, oil returns could predict the Saudi Arabian market, but could

not be predicted by any of the GCC stock markets. However, after the oil price

153

rises during the second period, oil could predict all three GCC stock markets,

and could be predicted by the Kuwaiti and UAE markets. Finally, for the third

period oil could predict, and be predicted by, the Kuwaiti and UAE markets.

2. The variance decomposition indicated that all variables in the system were

generally exogenous, while in the second period, the results appeared to be more

endogenous since the error forecast reached up to 35 per cent for the UAE

market. This could be explained by other stock markets and oil, while for the

Saudi Arabian market the error forecast reached up to 19 per cent. For the third

period the results appeared more likely to be endogenous.

3. The impulse response functions indicated that for the first period the responses

of the GCC markets to shocks in oil returns were generally small, while during

the second and third periods these responses were large, especially for the

Kuwait, Saudi Arabia and oil markets.

4. All markets showed a large response to shocks generated by oil, and showed

memory during the second and third periods covering the GFC.

154

Chapter Six: Oil Price Shock Effects on Macroeconomic

Fundamentals of the GCC Countries

6.1 Introduction

Recent changes in oil prices in the global economy have been rapid and unprecedented.

Oil prices have traditionally been both highly volatile and subject to exogenous supply

shocks from natural disasters, political events and financial crises. Furthermore, the

demand for oil products is expected to increase for the predictable future, driven by the

increased demand for oil by China and India. Over the past three decades China’s

economy has grown 15-fold to become the world’s second largest economy after the US.

By 2050, India is expected to be the world’s third largest economy. Today, Asia

accounts for 50 per cent of GCC trade, a substantial change from 30 years ago when

some 80 per cent of GCC trade was with the developed economies of Europe and the

US (International Institute for Strategic Studies 2012).

The GFC beginning in September 2008 was followed by a year of less acute financial

turmoil, which substantially reinforced the cyclical downturn in oil prices. At the

beginning of 2008 the basket prices of oil was less USD 100 per barrel, by the middle of

the year it was approximately USD 140 per barrel and by the end of the year the price

was below USD 40 per barrel. The surge in oil prices through 2008 enlarged the current

account surplus, despite remarkable increases in imports. Through average oil prices 45

per cent higher in 2008 than in 2007 and a modest increase in the production of oil, the

external current account surplus was increased to $239 billion in 2008, equivalent to 21

per cent of GDP. Oil export earnings reached up to 51 per cent of GDP in 2008, as

compared with 45 per cent in 2007 (Institute of International Finance & QNB 2010).

155

Figure 6.1 shows the trend price of the Brent Daily Spot Price for the 24 years 1987–

2011 versus the GDP of the selected GCC countries. The correlated trend between them

is notable, except for the period which witnessed the incursion of Kuwait by Iraq in

1990. The consequences of this action were catastrophic for Kuwait.

Source: US Department of Energy EIA and IMF.

Figure 6.1: Average Oil Price and GDP for Selected GCC Countries 1987–2011

The GCC continues to play an important role in the stability of the international oil

market. In 2010 the GCC controlled 36 per cent of the world’s proven oil reserves and

22 per cent of proven gas reserves. As a cluster they remain the world’s largest producer

of crude oil, at up to 22 per cent of global oil exports (Figure 6.2) The GCC countries

have often helped to decrease volatility in the world energy market.

-60

-40

-20

0

20

40

60

80

100

120

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 Ave

rage

Oil

Pri

ce &

GD

P

Year

Oil

Kuwait

SA

UAE

156

Source: BP Statistical Review of World Energy.

Figure 6.2: Geographical Distribution of Proven Oil and Gas Reserves

Table 6.1 shows the GCC countries’ market capitalisation of listed companies and the

corresponding contribution of oil as a percentage of GDP for each country. Oil is

responsible for between 22 per cent of Bahrain’s total output to 44 per cent for the

Kingdom of Saudi Arabia. The GCC markets differ from those of the developed and

other major emerging market economies in that they are predominately segmented,

largely isolated from the international markets and are overly sensitive to regional

political events (Arouri & Rault 2010).

Table 6.1: GCC Economies, Stock markets and Oil in 2007

Market No. of

companies*

Market

capitalisation

($ billion)

Market

capitalisation

(per cent GDP)*

Oil(per cent

GDP)

Bahrain 50 21.22 158 22

Kuwait 175 193.50 190 35

Oman 119 22.70 40 41

Qatar 40 95.50 222 42

UAE 99 240.80 177 32

Saudi Arabia 81 522.70 202 44

Source: Arab Monetary Fund and Emerging Markets Database.

* Numbers in 2006

10%

36%

24%

30%

34%

22% 22% 22%

0%

5%

10%

15%

20%

25%

30%

35%

40%

EU & Eurasia GCC Non GCC & MENA Rest of The World

Crude Oil & Gas Reserves: Share in world (%)

OIL GAS

157

6.2 Background

The relationships between real economic variables and financial asset values have long

been topics of economic research. Since the US-triggered financial crisis in 2008,

stemming from the Lehman Brothers filing for bankruptcy, the sharp fluctuation of oil

prices and volatile swings in the major stock markets have caused great concern

regarding economic growth in both the developed and developing countries. The aim of

this chapter is to investigate the impact of oil prices shock on real economic activity in

three economies of oil exporting countries: Kuwait, the UAE and Saudi Arabia.

A brief review of related empirical investigations is provided below. First, the

interrelation between oil price change and real economic activity is a significant issue

that has been studied for most industrialised countries (Hamilton 1983, 2003; Gisser &

Goodwin 1986; Mork 1989; Lee et al. 1995; Ferderer 1996; Hooker 2002; Jiménez-

Rodríguez & Sanchez 2005). The existence of a negative relationship between oil price

increases and economic activity has become widely accepted since Hamilton’s

pioneering 1983 work. Later, other researchers extended Hamilton’s basic findings

using alternative data and estimation procedures.

Numerous researchers have suggested that oil price fluctuations have considerable

consequences for real economic activity. First, the transmission mechanisms by which

oil price impacts real economic activity include both supply and demand channels. The

supply-side effects are related to the fact that crude oil is a basic input of production,

and an increase (decrease) in oil price leads to a rise (fall) in production costs, which

induces firms to lower (raise) their output. In addition to oil’s impact on countries

through their terms of trade, it will also raise inflation and interest rates, with the

inflationary impact (and monetary response) of developing countries likely to be much

greater due to the larger weight of fuel in the consumer basket and/or monetary policy

credibility/inflation expectations not being as well entrenched as those in advanced

economies. With rising prices, workers will pressure employers for higher wages to

compensate for the increased cost of living. If there is a risk of price increases

translating into a spiral of rising producer costs and wages then central banks are likely

to intervene by tightening monetary policy through raising interest rates. The higher

158

interest rates will then act as a signal to market participants that monetary authorities

will not tolerate higher inflation (Rensburg 2012). As there is an inverse relationship

between interest rates and inflation, if there is more money in an economy, people tend

to spend more, thus (as a whole) driving up the cost of goods and services. If there is

less money in an economy, there is less to spend and low demand equals lower prices. If

interest rates are high, it is more expensive to borrow, hence less money in an economy.

