University of Economics in Prague

Faculty of Economics

Study program: Economics

WHAT CAUSED THE HIKES AND DIPS OF

GOVERNMENT BOND YIELDS DURING

THE SOVEREIGN BOND CRISIS?

Bachelor’s thesis

Author: Tomáš Václavíček

Thesis supervisor: doc. Ing. Zdeněk Chytil, CSc.

Rok: 2014

I declare on my honor that I have written my bachelor’s thesis individually

and unaided using the literature referenced in the bibliography.

Tomáš Václavíček

In Prague, on May 23rd

, 2014

I would like to thank my thesis supervisor doc. Ing. Zdeněk Chytil, CSc.

for guidance and advice about the topic.

Abstract

This study examines the determinants of government bond spreads vis-à-vis Germany

for eleven EMU member countries in the period 2000Q1 to 2013Q3 with a special focus

on the European Debt Crisis. The aim of the thesis is to test whether selected financial,

fiscal and macroeconomic variables have an impact on government bond spreads. A

novel contribution is testing whether there has been a significant change of government

bond spread determinants following the ECB interventions in summer 2012. Variables

reflecting the sustainability of public finance, liquidity of government bonds, risk

aversion and competitiveness of a particular country were found to be significant

determinants of government bond spreads, unlike banking sector indicators.

Government bond spreads thus increase in response to rising debts and deficits and the

loss of international competitiveness. No significant change in the composition of

government bond yield determinants as a whole was found for the period after the ECB

interventions, despite changes in several variables. Results of the thesis suggest that it is

important to follow a sound fiscal policy and to prevent a deterioration of a country’s

international competitiveness in order to keep government bond spread of a particular

country low.

Key words: debt crisis, government bonds, rating agencies, banking crisis,

budget deficit, sovereign debt

JEL Classification: E43, E62, G12, H63

Abstrakt

Tato studie zkoumá faktory, ovlivňující výši spreadů úrokových měr státních dluhopisů

oproti Německu na příkladu jedenácti zemí Eurozóny v rozmezí čtvrtletí 2000Q1 až

2013Q3 s důrazem na období evropské dluhové krize. Cílem práce je otestovat, zda

vybrané finanční, fiskální a makroekonomické proměnné mají vliv na spreadů státních

dluhopisů. Jedním z přínosů této práce je zkoumání potenciální změny faktorů

ovlivňujících spready po intervencích ECB v létě 2012. Proměnné, které reflektují

udržitelnost veřejných financí, likviditu státních dluhopisů, averzi investorů k riziku a

konkurenceschopnost konkrétní země, se ukázaly jako významné determinanty spreadů

státních dluhopisů, na rozdíl od indikátorů bankovního sektoru. Nepodařilo se prokázat

významnou změnu vztahů mezi proměnnými v období po intervencích ECB jako celek,

ale působení některých proměnných na státní dluhopisy se změnilo. Výsledky této

studie naznačují, že dodržování solidní fiskální politiky a prevence zhoršování

konkurenceschopnosti ekonomiky jsou důležité kroky k udržení spreadů úrokových měr

státních dluhopisů na nízkých hodnotách.

Klíčová slova: dluhová krize, státní dluhopisy, ratingové agentury, bankovní

krize, rozpočtové schodky, zadlužení států

JEL klasifikace: E43, E62, G12, H63

Table of contents

Introduction ....................................................................................................................... 7

1. Theoretical background ......................................................................................... 10

1.1. Credit risk of government bonds .................................................................. 12

1.1.1. Government deficits and debt dynamics ................................................. 12

1.1.2. International competitiveness and government bond yields ................... 15

1.1.3. Inflation and bond prices ......................................................................... 18

1.2. Other sources of risk .................................................................................... 19

1.2.1. Rating agencies and the media ................................................................ 19

1.2.2. Liquidity risk ........................................................................................... 20

1.2.3. Sudden stops ............................................................................................ 21

1.2.4. Exchange rate risks .................................................................................. 22

1.3. Banks and government bonds ...................................................................... 22

2. Development of yields in the context of the crisis ................................................ 26

3. Overview of previous empirical research .............................................................. 32

4. Theoretical model .................................................................................................. 37

4.1. Government bond yields .............................................................................. 37

4.2. Fiscal variables ............................................................................................. 39

4.3. Macroeconomic variables ............................................................................ 40

4.4. Financial variables ....................................................................................... 42

5. Description of data ................................................................................................ 48

6. Econometric model ................................................................................................ 52

6.1. Basic econometric model of fiscal fundamentals ................................................ 52

6.2. The baseline model ............................................................................................. 55

6.3. Model accounting for a potential structural break .............................................. 59

7. Robustness checks ................................................................................................. 65

Conclusion ...................................................................................................................... 66

List of graphs and figures ............................................................................................... 69

Bibliography ................................................................................................................... 70

Appendix ......................................................................................................................... 74

7

Introduction

In 2009, the Eurozone was hit by a crisis which is often mentioned as second

only to the Great Depression of the 1930’s. Production of the countries in the Eurozone

declined by 4.5% on average, problems in the banking sector of countries such as Spain

or Ireland were revealed and government debts began to escalate. One of the most

important phenomena of the crisis was the rise of government bond yields. Likely due

to concerns about a potential default of the Greek government, government bonds yields

of this small country experienced a hike from levels around 5% p.a. at the end of 2008

up to more than 27% in mid 2012. Yields paid by most governments1 on their debt have

also followed an upward trend, leading to significant troubles in financing the

government expenses. This study attempts to reveal what caused the sharp increase in

the yields on sovereign debt and why are the yields that investors demand from some

governments significantly higher than for Germany or other countries with

approximately the same level of government debt outstanding to GDP.

The main tested hypothesis relates to the question whether selected financial

sector indicators, variables related to governments’ fiscal policy and indicators

reflecting the competitiveness of an economy in international trade have significant

predictive power over government bond spreads.

The second hypothesis of this article is whether there has been a significant

change in the relationship between explanatory variables and the government bond

spreads following the actions and announcements of the European Central Bank (ECB)

in summer 2012. This hypothesis is tested through an augmented model using

interactions of time dummy variables with its independent variables to reflect the

additional effect of these indicators on government bond spreads in the period between

2007 and 2009, 2009 and 2012 and, lastly, in the period after the ECB interventions in

Summer 2012. An analysis of bond determinants in the last period is one of the novel

pieces of this research.

1 Notable exceptions were government bonds of Germany, Luxembourg, Finland, the

Netherlands and France

8

To test both hypotheses, this study analyzed a range of potential determinants of

spreads of 10 year government bonds in the period from 2000Q1 to 2013Q3 of the

following eleven Eurozone countries vis-à-vis German bond yields – Austria, Belgium,

Greece, Spain, Finland, France, Ireland, Italy, the Netherlands, Portugal and Slovenia.

Estonia and Latvia were not included, as they joined the Eurozone more than halfway

through the crisis, in 2011 and 2014, respectively. Several other countries were left out

from the model based on reasons listed in Chapter 5. Most recent data, typically from

the ECB or Eurostat which were publically available were used to examine these issues

through regression analysis.

In the baseline model, which was used to test the first hypothesis, simple

regression techniques such as pooled OLS were employed along with more complicated

methods of fixed effects (FE) and two stage least squares (2SLS) or with their

combination. A model which accounts for potential structural breaks in 2007, 2009 and

2012 was estimated to investigate the second hypothesis, using 2SLS with inbuilt FE.