In addition, oil price changes also entail demand-side effects on consumption and

investment. Consumption is affected indirectly through its positive relationship with

disposable income. The rise (fall) of oil price reduces (increases) consumer spending

power. Moreover, a rise (fall) in oil price is generally considered to have a negative

(positive) impact on investment by increasing (decreasing) firms’ costs (Wang 2010). In

this situation, oil exporting countries will gain in their real income as prices of goods

exports are expected to rise. Recent research by the IMF discussed two approaches for

oil supply forecasting using geological and economic/technological models, and found

that the amount of oil produced is influenced by a combination of (1) geological

depletion and (2) price levels. Their findings indicates that (1) oil supply in the future

will not rise nearly as rapidly as in the pre-2005 period and (2) oil prices are likely to

nearly double in real (inflation-adjusted) terms by 2020 (Benes et al. 2012). The income

effect will be strongest where exports represent a large share of GDP and the export

basket is less diversified. Oil exporting countries in the Middle East region could

observe GDP gains of around 6.6 per cent and 2.4 per cent in 2012 (Rensburg 2012).

Masood Ahmed, Director of the IMF’s Middle East and Central Asia Department, said:

Middle East oil exporters are benefiting from high oil prices, and we expect

GDP growth to strengthen and become more broad-based this year.

Nonetheless, fiscal vulnerabilities to falling oil prices have increased, and

structural challenges remain, such as the need to create jobs for growing

working-age populations and to further diversify the economies. (Ahmed

2012).

Many researchers have studied the relationship between oil prices and GDP. Most of

these studies have concentrated on the developed oil importers, with a lesser focus on

emerging markets or oil exporting countries. This chapter will focus on oil exporting

159

countries. The present chapter will examine the effects of an oil price shock on oil

exporting in Kuwait, the UAE and Saudi Arabia by a simultaneous assessment of the

dynamic relationship between real economic activity of GDP and oil price shocks using

the most recent time series data and an unrestricted VAR model.

The remainder of this paper is organised as follows. Section 6.3 presents the data and

empirical descriptive results. Section 6.4 presents the empirical framework including

the econometric methodology and an introduction to the data used. Section 6.5 reveals

the empirical results including the unit root test, variance decomposition and the

impulsive response. Finally, the conclusions of the analysis are summarised in Section

6.6.


The average yearly data for crude oil price came from US Department of Energy EIA

while the GDP data been extracted from IMF data. Yearly data of oil and GDP was

employed for the period between 1987 and 2011. Table 6.2 presents descriptive

statistics for each country’s GDP for the period between 1987 and 2011. As shown in

Table 6.2 the distribution properties of the return series appear to be non-normal as all

markets have positive skewness, indicating that the bulk of the values lie to the left of

the mean. The kurtosis in all markets except Saudi Arabia exceeds 3, indicating a

leptokurtic distribution. The final statistic presented in Table 6.2 is the calculated

Jarque-Bera statistic and corresponding p values used to test the null hypotheses that the

GDPs for emerging countries and oil are not-normally distributed. Table 6.2 also shows

that the volatility (measured by standard deviation) for the oil price and Kuwait’s GDP

(27.8, 17.3) are higher than all the other series, as oil increased around ten-fold during

the study period from minimum value of $12.8 to $108.2.

160

Table 6.2: Summary Statistics

KUWAIT OIL SAUDI ARABIA UAE

Mean 4.619760 36.75700 3.225240 6.089240

Median 3.663000 23.76000 3.158000 5.323000

Maximum 50.68800 108.1550 9.104000 23.56200

Minimum –41.00800 12.76000 –3.984000 –3.150000

Std. Dev. 17.26844 27.85492 3.346392 6.194715

Skewness 0.047996 1.290761 0.048407 0.951472

Kurtosis 5.251020 3.392975 2.340830 3.927829

Jarque-Bera 5.287816 7.102796 0.462373 4.668816

Probability 0.071083 0.028685 0.793592 0.096868

Observations 25 25 25 25

6.4 Methodology


Unit roots were tested for in each series. The null hypothesis of the existence of a unit

root (non-stationary) was tested against the alternative hypothesis of stationary variables

using the ADF statistic (Dickey & Fuller 1981). Automatic selection of lags was

employed based on the SIC; Table A.6 in the Appendix reports the results, which

showed that all series are stationary.

6.4.2 VAR

This study will consider the following VAR model of order or simply,

(6.1)

Where is a vector of endogenous variables is the

intercept vector of the matrix of autoregressive coefficient

for generalisation of a white noise

process.

161

This study used two-variable VAR for each country under study. The variables

considered for the model were real GDP and average oil price. Table A.7 in the

Appendix reports the results of the VAR modelling.

6.5 Results

6.5.1The Effect of Oil Price Shocks on GDP

6.5.1.1 Variance Decomposition

Variance decomposition analysis measures the percentage of the forecast error of a GDP

that is explained by another series or oil prices. It indicates the relative impact that one

series has upon another and oil prices within the VAR system. Variance decomposition

enables the assessment of the economic significance of this impact as a percentage of

the forecast error for a variable sum to one. The orthogonalisation procedure of the

VAR system decomposes the forecast error variance, the component that measures the

fraction of GDP of a particular country explained by innovations in each of the four

series.

Table 6.3 and Figure 6.3 provide the variance decomposition of the ten-year forecast

error of each series, accumulated by innovations in each of the four series for the study

period. Each row indicates the percentage of forecast error variance explained by the

series indicated in the first column, for instance at 5 period horizons for KUWAIT the

22 per cent of forecast error variance in Kuwait is explained by the oil price. The results

indicate that most markets and oil returns are strongly endogenous in the sense that the

percentage of the error variance accounted for by the Kuwaiti market is around 65 per

cent at time horizon 5, while the percentage of the foreign explanatory power, as

indicated by the foreign column, is significant, reaching up to 60 per cent at time

horizon 9. Within the GCC countries the UAE is the most endogenous, with 96 per cent

error variance explained by other series and oil; with 36 per cent of this explained by

other countries’ GDP and 60 per cent of forecast error variance in the UAE’s GDP

explained by oil. Saudi Arabia came in as the second most endogenous, with 94 per cent

error variance explained by other series, with 67 per cent explained by oil prices. This

means that 67 per cent of the forecast error variance in the Saudi GDP is explained by

oil prices. Thus, it can be concluded that oil prices play a major role in the forecasting

162

of error in the GCC countries’ GDP variance, with least variance for Kuwait; these

results match the earlier findings for the VAR estimation.