The results of the model are showing that apart from government debt and the

budget balance, unit labor costs, changes in the current account balance, investor risk

aversion, and liquidity conditions of a particular country play an important role. Both

fiscal variables and indicators of country competitiveness are thus very important

factors influencing the ease of financing government budget deficits. Indicators

reflecting the relationship between government bond spreads and the situation of the

banking sector were not found to be significant.

The issues analyzed in this study are also of interest for many economists from

the ECB, IMF, national central banks and similar international and national

organizations. As a part of this research, empirical articles mostly from these

institutions were analyzed in chapter 3 and their experience was used where it was

relevant to the hypotheses and scope of this thesis. A range of theoretical articles was

also analyzed and included in this work.

The thesis is organized as follows. Chapter one explores the principles of

government bond valuation and factors which might influence the decision making of

9

investors. These include credit risk components such as government debt and deficit and

current account balances and other sources of risk such as liquidity risk. The connection

between banks and governments, another part of the first hypothesis, will be studied at

the end of the chapter. Chapter two will focus on the factors which might cause a shift

among government bond yield determinants, adding some theoretical arguments to the

analysis of the second hypothesis. The third chapter will summarize the methods used

and results obtained from the most recent literature on the topic and the following

chapter will attempt to connect the insights of previous theoretical and empirical articles

into a theoretical model. Chapter five will summarize the data used in the practical part

of the thesis and the following chapters will attempt to test the two hypotheses of this

study through econometric methods and to draw conclusions and policy implications

from this research. The last chapter concludes.

10

1. Theoretical background

Jay C. Shambaugh’s article “The Euro’s Three Crises” (Shambaugh, 2012)

explores the European Debt Crisis as a combination of a debt crisis, growth crisis and a

banking crisis, which tend to enforce each other. Policy responses aimed at alleviating

one of the crises only, such as austerity, can then worsen the development of the other

two crises, which the Eurozone is facing simultaneously. (Shambaugh, 2012) This

thesis will attempt to incorporate Shambaugh’s insights into the analysis of spreads of

government bond yields in the Eurozone, combining a macroeconomic perspective with

a financial point of view and analyzing various factors that can have an influence over

the sovereign bond market.

Focusing on bonds and debts, there are many opinions in theoretical literature

concerning the idea that the size of government bond yields is determined by a greater

number of variables rather than just by the size of government debt and that debt crises

have various causes. Such stance is advocated by economists like Manasse and Roubini

(2005). These authors distinguish various types of debt crises based on their causes,

stating: “We find that most debt crises can be classified into three types: i) episodes of

insolvency (high debt and high inflation) or debt unsustainability due to high debt and

illiquidity; ii) episodes of illiquidity, where near default is driven by large stocks of

short-term liabilities relative to foreign reserves; and iii) episodes of macro and

exchange rate weaknesses“ (Manasse, Roubini, 2005, p.27) Some empirical articles

analyzed in this work, such as Alexopoulou, Bunda and Ferrando, (2009) also support

this point of view.

These arguments were the motive for the formulation of the main hypothesis of

this study, which examines the relationship between financial sector indicators, selected

fiscal and macroeconomic variables and government bond yields. Based on this

hypothesis, three main areas will be described in the theoretical part of this article – the

sources of credit risk potentially influencing government bond valuation, other sources

of risk and the relationship of banks and sovereigns, which might also influence

government bond markets.

11

The second hypothesis of this study will be tested by analyzing the differences in

government bond spread determinants over time. Chapter 2 will attempt to provide

some theoretical arguments and insights corresponding to this hypothesis, which will

also be tested empirically in the econometric model in the subchapter 6.3.

In the theoretical section, we will first look at government bonds from the

microeconomic perspective of the investor, who purchases bonds. The investor can be

either an individual, or an organization – such as a pension fund or a bank. The focus

will be on understanding the key factors influencing investor’s decision making, which

in turn influences prices and yields of bonds.

A basic way to analyze various investments from the viewpoint of the investor is

to consider their risk, return and liquidity. It would be desirable to maximize the

expected return and liquidity and to minimize the risks. However, there is a fundamental

trade-off between these factors — one cannot have high expected returns without

undergoing major risks. If the investor prefers to minimize risks, instead, he or she can

only invest in financial instruments with low expected returns. When it comes to the

expected return on an investment, the Capital asset pricing model (CAPM) theory is the

most common tool used in Finance to assess valuation of particular assets by

decomposing the expected (ex ante) return of financial assets into a risk free-rate plus

risk premium.

What we can take from the CAPM model and apply in this research is the idea

of risk premiums which lead to higher yields when an asset is perceived as more risk-

prone. Investors who see the riskiness of particular government bonds rising demand a

higher rate of return as a compensation for the higher risks they are undertaking. The

same logic applies to a decrease in liquidity, which leads investors to ask for a higher

liquidity premium. This could be represented by a downward shift of the demand curve

for bonds, resulting in lower bond prices and higher yields. In the following

subchapters, we will look at various risks which influence the valuation of government

bonds in greater detail.

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1.1. Credit risk of government bonds

For debt securities, credit risk is usually considered the main component

influencing their price and yield. Credit risk is essentially the risk that a particular entity

would not be able to pay off its debts, a probability that it would fall into a default. Such

probability is being estimated by rating agencies in their credit ratings as well as by

individual investors, who demand a higher expected return as a compensation for

holding bonds in such a scenario.

Manasse and Roubini (2005) showed that it is not only the ability to repay debts

that counts. They distinguished two factors that play the biggest role in the credit risk of

government bonds – the ability of countries to repay their debt, captured more or less

imperfectly in various measures which link the size of government debt to the output of

the economy or revenues of its public sector – and the willingness of the governments to

repay their debts. The second feature is set up as a comparison of relative benefits and

costs of the default. Openness to international trade and output growth are mentioned as

two of the factors influencing such decision. For an open economy or a fast growing

economy, being cut off foreign capital, a frequent consequence of a government default,

would be more costly. Default thus becomes a less preferable option for such countries.

(Manasse, Roubini, 2005) Based on this theory, investors would demand lower yields

from countries with a high GDP growth rates or from small open economies. This issue

will be examined in the econometric part of the thesis with respect to economic growth.

An important aspect of the European Debt Crisis was also the fear of crisis

contagion. The idea was that a default of one country would push yields up for other

periphery economies and thus would make them more vulnerable. At the same time, the

departure of Greece or other troubled economies from the monetary union was also

pronounced as unacceptable by the political elites of the Eurozone and was seen as a

large potential threat.

1.1.1. Government deficits and debt dynamics

Government deficits and debts should according to the theory be one of the main

determinants of government yields, as they are fiscal fundamentals directly connected to

the probability of default and thus closely related to credit risk. A higher budget deficit

13

and a higher level of debt both imply greater credit risk and should theoretically cause

government bond yields to rise if all other factors are held constant.

Since the aggregate amount of debt also depends on the size of a particular

economy, we will focus rather on the Debt to GDP ratio in the discussion about the

impact of debt on government bond yields. When it comes to factors which cause the

Debt to GDP ratio itself to rise or fall, the following equation of debt sustainability

holds:

∆Dt= (ri –gt) x Dt-1 +primary balance

In this equation, D is the debt to GDP ratio, r is the nominal interest rate paid by

the government, g is the nominal growth rate and “primary” stands for the primary

budget deficit as a proportion of the GDP, which is the government deficit cleared of

debt interest payments. We can see that the higher the nominal interest paid on

government debt, the higher is the new level of debt. This effect, however, is typically

relatively small due to a long maturity of government bonds. It presents an opposite

effect than this paper is analyzing and the econometric problems that this conclusion

poses for government bonds analysis will be commented further in the text.

Nominal GDP growth has an important role in decreasing the Debt to GDP ratio.

We can draw a very important conclusion from this relation: a country with a budget

deficit does not need to have a rising Debt to GDP ratio, as long as its economy is

growing faster than the nominal interest rate. An example of this was the case of many

EMU members before the outbreak of the crisis.

However, the opposite is also true - when the nominal interest paid on

government debt is higher than the nominal GDP growth rate, than the Debt to GDP

ratio can be escalating despite a positive primary budget balance. Thus, we can be

rightly worried that the Debt to GDP escalation in the periphery countries of the

Eurozone during the crisis may not be largely due to a gap between government

revenues and expenses, but also a product of a lower nominal GDP growth rate.

14

Shambaugh (2012) goes even one step further and combines this theoretical

outcome with the government expenses multiplier. According to his analysis, a

multiplier of 1 or more would mean that a country with a large amount of debt

outstanding which would decide to cut government expenses would end up with worse

growth prospects and an even higher level of Debt to GDP. George Soros (2013) draws

similar information from this relation, stating: „The financial problem is that Germany

is imposing the wrong policies on the Eurozone. Austerity doesn’t work. You cannot

shrink the debt burden by shrinking the budget deficit. … In conditions of inadequate

demand, budget cuts cause a more than proportionate reduction in the GDP – in

technical terms the so-called fiscal multiplier is greater than one.”

It is interesting to note that according to ECB data from the 3rd

quarter of 2013,

Greece has recently changed its primary budget deficit into a surplus (ECB SDW,

2014). This was also noted by the world media, such as Reuters or Wall Street Journal.

However, as the equation above postulates, a country which decreases its primary

deficit to zero is still not out of the woods and its Debt to GDP may continue to rise due

to poor growth prospects or low level of nominal GDP growth, which would push the

bond yields up, taking all other factors constant.

Government debt influences real government bond yields in two ways: it can

cause the crowding out of private investment or it can increase the risk of a default,

leading investors to demand a higher default risk premium. Both effects lead to a

positive relationship between government debt and yields in the long term.2

A high level of government or even corporate and household indebtedness might

be potentially damaging for the growth prospects of the economy. It could then have an

additional effect on government bond yields through lower growth, which makes it

harder for a government to decrease its Debt to GDP ratio.

When we look at the graph illustrating the development of government debt of

each of the 16 members of the Eurozone who entered the monetary union before 2010,

2 More on these issues can be found in Poghosyan (2012)

15

we can see a downward or sideways trend in the pre-crisis years and a strong upward

trend after the onset of the crisis. There are only 3 countries, which have managed to

keep their sovereign debt below the 60% of GDP, as stated in the Maastricht criteria, for

the whole period – Luxembourg, which only recently has seen its debt rise over 20% of

GDP, and Finland and Slovakia, which both ended just below this line as of the 3rd

quarter of 2013. Some countries, like Austria, Belgium and Germany, managed to keep

their Debt to GDP relatively stable, while debt levels of Ireland, Greece, Spain and

Portugal have risen sharply, leading the investors to begin questioning their ability to

repay the debt and pushing their government bond yields much higher.

Figure 1: Total government debt as a % of GDP for Eurozone member countries

Source: Own graphics based on Eurostat data; countries are labeled using the 2 letter ISO codes, similarly

to other graphs used in this thesis 3

1.1.2. International competitiveness and government bond yields

Various authors such as Merler and Pisani-Ferry (2012) or Kang and Shambaugh

(2013) view the European debt crisis to a large degree as a balance of payments crisis. If

their theory is correct, it should be possible to find a significant relationship between a

3 The codes for the EU countries can be found here:

http://publications.europa.eu/code/pdf/370000en.htm

16

country’s external balance or another proxy for competitiveness and the yields it has to

pay on its debt.

An important thing to keep in mind is how lack of competitiveness represented

by a negative Current Account balance directly impacts the gross domestic product. As

a country’s competitiveness deteriorates, exports fall and imported goods become

relatively more attractive for consumers than domestically produced goods. Thus, the

sum of net exports falls, contributing to a GDP decline.

In an economy with a flexible exchange rate, this would generally be offset in

the short run by a resulting domestic currency depreciation, which would again make

domestic goods more competitive in the international markets. But since the observed

countries were members of a currency union with fixed exchange rates towards each

other, the imbalances may persist for a relatively long time period.

Significant and lasting pre-crisis Current Account imbalances were indeed

reported in various Eurozone countries. It is in fact the problem that all PIIGS4

economies shared, as illustrated on the Figure 2. It is also important to note that during

the crisis, these imbalances have started to improve. Ireland was the most successful in

clearing its international trade imbalances, reaching a positive Current Account balance

in the second half of 2010 and in most quarters of the following year. Italy had

generally milder Current Account deficits than other periphery countries.

What could be the causes of such imbalances of competitiveness? Kang and

Shambaugh (2013) report that a drop in exports was not the main factor driving the

Current Account into deficits, as the ratio of exports to GDP remained relatively stable

for all PIIGS economies apart from Ireland, where larger structural changes were taking

place. The Current Account consists not only of exports and imports of both goods and

services, but it also includes net income from abroad and current transfers. And these

are the items which have led the Current Account into bigger deficits for Greece, Spain,

4 An acronym used for the most troubled economies of the Eurozone during the crisis: Portugal,

Ireland, Italy, Greece and Spain.

17

Portugal and Ireland. (Kang, Shambaugh, 2013) Domestic economic boom, fiscal

deficits and cheap credit were also mentioned in the study as factors which helped to

increase the size of imports and thus lead to growing Current Account deficits.

Figure 2: Current account balance for PIIGS countries and Germany

Source: own graph based on OECD data

Several economists such as Costantini, Fragetta, and Melina (2013) have

pondered the idea that inflation differentials are one of the determinants of government

bond yields. This effect would work also through the channel of real exchange rate,

making the domestic goods less competitive. The Real effective exchange rate will

therefore be discussed in the theoretical model along with other indicators of

competitiveness.

The issue of competitiveness is closely related to the existence of a single

currency area in the Eurozone. As Merler and Pisani-Ferry (2012) mention, the

intention to decrease external imbalances among Eurozone countries and to prevent a

balance of payments crisis was used as one of the reasons for establishing the monetary

union. From hindsight, we can now state that it has failed in achieving this goal and

probably contributed to a worse development in this area.

18

Membership in the Eurozone altered the ability of countries with large external

imbalances to restore their competitiveness, which could have influenced the yields on

sovereign debt.