Table 6.3: Variance Decomposition for the Forecast Error of GCC GDP and Oil

KUWAIT


ARABIA UAE

1 13.93383 100.0000 0.000000 0.000000 0.000000

2 17.88071 65.23746 18.91011 5.068349 10.78408

3 21.33689 53.96953 13.29566 21.41963 11.31518

4 21.95285 52.91082 12.57455 20.97402 13.54060

5 27.85957 42.85528 22.06811 24.23051 10.84610

9 39.00827 30.04225 48.85443 14.18242 6.920902

10 39.86159 30.65487 47.95756 13.59164 7.795931

OIL

Period S.E. KUWAIT OIL SAUDI

ARABIA UAE

1 15.49514 10.32281 89.67719 0.000000 0.000000

2 16.85598 15.96418 80.02095 1.786184 2.228685

3 17.38697 16.70829 78.49037 1.828615 2.972720

4 22.53316 14.05746 82.62787 1.544555 1.770117

5 27.47817 16.79745 79.88204 1.424241 1.896267

9 47.30357 16.04120 80.70289 0.730116 2.525800

10 48.76801 18.69546 76.23651 0.773211 4.294814

SAUDI ARABIA


ARABIA UAE

1 2.532674 16.52817 1.498696 81.97314 0.000000

2 4.164047 59.83059 2.695441 31.45628 6.017697

3 4.819605 55.72372 8.177250 30.69821 5.400826

4 6.745609 32.30633 45.75159 18.62930 3.312782

5 8.234897 29.23175 53.16070 14.61211 2.995441

9 12.91148 24.82923 65.33204 6.610175 3.228559

10 13.44804 22.89399 67.17211 6.225491 3.708412

UAE


ARABIA UAE

1 6.718923 0.103021 33.97777 54.00537 11.91384

2 8.632691 11.15141 39.47124 41.63541 7.741949

3 10.14225 21.30937 34.14417 37.06426 7.482199

163

4 11.18800 17.52425 45.20188 31.11101 6.162860

5 12.69500 21.49384 46.87183 26.48384 5.150496

9 16.46263 22.81253 55.89546 17.55640 3.735606

10 17.78485 20.95188 60.31832 15.50456 3.225240

Variance Decomposition: KUWAIT OIL SAUDI ARABIA UAE

Figure 6.3: Variance Decomposition for the Forecast Error of GCC GDP and Oil

In general the results achieved in this study can help verify the dominant series in the

system that manipulates all other markets and links their interdependence to oil.

6.5.1.2 Impulse Response

The estimated impulse response of the VAR system enables an examination of how

each of the four variables responds to innovations from other variables in the system.

Table 6.4 and Figure 6.3 summarises the accumulated responses of all countries and oil

to a one standard deviation shock in oil price over the study period. In general, the

responses are significant and increase/decrease rapidly, indicating that series respond

efficiently to shocks generated from oil. However, it is notable that a shock originating

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

KUWAIT OIL SA UAE

Variance Decomposition of KUWAIT

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

KUWAIT OIL SA UAE

Variance Decomposition of OIL

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

KUWAIT OIL SA UAE

Variance Decomposition of SA

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10

KUWAIT OIL SA UAE

Variance Decomposition of UAE

164

in oil prices has a major and persistence impact on all GCC country GDPs, with Kuwait

having the greatest response. All series began responding to oil shocks from the first

time horizon, while Kuwait responded from the second time horizon. In addition, Saudi

Arabia and the UAE responded in a contradictory manner to the Kuwaiti GDP response;

the Kuwaiti GDP’s behaviour can be justified as Kuwait was the country mostly

severely affected by the political and military conflicts within the Gulf Region. Further,

is notable that almost all the series responded negatively to oil shocks.

Table 6.4: Accumulated Response of All Series to One Standard Deviation

Innovation Shock from Oil


1 0.000000 14.67359 0.310053 3.916491

2 –7.775561 11.20310 –0.299239 0.164639

3 –7.509337 8.053046 –1.495939 –2.224331

4 –7.244694 21.55326 2.853658 2.407879

5 3.275886 8.002909 –1.049121 –1.946452

6 –1.459010 13.08856 –2.509788 –3.771008

7 –11.98427 24.48947 2.813069 3.033275

8 3.428382 0.917844 –3.012775 –1.972975

9 –10.76169 23.08120 –0.104896 –3.094153

10 –6.445549 20.37609 3.440222 3.174858

Cholesky ordering: KUWAIT OIL SAUDI ARABIA UAE

165

Figure 6.4: Accumulated Response of All Series to One Standard Deviation

Innovation in Oil

Regardless of the different magnitude of the impulse response values, these results

reflect the significant impact of the increase in oil prices on GCC country GDPs. This is

quite normal since the GCC countries produce about 21 per cent of the world’s daily oil

production and possess about 43 per cent of the world’s oil reserves.

6.6 Summary and Conclusions

Oil price volatility plays an essential role in controlling and forecasting risks in various

economical and financial operations. In this chapter, oil volatility is represented in terms

of variance decomposition and impulse response. This chapter examined the

transmission of oil price shocks in three GCC country GDPs during the period from

1987 to 2011using a VAR model. Overall, the results indicate that the model performs

well statistically. The VAR model suggests that there is a strong significant relationship

between GCC country GDPs and oil prices, pointing to these countries extrapolative

power on oil prices. This would also show that political and economic stability has a

direct impact on stability in oil prices. Oil prices do affect the GCC countries’ GDPs,

-16

-12

-8

-4

0

4

1 2 3 4 5 6 7 8 9 10


0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10


-4

-2

0

2

4

1 2 3 4 5 6 7 8 9 10


-4

-2

0

2

4

1 2 3 4 5 6 7 8 9 10


Accumulated Response to Cholesky One S.D. Innovations

166

with the largest effect on Kuwait’s GDP, largely due to the Gulf conflicts over the last

three decades. A high priority for decision-makers in GCC countries is to secure diverse

income resources and to attempt to increase the contribution of the non-oil sector to

GDP. In Saudi Arabia, for instance, the contribution of oil to GDP amounts to about 44

per cent; in Kuwait, it is 35 per cent and in the UAE 32 per cent (2007 data). This leads

to risk as a result of linking these countries’ major economies sectors to oil prices,

which in turn leads to risks in the oil market. This cycle acts as a negative influence on

the performance of these countries’ financial and economic bodies; particularly as they

prepare for a single currency and other integration mechanisms in the near future.

167

Chapter Seven: Summary and Conclusions

7.1 Introduction

The objective of this study was the development of a procedure to model volatility

spillover based on the MGARCH and VAR approaches. These procedures were used to

test for the volatility and volatility spillovers between oil and selected GCC stock

market returns. In addition, they captured the dynamic conditional covariance and

dynamic conditional correlation between the equity markets of selected developing

countries in the GCC and the international prices of oil, measured by the Europe Brent

Spot Price. The second and third related aims of this thesis were to investigate the

relationship between oil prices and stock market returns by applying the VAR model,

and to examine the relationship between oil prices volatility and GCC country GDPs.

The raison d’être for this was the argument that analysing oil volatility spillover patterns

would reflect on the GDPs and stock markets of countries in this region.

Oil is an exhaustible resource; thus, a good forecasting structure for oil shocks is vital to

all parties involved in the energy business, such as governments, oil companies,

financial institutions, public policy planners and makers, as well as oil exporting and

importing countries. Such study will help to ensure the future stability and security of

the crude oil market. This research study has discussed the Hubbert model and the

multi-cyclic Hubbert model forecasting techniques for world oil production. After

evaluating the production trends of the major GCC oil producing countries, these

models are among the most renowned statistical models for the prediction of oil and gas

production. Each country has its own prediction table and the world model can be

discussed by combining the data from all countries.

168

As oil is an exhaustible resource, this makes it obligatory for the GCC politicians to

assess the future economic concentration and diversification of their economies. This

study found that the GCC countries’ GDPs were consistently heavily dependent on just

one or two economic sectors. Essentially, the lower the concentration ratio and the

higher the diversification quotient, the more diversified a nation’s economy (Shediac,

Abouchakra & Najjar 2008).

While some important preceding research has been undertaken on volatility in the GCC,

it has concentrated on single representative indices and was developed along the lines of

more complex temporal volatility structures on the same index. This study has provided

a new direction to the analysis of volatility and volatility spillovers in the GCC

countries by exploring the effect of oil price volatility on selected GCC emerging

markets and GCC country GDPs.

The remainder of this chapter is structured as follows. Section 7.2 presents an overview

of the chapters included in this thesis. This is followed by a section providing a

discussion of the results and some possible implications. Finally, there are two related

sections dealing with the limitations of this study and some recommendations for

further research.

7.2 Overview of the Thesis

Chapter 1 highlighted the importance of oil price volatility and described the motivation

for this research study, which was driven by an examination of stock market volatility

through market indices. This required the application of MGARCH rather than single

time series data. This study examined the volatility and shock transmission mechanisms

between the equity markets of the GCC countries and crude oil prices on one hand and

between crude oil prices and the GDPs of these countries on the other. The MGARCH

and VAR models were used to identify the source and magnitude of spillovers. The gap

in the literature regarding stock market volatility in emerging markets was highlighted,

and, in particular, the relatively small volume of literature focused on the GCC

countries. The chapter also described the main objectives of the thesis and the

significance of the research.

169

Chapter 2 pointed out the contradictory views regarding the effect of international oil

prices on the stock market, with reference to the different empirical analysis approaches

of cross-sectional and time series data on a macroeconomic level. The literature on oil

and stock market volatility has produced a large amount of theoretical and empirical

research, especially since the development of the ARCH/GARCH models of Engle

(1982) and Bollerslev (1986). This theoretical research has not been definite on how oil

and stock market volatility should be modelled and, as such, the development of

empirical models to explain and predict oil and stock market volatility remains an active

area of research. Chapter 2 began with an introduction to the importance of oil as an

exhaustible resource, the importance of peak oil and some empirical work on world oil

peak production, and then outlined the history of the stock markets in the GCC

countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. The importance

of volatility for forecasting and predictions that can be used in investment, options

pricing, hedging, market making, portfolio selection and many other financial and

economic activities was also discussed.

This chapter then outlined the concept and measures of volatility. One of the main

reasons that make stock market volatility an important topic of interest is its relationship

to the EMH. This section of the chapter introduced the concept of market efficiency and

showed how evidence of volatility clustering is in contradiction with the EMH. Further,

for the purposes of developing a model for stock market volatility, the factors that cause

oil and stock market volatility are an important consideration. Hence, the literature on

the causes of oil and stock market volatility was discussed.

Chapter 2 also described theoretical analysis using the ARCH/GARCH and MGARCH

conditional heteroscedasticity models, and demonstrated that the MGARCH classes of

models are the most appropriate choice for modelling volatility in oil and the GCC

stock markets. The empirical evidence of volatility with the ARCH/GARCH and

MGARCH models was discussed within the context of this region. In particular, the

literature was grouped into the developed markets and the emerging markets of the

GCC countries. This chapter highlighted the GCC countries’ economic growth, the

importance of economic diversification and approaches to diversify the economies in

this region. It also discussed the resource curse and Dutch disease, and the ways to

avoid these diseases, particularly in the GCC countries. In summary, the literature

170

review in this chapter indicated that there are contradictory findings on the relationship

between oil and stock markets on one side, and oil and economic growth on the other.

This study, in contrast, has provided further evidence on this relationship by introducing

the use of sophisticated econometric methods with time series data and a comparative

analysis of oil and stock market volatility at a regional level, and oil and economic

growth at both regional and global levels.

Chapter 3 provided an overview of the history and economic position of selected GCC

countries; Saudi Arabia, Kuwait and the UAE. The focus of this chapter was to describe

the key characteristics of these countries and link these characteristics to standard of

living indicators. This chapter highlighted the importance of economic diversification,

and described the position of the economic diversification of these countries compared

to other countries. Diversification was measured by determining the concentration ratio

or the diversification quotient. By comparing the international experience of the

Canadian and Norwegian models of economic diversification with those of the GCC

countries, it was shown that Dubai Emirate’s diversification led that of all the GCC

countries and emirates, while Abu Dhabi Emirate ranked second last. Overall, the UAE

led all GCC countries, followed by Saudi Arabia and finally Kuwait. The literature

review in Chapter 3 showed that non-hydrocarbon growth and SWFs were the major

mechanisms used by these governments to promote economic diversification.