At the end of their analysis, Sinn and Wollmershaeuser (2011) summarize the

problems that countries of the Eurozone periphery are facing in the following way,

which can serve as an illustration of the thoughts of economists, who view the European

Debt Crisis primarily as a crisis of competitiveness: “The cheap credit that the Euro

made possible for the periphery countries led to inflationary bubbles and huge Current

Account deficits.“ Authors also mention that at first, private capital inflows were large

enough to cover these deficits. This changed with the start of the crisis and private capital

flows had to be substituted with rescue packages. Capital flows before and during the crisis

will be discussed further in the text.

1.1.3. Inflation and bond prices

The economic phenomenon of inflation is related to the government bond yields

in several ways – not only it influences the real yield of the bond investment and thus its

valuation, but it also has an effect on the changes of government debt to GDP measure

as well as an influence on the competitiveness of monetary union member countries

through inflation differentials. That being said, this chapter will focus just on the effect

on investors and bonds, with the other issues being discussed separately in other

chapters.

A high level of inflation would mark the deterioration of stability of a particular

economy. It would complicate economic calculation and investment decisions and make

a long term investment more risky. Investors would demand higher yields as a

compensation for increased risks.

While some countries like Italy or Greece had a history of relatively high

inflation levels, high inflation was not the case in the period between 2000 and 2013 in

any of the Eurozone countries. The monetary union indeed led to a convergence of

inflation rates among its member countries, but some inflation differentials persisted.

Among the countries who were members of the EMU from the beginning, Spain and

Luxemburg had the highest average annual inflation with the figure standing at 2.8%.

19

Germany and France had the lowest average inflation rates of 1.7% and 1.8%,

respectively. (ECB SDW, 2014) But even some of the countries with higher than

average inflation rates have experienced periods of deflation during the European Debt

Crisis, thus being exposed to greater variation of inflation rates.

Alexopoulou, Bunda, and Ferrando (2009) mentioned another effect, which

could link the inflation rate to government bond valuation. Since central banks are

committed to keeping the inflation rate low, an increase in the inflation rate often leads

to a tighter monetary policy, pushing the interest rates and government bond yields up

Considering a general effect of an increase of inflation on bond valuation from

the investors’ point of view, we could expect a rise in nominal bond yields in response

to an increased inflation rate, as inflation decreases real yields. The effect of inflation on

the Debt to GDP measure, which is analyzed later, however, complicates the overall

relationship between these variables somewhat.

1.2. Other sources of risk

Apart from credit risks, there are also other sources of risk which might

influence government bond valuation. Among these, liquidity risk and exchange rate

risk are the most pronounced. Liquidity risk is also connected with significant changes

in the sovereign debt ratings and with the theory of sudden stops, which will also be

briefly explained in this chapter.

1.2.1. Rating agencies and the media

Sovereign bond ratings serve as an additional signal about the quality of the

debtor, which can influence investor decision making. As long as ratings are considered

trustworthy, they can also decrease the costs of gathering information for the investors.

Afonso, Arghyrou and Kontonikas (2012) have found a significant relationship

between rating announcements and government bond spreads. However, there are

concerns about the effect of sovereign bond ratings with respect to government bond

yields. Because it takes time before a rating agency responds to new information,

investors often expect the changes in sovereign bond ratings, and so the credit rating

change can be already partially priced in the market prices before the change is

20

announced. (Flores, 2010) Ratings might be partially influenced by the development of

yields on the bond markets as well. The relation of credit rating agencies and the

government bond market thus remains unsure, while rating announcements may have an

effect on bond yields, since they serve as a market signal about quality of the

counterparty.

What seems to matter the most with respect to credit ratings are downgrades and

upgrades which change the status of an asset from the investment grade to the

speculative grade, or vice versa. As Flores (2010) notes, “If a country is downgraded to

speculative grade the pool of potential investors can shrink dramatically, causing more

serious financial market consequences.” This refers mostly to so-called forced sales of

government bonds by organizations required to hold mostly or exclusively the financial

assets from the investment category. An important event when sovereign bond ratings

could have significantly impacted bond yields was thus the downgrade of Greece in

April 2010, which sent Greek bonds into the speculative category and brought fears of a

possible default of this country, raising the yields on Greek debt.

Sonja Juko (2010), analyzed the following fast loss of confidence of the investor

community in the ability of the Greek government to repay its debt. Her finding was

such that the media had a decisive role in this process, and both the frequency of

mentioning a troubled country in the media and the expressions used to inform about its

economic situation matter for its bond valuation. (Juko, 2010)

As mentioned above, rating downgrades which send an asset towards the

speculative grade, can lead to forced sales by banks and institutions, which have certain

prescribed requirements about the quality of the debtor. This can lead to further

problems with liquidity and eventually produce sudden stops.

1.2.2. Liquidity risk

Liquidity risk depends on the volume traded of particular assets as well as

market depth. Low market depth would mean that every transaction would have a non-

negligible effect on the market equilibrium. Since a low market depth would imply that

a large scale buyer of bonds would have to pay more money on the last purchased

bonds, due to a price increase caused by his own transaction, it is likely to push

21

government bond yields upwards, ceteris paribus. A similar effect is predicted for

illiquidity of a market for bonds of a particular country as a whole. As the previous

subsection implies, liquidity of the market for bonds of a particular country can shrink

or increase in response to significant changes in sovereign ratings.

1.2.3. Sudden stops

Economists like Sinn and Wollmershaeuser (2011) are in favor of the sudden

stops theory as a description of the reasons for solvency issues and a following sharp

decline of the gross domestic product in the periphery countries of the Eurozone. This

theory works with the international capital flows. These flows can experience a sharp

and often unexpected turn in their overall trend in the form of investor flight from the

country, likely causing the effects mentioned above. Such outflow will cause financing

problems both for the private sector players and for the government. The former affects

spreads both directly, by restricting the pool of potential investors, and indirectly, by

leading to sharp economic declines as observed in Asian or Latin American countries in

late 1990s. (Calvo, Carmen, 2000)

The effect should be observable in the balance of payments and in Target 2

(Trans-European Automated Real-Time Gross Settlement Express Transfer), a clearing

system used to process payments between national central banks (NCBs) of Eurozone

member states. Sinn and Wollmershaeuser (2011) view the pre-crisis balance of

payments imbalances as similar to those which have caused the fall of the Bretton

Woods System. They measure the imbalances based on the country positions in the

Target2. Significant imbalances have indeed been documented in this measure with

Germany owning accumulated claims on the Eurosystem worth 390 Billion EUR, while

PIIGS countries had a negative position of 404 Billion EUR. Authors of this study also

reveal that Target liabilities are essentially additional government debt, which was

largely unnoticed until lately, since it is carefully hidden in the balance sheets of

respective national central banks. (Sinn, Wollmershaeuser, 2011, pg. 4) Merler and

Pisani-Ferry (2012) also mention significant imbalances in pre-crisis Target 2 balances

as well as in the first years of the European Debt Crisis.

22

Target position of a specific country is significantly related to its Current

Account balance. If a Current Account deficit is not offset by an inflow of private

money, represented by the financial account, it has to be equalized by an increase of

Target balance liabilities. Current Account surpluses would be similarly converted into

Target claims on other National central banks.

Sharp reversals of international capital flows that are documented by these

statistics imply a potential effect on widening government bond spreads. This should be

captured to a large degree by the current account dynamics.

1.2.4. Exchange rate risks

For foreign bond holders, exchange rate risk also plays an important role, but it

is not the case for investors from other countries within a monetary union, who are

major holders of EMU government bonds apart from domestic investors. Therefore, we

can consider this effect to be negligible during the European debt crisis. For the pre-

crisis period, a range of papers5 claim, that the elimination of exchange rate risk was the

main reason for the convergence of government bond yields in the Eurozone after the

introduction of the Euro.

1.3. Banks and government bonds

This section will focus primarily on the relationship between banks and

sovereigns during the crisis. This relationship has been examined in several papers, but

it has not been a typical part of empirical research on government bonds.

Banks are a very important part of a modern economy due to their role in

providing loans. The business of a bank is largely pro-cyclical, which can be seen from

the performance of bank stocks and bank ROE ratios. The functioning of the loanable

funds and credit booms and busts are also essential drivers of the nominal GDP.

An important fact to notice about banks is the structure of their balance sheets,

where bank capital is typically just a small fraction of its total assets. The most

significant component of bank assets were loans to Euro area residents, which

5 Such as Klepsch (2011)

23

accounted for 55.2% of total assets. Most of the loans went to the households and to

monetary financial institutions (MFIs), loans to non-financial corporations amounted for

somewhat less. Bank holdings of securities were the next major item

Government bond holdings amounted to approximately 5.7% of total bank assets

on average. For some of the countries which faced difficulties in the banking sector over

the last years, this figure is lower than the average, such as 3.25% or 4.97% for Greece

and Ireland, respectively, while for Spain, the percentage is higher and government

bonds amount for 9.37% of bank assets. (ECB SDW, 2014).

Angeloni and Wolff (2012) studied the impact of holding government debt

during the economic crisis on bank stock performance and found out that ownership of

Greek, Portuguese, Italian and Irish government bonds indeed had a significant effect in

different months of 2011. But factors like bank location in either a periphery or core

country of the Eurozone seemed to matter even more.

Figure 3: The structure of government bond holders as of 2011

Source: Eminescu, 2012

From the other side, we can look at the structure of government bond holders,

which Eminescu (2012) gathered for each country separately, as shown on the graph

above. As we can see from the graph, domestic financial corporations owned very

substantial proportions of government bonds. The relationship between banks and

24

governments could thus be very important factor influencing sovereign bond valuation.

If such effect is revealed, it might be particularly strong for Luxembourg, Malta, or

Slovakia, where domestic banks own the largest proportion of government debt. Bond

holdings by non-residents were also significant and accounted for approximately 31.5%

of debt in these countries. For some countries analyzed in this study, this proportion was

bigger than 50% - that was the case of Finland, Austria, Portugal, Slovenia, Ireland,

France, the Netherlands, Germany and Belgium (Eminescu, 2012).

Returning back to the aggregate balance sheet of banks in Eurozone countries,

we can notice that 54% of the total bank liabilities corresponded to the deposits of Euro

area residents, while only 7.85% on average corresponded to the bank capital. The ratio

of capital to risk-weighted assets varied greatly between countries, ranging from 4.90%

for Finland, 4.99% for the Netherlands, or around 5.70% for Belgium and Germany to

15.19% for Greece and 18.07% for Cyprus. We can notice that the bank capital forms a

very small part of the total assets, when compared to non-financial sectors of the

economy. This means that any bank is in need of a rigorous risk management, in order

to prevent the risk of insolvency.

The size of bank capital is also approximately on par with the bank holdings of

government debt, which has been mostly increasing during the European Debt Crisis.

(ECB SDW, 2014). As government bond holders, banks can thus be significantly

impacted by changes of government bond prices. If the yields rise for example due to a

greater level of government debt, bond prices will drop and banks suffer financial losses

on their assets. And the resulting bank losses can turn into yet another problem for the

government.

We must acknowledge that the relationship between banks and governments

goes both ways. While banks can be severely impacted by a negative development on

government bond markets, problems of the banking sector can also negatively impact

public finance. Banking crises are increasingly regarded as public problems, since banks

at the risk of illiquidity or insolvency usually turn for help from the state. Rational

investors would price this risk into the valuation of government bonds, further

undermining the weak position of the banking sector.

25

Currently, a new solution in the form of a Bail in is being discussed in the

European Commission after being tested on Cyprus. The principle is that bank stock

owners should bear the costs of bank rescue rather than the taxpayers. Therefore, the

use of a Bail in instead of a Bail out might weaken the relationship between banks and

governments discussed above.

26

2. Development of yields in the context of the crisis

Government bond yields have gone through rapid development over the last 14

years. This chapter will introduce the macroeconomic and political context, which has

been changing bond valuation during the second half of the observed period – after the

onset of the crisis. This way, it will elaborate on possible theoretical arguments related

to the second hypothesis of this study.

Figure 4: Development of government bond yield spreads vis-à-vis Germany

Source: own graphics based on Eurostat data

Based on figure 4, we can see a long period characterized by small yield spreads

over German yields and further convergence of government bond yields among the

members of the Eurozone and countries which were joining the monetary union. Only

the governments of Slovakia, Slovenia, Malta, Greece and Cyprus have faced spreads

over German government bond yields of 100 basis points (or 1 percentage point) or

more in the period before the financial crisis. These differences were decreasing, as the

respective country was about to join the EMU6 After the arrival of the crisis, spreads

widened and premiums of 400 b.p. or more became common, especially for the

periphery countries of the Eurozone. But after the first half of the year 2012, trends have

6 Greece entered in 2001, Slovenia in 2007, Cyprus and Malta in 2008 and Slovakia in 2009

27

shifted again and the period after the ECB intervention which happened during that year

was characterized by decreasing spreads.

What were the institutional factors that could have impacted the size of

government bond yields during the crisis? Let us look briefly at the story of the

European Debt crisis and policy responses which were used to counter it and could be

connected to this topic.

The European Debt Crisis was preceded by the Subprime crisis, which

originated in the USA and was at its start in 2007 registered only by a minority of

economists before the media started to inform the public about massive bank losses.

Wyplosz (2010) compares the development of European economies and the US

economy at the outbreak of the crisis and concludes that the Eurozone could not have

avoided being affected by the crisis. The crisis spread to Europe mostly through the

channel of exports and bank holdings of toxic assets, often securitized or complex

financial assets based on mortgage and loan portfolios.

ECB reacted to the first blow of the crisis by providing more liquidity to banks

in order to prevent an interbank market liquidity crunch. This could have helped to

prevent an increase in the credit risk of government bonds and thus could have kept

spreads relatively low at the beginning of the crisis. But ECB’s reaction in terms of

lowering the key interest rates was remarkably slow according to Wyplosz (2010). The

first reduction in the ECB interest rate happened in October 2008, more than a year after

a similar decision was adopted by the FED. (Wyplosz, 2010) In July 2008, ECB even

increased its key interest rate by 25 basis points on inflation fears.

Some of the same factors which led to the financial crisis in the USA were also

relevant in the periphery countries of the Eurozone. Spanish economy, for example,

showed many signs of overheating before the crises – Spanish GDP was growing at

rates around 3.5% p.a., real estate prices were appreciating more than 10% p.a. on

average and the country was experiencing a strong lending boom. (OECD Stat Extracts,

2014) In Ireland, an unsustainable lending boom was also a key cause of economic

problems. Some laymen and economists claim that the crisis had its roots in poor fiscal

28

management. While this might be true for Greece or Italy, Spain did certainly not have a

more irresponsible pre-crisis fiscal policy that an average Eurozone member country,

with a positive budget balance in 2005, 2006 and 2007 and debt to GDP well below the

average for Eurozone countries. (Eurostat database, 2014) Yet, investors started pricing

its government bonds differently after the arrival of the crisis, which suggests the role of

some other factors.