Diversification can be achieved in two main ways: by investing offshore through SWFs,

which diversifies income, or by investing domestically (in this case, in non-hydrocarbon

growth).

As world markets move toward globalisation, there is increasing evidence of the

interlinkage between oil price volatility and GDP. The relevant empirical findings have

identified a linear negative relationship between oil prices and real activity in oil

importing countries, which has been supported by the work of Darby (1982), Hamilton

(1983), Burbidge and Harrison (1984) and Gisser and Goodwin (1986). Therefore, the

performance of these economies are affected by oil price volatility. The next chapter

examined the methodology for modelling stock market volatility and volatility

spillovers in the GCC counties.

Chapter 4 was pivotal to this thesis, as it described part of the research methods and

models that were employed in this thesis. This chapter began with the provision of a

171

general structure for modelling multivariate volatility and volatility spillovers: the

MGARCH-BEKK model of Engle and Kroner (1995), explained in Chapter 2. Using

this general and then more specific methodology, and imposing restrictions on a general

model, finite crude oil volatility and volatility spillover models were proposed and

described as further potential specifications of volatility dynamics in the GCC economy

and GCC stock markets returns.

Overall, results of chapter 4 indicated that the model performs well statistically. It is

obvious that the volatility for the emerging markets of the GCC countries are relatively

within the same level while UAE and the Saudi Market are less volatile than Kuwait

and Oil market over the study period. In addition as observed in figure 4.3 the daily

market returns indicate volatility clustering and leverage effects since the correlations

between the regional markets of (Kuwait, UAE & Saudi Arabia) and Oil increased

significantly during the GFC.

The coefficients of the lagged own innovations (the ARCH term) were significant in all

markets and highest in the UAE market. It was clear in this study that the Saudi Arabian

and oil markets spilled over innovations to all markets. Additionally, an important

finding was that oil received negative innovations from Saudi Arabia, supporting the

research findings of Malik and Hammoudeh (2007). Further, it was found that oil also

received negative innovations from the Kuwaiti market. The own volatility persistence

(the GARCH term) implied that all markets share the same low risk-return trade-off in

terms of lagged own variance persistence. Notable here is that the finding that oil

received volatility persistence from the Saudi Arabian market, a result that also

supported the previous findings of Malik and Hammoudeh (2007), while Saudi Arabia

showed no volatility persistence from any of the markets studied, apart from a slight

degree of persistence from oil. These results imply that the emerging markets in the

GCC countries primarily derive their volatility persistence from within their domestic

markets. In other words, the emerging markets are susceptible to conditions within the

GCC region; therefore, international investors could seek increased diversification in

the GCC markets and exploit the opportunities for high returns due to the higher risk-

return trade-off.

172

In Chapter 5 the VAR model was used to capture the effect of oil price shocks on stock

markets. The chapter began the VAR estimation by determining the appropriate lag

structure for the system, followed by an estimation of the variance decomposition and

finally, the analysis of the impulse response. In order to examine how the rise in oil

prices affected the GCC country stock markets and the dynamic interrelation between

them, the study period was divided into three sub-periods and a VAR system was

estimated for each period. The first period (Normal) was from 21 September 2005 to 6

October 2006. The second period (Rise) was from 9 October 2006 to 13 October 2008

and included the remarkable rise in oil prices, since oil tripled during the study period

from a minimum value of $49.95 per barrel to $143.95 per barrel. The third period

(GFC, Fall) was between 14 October 2008 and 11 February 2010, and included the GFC.

The empirical outcomes of Chapter 5 suggested the following: (1) for the first period,

oil returns were able to predict the Saudi Arabian market, but could not be predicted by

any of the GCC stock markets. However, after the oil price rose sharply during the

second period, oil was able to predict all the GCC stock markets, and could be predicted

by the Kuwaiti and UAE markets. For the third period, oil could both predict and be

predicted by the Kuwaiti and UAE markets. (2) Variance decomposition indicated that

all variables in the system were exogenous, while in the second period, the results

appeared more endogenous, since the forecast error rose to 35 per cent for the UAE.

This could be explained by other stock markets and oil. For the Saudi Arabian market

the forecast error reached 19 per cent during this period. For the third period the results

appeared more likely to be endogenous. (3) The impulse response functions indicated

that for the first period the responses of the GCC markets to shocks in oil returns were

generally small, while during the second and third periods these markets’ responses

were large, particularly for the Kuwaiti, Saudi and oil markets. (4) The response of

stock returns for all markets to shocks generated by oil was large and had memory

during the second and third periods.

From a political point of view, these results suggest that there is a significant

relationship between GCC markets, which highlights these countries’ extrapolative

power for oil prices. This would also indicate that political and economic stability has a

direct impact on stability in oil prices. Oil prices do affect the GCC markets, and the

GCC markets also affect oil prices. A high priority for decision-makers in the GCC

173

countries must be to secure diverse income resources and to attempt to increase the

contribution of the non-oil sector to GDP.

The aim of Chapter 6 was to investigate the impact of oil price shocks on real economic

activity in the economies of three oil exporting GCC countries: Kuwait, the UAE and

Saudi Arabia between 1987 and 2011. A VAR model was used to investigate the long-

run relationships between real economic activity and oil prices in the GCC region.

Chapters 6 highlighted the importance of oil as a finite resource, since the GCC

countries will reach their oil production peaks between 2027 and 2033. This means that

they must seek to minimise their economic concentration and maximise their economic

diversification to make their economies less vulnerable to external events, such as

changes in the price of the dominant commodity, oil.

The relationship between real economic variables and financial asset values has long

been a topic of economic research. Since the US-triggered financial crisis in 2008,

stemming from the Lehman Brothers filing for bankruptcy, the sharp fluctuation of oil

prices and volatile swings in the major stock markets have caused great concern

regarding economic growth in both the developed and developing countries. First, the

interrelation between oil price changes and real economic activity is a significant issue

that has been studied for most industrialised countries (Hamilton 1983; Gisser &

Goodwin 1986; Mork 1989; Lee et al. 1995; Ferderer 1996; Hooker 2002; Hamilton

2003; Jiménez-Rodríguez & Sanchez 2005). The existence of a negative relationship

between oil price increases and economic activity has become widely accepted since

Hamilton’s pioneering work in 1983. Later, other researchers extended Hamilton’s basic

findings using alternative data and estimation procedures. Overall, the results of Chapter

6 indicated that the VAR model performed well statistically. This model suggested that

there is a strong and significant relationship between GCC country GDPs and oil prices.

Oil prices affect the GCC countries’ GDPs, with the strongest effect of oil being on

Kuwait’s GDP, as this country was more vulnerable during the Gulf conflicts of the last

three decades. A highly priority for decision-makers in the GCC countries must be to

secure diverse income resources and to increase their contribution to GDP. For instance,

the oil contribution to Saudi Arabia’s GDP amounts to about 44 per cent, while oil

contributes 35 per cent of Kuwait’s GDP and 32 per cent of that of the UAE according

to the Arab Monetary Fund. This may lead to risk as a result of linking these countries’

174

major economic sectors to oil prices, which in turn leads to risks in the oil market, a

cycle which may act negatively on the performance of their financial and economic

bodies, particularly as these countries prepare for a single currency and other integration

mechanisms in the near future.

7.3 Implications of the Results of this Study

The outcomes of this study have significant implications for volatility and VAR

modelling in general, and for modelling volatility in the GCC in particular. These

implications can be summarised as follows:

1. Modelling the volatility and VAR analysis of return on the total level using one

index is different from that using many stock indices, in that each has diverse

volatility behaviour in general, and different temporal volatility. This may have

implications for predicting the volatility of an equity market using indices alone.

2. Modelling volatility and VAR analysis using series data of total shocks is

different from using series data operating in similar industries in that each sector

has a different volatility structure in general and different temporal volatility in

particular.

3. In general, the oil return has a predictable component in the GCC stock markets,

which is consistent with the findings of Maghyereh and Al-Kandari (2007),

Mork et al. (1994), and Hamilton (2000) on the relationship between oil and

stock markets. However, Rao (2008) found that the emerging markets in the

GCC gain more of their volatility persistence from the domestic market.

4. The volatility of the GCC emerging markets seems to exhibit contagious effects

from the GFC, which began in the USA/UK, and appears to have been

transmitted to the GCC countries simultaneously, as shown by the spikes in all

three stock markets analysed here, as well as the oil price. Further, the leverage

effect is clearly operating here, as shown by the increase in the conditional

variance across the GCC countries and the international oil market.

5. The findings of this study of the relationship between oil and stock markets were

found to be consistent with previous empirical studies by Gisser and Goodwin

175

(1986) and Hickman et al. (1987), which confirmed an inverse relationship

between oil prices and aggregate economic activity.

7.4 Limitations of the Study

This research, like most other research studies, has its limitations. There are some

limitations in this study arising from the inconsistent views on the relationship between

stock markets and oil price volatility, on one hand, and between oil and economic

growth in another. For oil and stock markets the theory is ambiguous about whether

stock markets are affected by oil price volatility. In contrast, the theory does not provide

a distinctive model to guide the empirical research for examining this relationship. In

this respect, researchers have applied different econometric methodologies and

techniques to establish the possible links between stock market returns and oil price

volatility. The results presented in this thesis were based on MGARCH and VAR tests

indicated that the relationship between them could be unidirectional, bidirectional or

that there could be no relationship at all.

Other limitations are determined by time constraints. An example of time limitation is

the fact that the current study sample was selected during a period where the price of oil

had tripled while the financial stock index dropped to 1/3 of its previous value due to

the GFC. The period selected for this study was convenient for leverage effect testing as

the degree of expectations about future volatility on bad news is not simulated for the

good news counterpart effects. However, in order to draw further generalisations this

research must be conducted under constant governing conditions where volatility on the

GCC stock markets is more stable, and modelled for a period before or after the sample

period used in this research where a price limit was enforced on all listed stocks.

The third limitation is from the shortage of GCC stock market data used in this study.

After assuming that empirical analysis would be undertaken from the available data for

the GCC countries’ stock markets and economic growth as is usual in econometric

modelling, it proved challenging to obtain quarterly and/or long term series annual data

on the size and activity of the GCC stock markets, along with the main factors of

economic growth. The absence of data limits the ability to apply the sophisticated

176

analyses of VAR methods and other economic model tests on the links between stock

markets, oil and economic growth for the GCC countries. However, given these

limitations this study has used sophisticated econometric techniques on regular series

datasets for the GCC stock markets, oil and the GDPs of the GCC economies.

7.5 Areas for Future Research

This thesis applied pooled series data to study the similarities, differences and effects in

the volatility and volatility spillovers between oil and selected GCC stock markets using

Saudi Arabia, UAE and Kuwait as a case study. The analysis has produced some

interesting results and an opportunity for future research is to extend the research to

other emerging markets within the GCC region or other Arab countries. The

encouragements for further research on other emerging markets come from the results

and the limitations of those studies that presently exist. Through undertaking this type

of research, generalisations can be made regarding the volatility and volatility spillover

structure of stocks when studied in the panel and series contexts.

The originality of this thesis, as mentioned before, is the modelling of volatility with

MGARCH and VAR model structure in a series data context. The direction and

possibilities for future research in that area are various. For example, the proposed

models can be replaced by applying the SUR systems and Wald tests with Granger

causality models pioneered by Kónya (2006). The importance of such models is to

study the causal relationship between oil, GDP and stock markets. A further area of

research could be to extend the proposed MGARCH models to analyse the long-run

relationship between the world price of crude oil, GDP and stock markets by applying a

co-integrated VECM with additional repressors as performed by Miller and Ratti (2009).

An additional significant area of future research from the perspective of the GCC

countries is to consider the causal relationships between stock market development,

privatisation and investment on the one hand, and between stock market development

and oil revenues on the other. This may suggest some initial causal links between GCC

stock markets and real economic activities. Stock market development could aid

privatisation and enable the attainment of more debt financing. This research considered

177

the effect of volatility and volatility spillover among three selected GCC countries and

oil price volatility as control variables for the interconnection between stock market

development and economic growth. Another proposed area of research could be the

analysis of the GCC countries comparable with the G-20 economies by evaluating the

different levels of economic growth and oil price volatility.

This study represents a pioneer attempt to:

1. Test empirically the effect of GCC stock market volatility and volatility

spillover through the analysis of conditional variance structures associated with

returns on the different stock market indices. In addition, this research focused

on the relationship between oil prices and GCC country GDPs.

2. Examine GCC economic growth by analysis of the VAR model between oil

prices and GDPs for the GCC countries selected in this study. The economic

theory of exhaustible resources (i.e., oil), can be traced back to 1914 (Chermak

2000). It is expected that oil production will decline by about 2.2 mbd after 2010;

according to Olivier Rech (Energy Advisor) petroleum production scenarios for

the International Energy Agency will face stronger tensions as of 2013, and an

expected overall decline of oil production "somewhere between 2015 and 2020”

(Oil Drum 2013), in which case OPEC will not have the ability to offset

cumulative non-OPEC declines and world oil production. Undoubtedly, the

GCC countries are very well aware that one day oil will run out, and all have

made efforts to diversify their economies in order to reduce their dependence on

oil.