Still, it took some time before the effects of the world-wide economic crisis

spilled over to Europe in full. In 2009, all Eurozone economies have seen their real

GDP declining, but more significant problems have not yet manifested themselves and

spreads barely started increasing. In October 2009, the Greek government announced

that statistics about the country’s public finance were inaccurate. At that time, Greece

was already suffering a large capital outflow (Merler, Pisani-Ferry, 2012), but the

announcement came as a proof for many investors that Greece cannot be considered a

reliable debtor. Government bond markets have reacted strongly and pushed yields up.

For example, the nominal yield that the Greek government had to pay on its newly

issued debt has risen from 4.57 % in October 2009 up to around 11 % just a year later.

From this point, high government bond yields were causing significant problems for the

financing of periphery countries of the Eurozone, and various measures aimed at the

rescue of troubled government and banks were being discussed.

Merler and Pisani-Ferry (2012) mention that we can distinguish three types of

public help to countries experiencing financial problems during the European debt crisis

– the first of them being assistance from the EU or the International monetary fund,

which according to the available data focused more than two thirds of its lending into

developed countries of Europe, which was a big shift compared to the past. (Reserve

bank of Australia, 2011) The second form of public support mentioned in the article

were liquidity provisions for troubled banks and the third one, which might be the most

significant for this research, were the purchases of sovereign bonds by the ECB.

Government bond purchases by the ECB, which are happening under the

Securities Markets Programme (SMP), were launched in May 2010. The official

intention cited on the ECB website as a reason for this program is “to address tensions

29

in certain market segments that hampered the monetary policy transmission

mechanism” (ECB website, 2014). The aim of such operations is based on a simple

economic idea – an increased demand for government bonds by the ECB should press

the price of bonds up, decreasing the nominal yields that a particular government has to

pay to finance its deficits.

Central banks are typically prohibited to buy government bonds on the primary

market. Such programs, known as debt monetization, were found to lead to rapid

inflation in the past. Instead, central banks including FED, ECB, Bank of England and

Bank of Japan engage themselves in the purchase of sovereign debt on the secondary

markets. Similarly to the purchase on the primary market, a government which is facing

a high probability of being assisted by a central bank through asset purchases in case of

economic difficulties is subject to moral hazard – it is not motivated so thoroughly to try

to keep a healthy balance of public revenues and expenditures. This relates to potential

endogenity of government debts and deficits, which will be discussed further in the text.

In comparison to the Quantitative easing (QE) conducted in the USA and in the

UK, ECB’s QE could be considered relatively small. Some economists argue that a

smaller scope of the QE in the EU might be a reason for a repeated decline of the

economy which took place in 2012 and 2013.

The Long Term Refinancing Operations (LTRO) were announced by the ECB at

the end of 2011, several weeks after the inauguration of its new president, Mario

Draghi. This policy was focusing on solving bank liquidity problems. A report by the

Central bank of Denmark (2013) commented the impact of the LTRO in the following

way: “The measures concerning liquidity in euro improved the banks' liquidity, which

might support their willingness to undertake market making in government securities.”

Thus, The LTRO should lead to decreased government bond yields, ceteris paribus.

This move was followed by a second lending arrangement between Greece and the IMF

in March 20127, with a similar expected effect of calming tensions on the financial

markets.

7 The 1

st lending arrangement was launched in May 2010.

30

In late July 2012, President of the European Central Bank Mario Draghi

announced, that the EBC would do whatever it takes to save the Euro. During the

following month, possible employment of outright open market operations was

mentioned by the ECB. Markets responded in a positive way: stock prices started rising

again, CDS spreads began decreasing and government bond yields for PIIGS countries

started falling rapidly. (The Economist, 2012) For example, 10 year Greek bonds have

seen their yields cut in half in just 5 months, dropping from 24.34 % in August 2012 to

11.1 % in January 2013. From a long term perspective, this event proved to be a key

turning point of the crisis, whether directly thanks to the intervention of ECB or as its

indirect result or as a result to some other, more or less unrelated effects. With the

exception of a few brief and unsystematic movements, spreads between the government

bonds of the periphery countries and 10 year German bonds were narrowing

significantly once more, and such trend is still continuing as of February 2014.

Apart from the ECB interventions, various other policy responses have been

discussed and some of them were implemented. One of the policy responses was a

banking union, planned as an attempt to make the banking sector more stable. If

successful, this policy could help to narrow yield spreads, if the relationship between

banks and sovereigns proves to be significant. The policy was drafted and planned to be

launched at the end of 2014 (ECB website, 2014).

From the perspective of bond markets, the establishment of the European

Financial Stability Facility (EFSF) in May 2010 was also an important move. This

organization was established to help troubled Eurozone economies to recover their

financial sector and restore their credibility on the financial markets. It is authorized to

intervene in both primary and secondary bond markets and to assist with the rescue of

banks by providing loans with specified conditions to the sovereigns. (EFSF, 2013)

To finance its needs, EFSF is allowed to issue its own bonds on the financial

markets. The infamous money contributions of member states towards the EFSF

actually serve as a backing for these bonds, which thus enjoy a high rating and bear

lower yields. (EFSF, 2013) A similar way of funding is used by the World Bank and the

31

IMF. EFSF assisted for example with the recapitalization of the financial sector in

Portugal and Ireland.

While the EFSF is a temporary organization with a limited scope, a new

permanent mechanism intended to avert the crisis was later established under the name

European Stability Mechanism (ESM). However, De Grauwe (2011) mentions some

features that might counter the potential effect of the establishment of ESM on

government bond spreads. According to his article, ESM obliges member states of the

EMU to attach so called ‘collective action clauses’ to its newly issued debt. Since this

clause might ask investors to take some of the losses when a country is assisted by the

ESM, it might actually lead to higher risk premiums on sovereign debt. Also, De

Grauwe ponders that the ESM asks an unnecessarily high risk premium on its loans and

thus damages the credibility of its programs. (De Grauwe, 2011)

As of 2013, trends in the government bond markets seem to be changing. While

the PIIGS have seen their yields finally decreasing, yields of government bonds with the

best credit ratings have increased slightly, probably indicating a return of the investors

towards more risky assets such as government bonds of the Eurozone periphery or

towards the stock market, which has been rising significantly during the year.

More than any other policy response, the ECB announcements and interventions

which began in summer 2012 have coincided with a major turn in the trend of overall

development of government bond spreads. While this cannot serve as a proof that these

events are related, this chapter provides a theoretical argument for the existence of a

significant change in bond yield determinants following the ECB intervention, which

would support the second hypothesis and will be explored empirically in section 6.3.

32

3. Overview of previous empirical research

This chapter will attempt to summarize the methods and conclusions on the most

relevant and most recent empirical papers on the topic of government bond yield

determinants during the Sovereign bond crisis available as of March 2014. Since the

range of empirical literature on this topic is very broad, this section will focus on the

articles written after the outbreak of the European Debt Crisis in 2009 and mainly on

those analyzing the countries of the Eurozone.