178

Appendices

Table A.1: Time Series Unit Root Test (1st difference of raw data)

Null Hypothesis: Unit root (individual unit root process)

Series: KUWAIT, OIL, SAUDI ARABIA, UAE

Date: 09/07/11 Time: 08:19

Sample: 1 1,148

Exogenous variables: individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Total (balanced) observations: 4,584

Cross-sections included: 4

Method Statistic Probability**

ADF—Fisher Chi square 595.162 0.0000

ADF–Choi Z stat –23.7992 0.0000

** Probabilities for Fisher tests are computed using an asymptotic Chi

square distribution. All other tests assume asymptotic normality.

Intermediate ADF test results

Series Probability Lag Max Lag Obs

KUWAIT 0.0000 0 22 1,146

OIL 0.0000 0 22 1,146

SAUDI ARABIA 0.0000 0 22 1,146

UAE 0.0000 0 22 1,146

179

Table A.2: Unit Root Test for Oil and Market Returns Series

Null Hypothesis: Unit root (individual unit root process)


Date: 10/24/11 Time: 10:15

Sample: 1 1,147

Exogenous variables: Individual effects


Automatic lag length selection based on SIC: 0

Total (balanced) observations: 4,584

Cross-sections included: 4

Method Statistic Probability**

ADF–Fisher Chi square 595.162 0.0000

ADF–Choi Z stat –23.7992 0.0000

** Probabilities for Fisher tests are computed using an asymptotic Chi

square distribution. All other tests assume asymptotic normality.

Intermediate ADF test results

Series Probability Lag Max Lag Obs

KUWAIT 0.0000 0 22 1,146

OIL 0.0000 0 22 1,146

SAUDI ARABIA 0.0000 0 22 1,146

UAE 0.0000 0 22 1,146

180

Table A.3: VAR Estimates for Oil and Market Returns 21/09/2005–06/10/2006

Normal–1st Period

VAR Estimates

Date: 10/24/11 Time: 10:23

Sample (adjusted): 5 273

Included observations: 269 after adjustments

Standard errors in ( ) and t-statistics in [ ]

KUWAIT OIL SAUDI

ARABIA UAE

KUWAIT(–1) –0.004190 –0.366426 –0.080747 0.253684

(0.06493) (0.10844) (0.15681) (0.17119)

[–0.06453] [–3.37892] [–0.51494] [1.48191]

KUWAIT(–2) –0.006333 0.163066 –0.023901 –0.136820

(0.06636) (0.11082) (0.16025) (0.17494)

[–0.09544] [1.47141] [–0.14915] [–0.78209]

KUWAIT(–4) 0.021187 –0.018403 –0.005723 –0.266719

(0.06602) (0.11026) (0.15943) (0.17405)

[0.32093] [–0.16691] [–0.03589] [–1.53241]

OIL(–1) –0.063302 –0.002872 0.036100 –0.029703

(0.03846) (0.06424) (0.09289) (0.10141)

[–1.64574] [–0.04470] [0.38863] [–0.29291]

OIL(–3) 0.009820 –0.011076 0.001485 –0.081507

(0.03893) (0.06502) (0.09402) (0.10264)

[0.25224] [–0.17035] [0.01580] [–0.79410]

OIL(–4) 0.053703 –0.031852 0.179679 0.171792

(0.03802) (0.06349) (0.09181) (0.10023)

[1.41261] [–0.50166] [1.95709] [1.71400]

SAUDI ARABIA(–1) 0.036221 –0.038308 0.036626 0.114009

(0.02644) (0.04417) (0.06386) (0.06972)

[1.36968] [–0.86735] [0.57352] [1.63526]

SAUDI ARABIA(–3) 0.020126 –0.001246 0.017673 –0.026359

(0.02659) (0.04442) (0.06422) (0.07011)

[0.75678] [–0.02806] [0.27517] [–0.37595]

SAUDI ARABIA(–4) 0.043548 0.008623 0.067771 0.019902

(0.02657) (0.04437) (0.06416) (0.07004)

[1.63922] [0.19434] [1.05633] [0.28415]

UAE(–1) –0.013680 0.031419 0.021717 0.002501

(0.02425) (0.04050) (0.05856) (0.06393)

[–0.56417] [0.77581] [0.37086] [0.03912]

181

UAE(–2) 0.019146 –0.034131 0.036777 0.033897

(0.02418) (0.04038) (0.05840) (0.06375)

[0.79178] [–0.84515] [0.62980] [0.53171]

UAE(–3) 0.009403 0.010706 0.010733 0.068787

(0.02417) (0.04036) (0.05836) (0.06371)

[0.38910] [0.26527] [0.18390] [1.07967]

* The VAR system has been estimated with 4 lags according to the AIC with oil as an

essential reference.

182

TableA.4: VAR Estimates for Oil and Market Returns 09/10/2006–13/10/ 2008

Rise–2nd Period

VAR Estimates

Date: 10/24/11 Time: 10:38




KUWAIT OIL SAUDI

ARABIA UAE

KUWAIT(–1) 0.038048 0.037004 0.082066 0.115364

(0.05171) (0.08395) (0.08070) (0.07717)

[0.73574] [0.44076] [1.01690] [1.49496]

KUWAIT(–2) –0.021578 0.003465 –0.017163 0.074483

(0.05140) (0.08344) (0.08020) (0.07669)

[–0.41983] [0.04152] [–0.21399] [0.97118]

KUWAIT(–3) –0.043575 0.070205 –0.015155 0.052542

(0.05072) (0.08235) (0.07916) (0.07569)

[–0.85906] [0.85254] [–0.19145] [0.69416]

OIL(–1) 0.000674 0.073895 0.003244 –0.012968

(0.02727) (0.04428) (0.04256) (0.04070)

[0.02470] [1.66897] [0.07622] [–0.31865]

OIL(–3) –0.013773 0.006177 0.020469 –0.004316

(0.02803) (0.04551) (0.04374) (0.04183)

[–0.49136] [0.13574] [0.46794] [–0.10319]

OIL(–4) –0.000347 0.027727 0.084446 0.128645

(0.02782) (0.04516) (0.04342) (0.04151)

[–0.01246] [0.61390] [1.94509] [3.09883]

SAUDI ARABIA(–2) –0.077402 –0.015647 –0.003831 –0.084142

(0.03339) (0.05421) (0.05211) (0.04983)

[–2.31786] [–0.28863] [–0.07352] [–1.68856]

SAUDI ARABIA(–3) –0.015988 –0.073113 0.062121 0.023396

(0.03354) (0.05445) (0.05234) (0.05005)

[–0.47670] [–1.34275] [1.18686] [0.46746]

SAUDI ARABIA(–4) 0.035898 –0.083859 0.087279 0.062481

(0.03360) (0.05455) (0.05243) (0.05014)

[1.06843] [–1.53743] [1.66462] [1.24622]

UAE(–2) 0.060400 0.096499 0.011453 0.006734

(0.03691) (0.05992) (0.05760) (0.05508)

[1.63637] [1.61041] [0.19883] [0.12227]

183

UAE(–3) –0.015440 –0.000992 0.055484 0.094971

(0.03697) (0.06002) (0.05769) (0.05516)

[–0.41765] [–0.01652] [0.96174] [1.72159]

UAE(–4) –0.041313 0.113892 –0.068198 –0.087785

(0.03705) (0.06015) (0.05782) (0.05529)

[–1.11498] [1.89340] [–1.17945] [–1.58772]

*The VAR system has been estimated with 4 lags according to the AIC with oil as an


184

TableA.5: VAR Estimates for Oil and Market Returns 14/10/2008–11/02/ 2010

Fall–3rd Period

VAR Estimates

Date: 10/24/11 Time: 10:46




KUWAIT OIL SAUDI

ARABIA UAE

KUWAIT(–1) 0.045959 –0.047976 –0.013285 0.043117

(0.05836) (0.08753) (0.04967) (0.07023)

[0.78745] [–0.54808] [–0.26747] [0.61398]

KUWAIT(–3) 0.070465 –0.008155 –0.056804 0.020354

(0.05797) (0.08695) (0.04934) (0.06975)

[1.21551] [–0.09379] [–1.15138] [0.29179]

KUWAIT(–4) –0.094481 0.107092 –0.051980 –0.029599

(0.05827) (0.08739) (0.04959) (0.07011)

[–1.62149] [1.22545] [–1.04826] [–0.42219]

OIL(–2) 0.038795 0.004186 0.070872 0.020751

(0.03858) (0.05787) (0.03284) (0.04643)

[1.00545] [0.07233] [2.15834] [0.44697]

OIL(–3) 0.036749 –0.010126 0.041133 0.000384

(0.03872) (0.05808) (0.03295) (0.04659)

[0.94904] [–0.17436] [1.24821] [0.00824]

OIL(–4) –0.005270 0.000123 0.025121 0.082353

(0.03834) (0.05750) (0.03263) (0.04613)

[–0.13745] [0.00214] [0.76994] [1.78519]

SAUDI ARABIA(–1) –0.058406 0.074596 0.004779 0.107508

(0.07254) (0.10880) (0.06173) (0.08728)

[–0.80514] [0.68564] [0.07741] [1.23170]

SAUDI ARABIA(–2) –0.114403 –0.181338 0.004356 –0.042570

(0.07156) (0.10732) (0.06090) (0.08610)

[–1.59879] [–1.68971] [0.07153] [–0.49443]

SAUDI ARABIA(–4) 0.029594 –0.180645 0.053519 –0.042212

(0.07057) (0.10584) (0.06006) (0.08491)

[0.41936] [–1.70679] [0.89116] [–0.49714]

UAE(–2) 0.078958 –0.003330 0.037232 0.180604

(0.05219) (0.07827) (0.04441) (0.06280)

[1.51291] [–0.04254] [0.83828] [2.87606]

185

UAE(–3) –0.028700 –0.053259 0.010644 –0.137512

(0.05214) (0.07820) (0.04437) (0.06274)

[–0.55046] [–0.68108] [0.23989] [–2.19195]

UAE(–4) –0.025367 0.063037 –0.006288 –0.062052

(0.05070) (0.07604) (0.04315) (0.06100)

[–0.50036] [0.82903] [–0.14575] [–1.01722]

*The VAR system has been estimated with 4 lags according to the AIC with oil as an


186

Table A.6: Time Series Unit Root Test

Group unit root test: Summary


Date: 11/26/12 Time: 10:42

Sample: 1 26

Exogenous variables: Individual effects


Automatic lag length selection based on AIC: 0 to 3

Newey-West automatic bandwidth selection and Bartlett kernel

Method Statistic Probability** Cross-

sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* –9.46366 0.0000 4 86

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W stat –8.82159 0.0000 4 86

ADF–Fisher Chi square 72.4744 0.0000 4 86

PP–Fisher Chi square 171.715 0.0000 4 92

**Probabilities for Fisher tests are computed using an asymptotic Chi square

distribution. All other tests assume asymptotic normality.

187

Table A.7: VAR Estimates for Oil and GDP 1987–2011

VAR Estimates

Date: 11/26/12 Time: 10:49




KUWAIT OIL SAUDI

ARABIA UAE

KUWAIT(–1) –0.530763 –0.064501 –0.118894 –0.100428

(0.36090) (0.40134) (0.06560) (0.17403)

[–1.47065] [–0.16071] [–1.81242] [–0.57707]

KUWAIT(–3) 0.072285 0.067549 –0.079812 –0.030265

(0.19332) (0.21498) (0.03514) (0.09322)

[0.37391] [0.31420] [–2.27133] [–0.32467]

OIL(–3) –0.456758 0.924230 0.199236 –0.017390

(0.63878) (0.71035) (0.11611) (0.30802)

[–0.71505] [1.30109] [1.71598] [–0.05646]

SAUDI ARABIA(–1) 3.696466 –1.354015 –0.755280 –0.543699

(1.57087) (1.74688) (0.28553) (0.75748)

[2.35314] [–0.77510] [–2.64521] [–0.71778]

SAUDI ARABIA(–3) 0.859085 0.735049 –0.275547 –0.632343

(1.89886) (2.11163) (0.34515) (0.91564)

[0.45242] [0.34810] [–0.79835] [–0.69061]

UAE(–1) –2.531924 1.085057 0.440459 –0.269690

(1.04822) (1.16567) (0.19053) (0.50545)

[–2.41546] [0.93084] [2.31178] [–0.53356]

UAE(–3) –0.982399 0.075690 0.316718 0.346315

(1.39780) (1.55442) (0.25407) (0.67402)

[–0.70282] [0.04869] [1.24658] [0.51380]

*As a consequence of lack of data the VAR system has been estimated with 3 lags

instead of 4 lags, according to the AIC with oil as an essential reference.

188

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