All of the articles mentioned in this section have studied government bond

spreads rather than government bond yields, mostly modeling spreads against German

bonds. An important part of the models was the lagged dependent variable, which builds

the high persistence of yields into the model.

In the work of Afonso, Arghyrou and Kontonikas (2012), which was selected as

one of the main articles for this research, the authors used the two stage least squares

(2SLS) method with fixed effects8, testing a number of variables such as implied stock

market volatility index (VIX) as a proxy for the international risk factor, 10 year

government bond bid-ask spread as a liquidity measure, expected values for the size of

government debt and budget deficit, real effective exchange rate, which is used to

account for macroeconomic imbalances, and the GDP growth rate. Afterwards, the

authors considered the possibility of a structural break in the model and examined it by

adding dummy variables for the period after 2007, accounting for the outbreak of the

global credit crunch, and after 2009, when the financial crisis evolved into the European

Debt crisis.

The authors concluded that while government bond yields did not reflect

changes in macroeconomic and financial fundamentals before the 2008 outbreak of the

8 The same approach was followed in this thesis, after a discussion of various econometric

techniques

33

crisis, this changed significantly in the period after 2008. The government budget was

found significant for the baseline period, while government debts were significant only

in the period of the European Debt Crisis. The growth of industrial production was

largely insignificant and similar results were obtained for the real exchange rate. In a

modified model, they also estimated the effects of credit ratings, which were found to

be a significant regressor as well even after controlling for all relevant factors. The

dummy variables accounting for potential structural breaks allowed the authors to reveal

that liquidity become a statistically significant regressor only after 2009, while the

international risk factor captured by the VIX measure entered this category in 2007.

Determinants of government yield bonds have thus changed significantly over time. A

similar approach will be used to test the second hypothesis of this thesis.

Another recent study of long term government bonds was conducted by

Catharina Klepsch (2011) using the FGLS method. It also focuses on risk aversion,

liquidity risk or credit risk, but uses different proxy variables – for example the risk

factor is measured through the corporate bond spread in some of the model

specifications, which might be considered an unlucky choice, as it also depends on the

size of government bond yields. Klepsch also acknowledges that in the conclusion,

saying that VIX is a better risk measure with respect to the European Debt crisis.

(Klepsch, 2011, p. 21). Her advice on this matter will be followed in this thesis.

Klepsch (2011) includes a control variable of GDP growth rates and also uses a

dummy for country-specific fixed effects as well as a financial crisis dummy, which is

applied from June 2007 on. Afterwards, various model specifications use an interaction

of the crisis dummy variable with variables measuring risk aversion and credit or

liquidity risk, similarly to (Afonso, Arghyrou, Kontonikas, 2012),. The article also

features a graphical decomposition of the yield spreads by various factors used in the

regression, as shown below. According to Figure 5, the model had large residuals in the

pre-crisis period, but not after the arrival of the crisis, when investors became more

cautious of the macroeconomic fundamentals. The research conducted by Mrs. Klepsch

further claims that ECB interventions prior to 2011 had only a very negligible influence

on yield spreads.

34

Figure 5: Decomposition of government yields

Source: Klepsch (2011)

Schuknecht, von Hagen and Wolswijk (2010) built their model on

microeconomic foundations based on the framework of the standard portfolio theory.

They consider the probability of default and repayment in case of default and the way it

would influence the decisions of the investor. They use the size of debt issue as a proxy

variable for liquidity risk and the corporate bond spread as a measure of risk aversion.

The researchers used dummy variables for membership in EMU and for the crisis.

However, most of the observations used for estimating the model are based on pre-crisis

data. The outcome of this study is such that fiscal fundamentals were priced in

government bond spreads even before the crisis and the relationships became stronger

after the onset of the crisis. A “safe haven” status was additionally confirmed for

Germany. This phenomenon will be discussed in the following chapter.

Tigran Poghosyan (2012) uses quite a different approach. First of all, he models

real bond yields. Secondly, the study distinguished long run determinants, which set the

overall trend for yields, and short run determinants, which result in deviations from the

trend curve. To distinguish between the two classes of bond yield determinants,

Poghosyan uses the panel cointegration method, specifically a PMG estimator.

35

(Poghosyan, 2012) Thirdly, while other studies usually control for market risks

associated with holding government bonds, such as liquidity risk, Poghosyan focuses

exclusively on fiscal and macroeconomic variables. Finally, his article modeled

government bond yields of 22 advance economies over a span of 30 years.

The long run determinants considered in Poghosyan (2012) include the Debt to

GDP ratio for the general government sector and potential growth. Short run

determinants include changes in debt ratio of general government debt to GDP, changes

in inflation, changes in real short-term interest rate, changes in output growth and

changes in the primary balance ratio. Interestingly, his independent variables do not

include any indicators that would describe the state of the financial sector or any

monetary indicators, apart from the real short-term interest rate. Poghosyan also

mentions that the short run determinants could be supplemented by other variables such

as a proxy for policy uncertainty, which, however, would be difficult to quantify. The

author expects a positive correlation between both debt to GDP level and potential

growth on one side and government bond yields on the other side based on economic

theory. Both of these expectations are confirmed by his model. The outcome of this

research also suggest that markets were likely undershooting the yields before 2009 and

overshooting during the crisis for the periphery countries of the Eurozone. (Poghosyan,

2012)

An article by Costantini, Fragetta, and Melina (2013), is also using panel

cointegration methods to explore the determinants of government bonds. This article did

not work with the common international risk factor, but instead, it used a new feature in

the form of competitiveness gaps, which were measured as cumulated inflation

differentials with respect to Germany. The findings of this article were such that fiscal

imbalances and liquidity conditions are the main long run determinants of government

bonds. Inflation differentials were also found as a significant regressor (Costantini,

Fragetta, Melina, 2013). The study did not reveal a structural break in the model.

Alexopoulou, Bunda and Ferrando (2009) were focusing on new EU member

countries instead, largely before the crisis period. They estimated a dynamic error

correction model using a pooled mean group technique (PMG) to distinguish long run

36

and short run determinants. The dependent variable in this model is the yield spread

against Euro area average.

Compared to other studies, large number of other variables, such as the Current

Account balance, gross external debt-to-GDP or degree of openness of an economy,

measured as the sum of imports and exports as ratio to GDP were included to this

model. Among previous empirical studies, this one seems to take into the international

scope of the problem the most. From this point of view, it could serve as an inspiration

for this thesis. However, there could be objections about the way data is treated in this

paper. Its authors had to linearly interpolate monthly values for debt to GDP ratio and

some other variables, which could bias the estimation slightly. Unlike its methods and

variables, outcomes of this study are not so relevant for this research and may be seen in

the original text.

37

4. Theoretical model

The main tested hypothesis of this study relates to the question whether selected

financial sector indicators, variables related to governments’ fiscal policy and indicators

reflecting the competitiveness of an economy in international trade have significant

predictive power over government bond yields.

The second hypothesis of this article is whether there has been a significant

change in the relationship between explanatory variables and the government bond

yields following the actions and announcements of the ECB in summer 2012.

In this part, the issues introduced in the Theoretical background section will be

discussed in connection to these two hypotheses and expected direction of the

relationship between particular explanatory variables and government bond yields will

be mentioned.

4.1. Government bond yields

Almost all articles studying this topic empirically have focused on modeling

bond yields as a spread of the yield paid by a particular country over the German 10

year bond yields. This approach is followed in this study.

The main reason for such decision is such that spreads make our risk measures

cleared from fluctuations in long term interest rates as well as from other factors such as

the market conditions. If we tried to model yields instead of spreads against German

bonds, we would be facing one significant obstacle. The international risk factor, which

was found to be the most significant determinant of government bond yields in a

number of previous studies (as mentioned in the 3rd

chapter), would in this case have an

opposite effect on the yields which were paid by countries perceived as “safe havens”

and by those perceived as more risky.

German bonds were chosen for this comparison, as Germany was considered the

most stable Euro area economy during its crisis and governments of other countries thus

38

had to pay a risk premium on their debt when compared to Germany. Also, the

government budget and debt of Germany was relatively more stable compared to other

Eurozone countries.

However, it is also important to mention the weaknesses and limitations of using

spreads for the research of government bond yields. German bond yields are naturally

not constant over time, but they are subject to both long term trends and short term

fluctuations. Models of government bond spreads are built on the assumption that the

German 10 year bond yield can be taken as a risk-free rate, and the part of the yield

above this level can thus be considered the risk premium for a certain country at a given

time.

Also, as the economic situation of Germany and the phase of the business cycle

that Germany experienced various stages of the business cycle from 2000 to 2013, the

10 year government bond yield of Germany also changed significantly. At the turn of

the century, German economy was publically considered to be the “sick man of

Europe”, with a stagnating level of GDP and a negative Current Account balance.

Accordingly, the German government had to pay approximately 5% nominal interest on

its 10 year bonds, which was systematically higher that the yields on the public debt of

Luxembourg in the period from 2002 to 2006. During the European Debt Crisis,

Germany was conversely considered to be the healthiest economy in the Eurozone and

investors were willing to accept very low interest rates on their investments in German

bonds.

Lastly, German bonds also enjoyed a “safe haven” status during the crisis, which

had the effect of reducing the yields its government had to pay, ceteris paribus. Let us

briefly explore this phenomenon. Financial crises are typically the times of heightened

risk aversion among investors. The shift towards assets perceived as safer is apparent

from various data capturing asset price, including the graph of the development of

government bond yields, which is featured in Chapter 3. With government bonds, this

effect should lead to the fall of government yields for assets with a high credit rating

(usually AAA) and strong underlying fundamentals during the crisis periods. Ejsing,

Grothe, and Grothe (2012) have indeed identified “safe haven” flows for both Germany

39

and France during the European Debt crisis. These countries were viewed as a part of

the “core” Eurozone and their macroeconomic outlook was considered relatively stable

by many investors and by the rating agencies. These “safe haven” flows pushed the

bond yields of these countries down, offsetting an increase in the credit risk component.

Figure 6: Comparison of German yield to maturity with the average YLD of

Eurozone16 countries

Source: Own graphics based of Eurostat data

The results might also depend on the type of bonds, as the sensitivity of yields

and bond prices to changes of various variables depends on whether they are fixed

interest bonds, zero coupon bonds, or floating rate notes. Such detailed segmentation is

however hard to find on aggregate level and none of the empirical papers analyzed have

used this as a component of their analysis. Thus, it will also be left out form this study.

4.2. Fiscal variables

Fiscal variables as a group are the fundamentals on which the price and yield of

government bonds are based. They belong to the credit risk component, which the

investor has to consider and which he usually prices into the bond valuation in the form

of risk premium.

40

Government debt and budget deficit

A rising Debt to GDP ratio for a particular country suggests a rising probability

of sovereign default, which implies a positive relationship between the size of

government debt and government bond yields. De Grauwe (2012) recommends testing

both the linear rand non-linear forms of this relationship, as investors holding

government bonds or a highly indebted country are likely to be more sensitive towards

an increase of the Debt to GDP ratio. (Giavazzi and Pagano (1996) in De Grauwe,

2012). Both versions will therefore be tested in this research.

The relationship between budget deficits and yields is analogous – a worsening

budget balance would increase the credit risk of an investment into government bonds.

Based on that, we could predict a negative casual relationship between the budget

balance of a particular country and the yield it has to pay on newly issued debt.

There is, however, a certain probability that these variables would bring

endogenity into the model, as a lower level of government bond yields might encourage

a particular government to be less careful about the state of its public finance and to

increase future deficits to unsustainable levels. This claim is sometimes mentioned with

respect to the periphery states of the Eurozone, such as Greece or Italy, in the period

before the crisis. Models accounting for potential endogenity will therefore be tested in

Chapter 6.

4.3. Macroeconomic variables

Interest rates

The effect of this variable is somewhat limited due the facts that spreads are

analyzed instead of yields. The main reason for adding this variable to the model is to

capture the effects of the entry of a particular country to the EMU. This event, as

observed on the data from the ECB, was tightly connected to the convergence of interest

rates into a single 3 month interest rate for the Eurozone. Allegedly, the convergence of

interest rates could be one of the factors explaining the pre-crisis convergence of

41

sovereign bond yields, along with the convergence of exchange rates. For the whole

Eurozone, the interest rate also captures the effects of monetary policy.

Indicators of competitiveness

To measure the effect of competitiveness of a particular economy on the yields

its government has to pay on newly issued debt, the Current account, Unit labor costs

and Real effective exchange rate are all added to the analysis and tested in various

model specifications.

Based on the discussion in chapter 1.3., a negative relationship between the

Current account balance as a percentage of GDP and government bond yields is

expected. Shambaugh (2012) mentions, that a Current account deficit also reflects

borrowing from abroad by all agents in the economy. According to his point of view, a

large Current account deficit might decrease the probability of sovereign debt

repayment, which connects it closely with government bond yields. The model will also

be tested with the changes in the level of Current account balance.

While the nominal exchange rate among the members of a monetary union stays

fixed, the Real Effective Exchange Rate (REER) is changing over time, reflecting the

competitiveness of an economy’s production when placed on international markets.

Significant and lasting imbalances can develop and impact trade among nations. An

appreciation of the REER makes domestic goods more expensive for foreign consumers

and thereby leads to a fall in exports, ceteris paribus. At the same time, it makes

imported goods relatively cheaper compared to domestic production, encouraging a

shift towards bigger imports.

An increase in the relative unit labor costs (ULC) could imply a forthcoming

worsened competitiveness position of a particular member of the EMU, as higher unit

labor costs would be reflected in rising prices of domestic production on international

markets which could not be offset by nominal exchange rate changes in a monetary

union. Thus, a bigger rate of change of this variable might have a positive impact on the

size of government bond yields, potentially leading to worsened servicing of the

country’s debt.

42

Both REER and ULC are closely related to the Current Account balance, so

multicollinearity will have to be checked carefully.

Output growth

A negative relationship is expected, since a lower growth rate generally

increases government revenues from taxes and positively impacts the government

budget balance in further quarters. Also, as discussed in section 1.2., better growth

prospects of the economy might help to decrease the Debt to GDP ratio over time. Both

of these factors would decrease credit risk and investors would thus demand a lower

risk premium, ceteris paribus.

Inflation

A higher inflation rate decreases the real return which bond investors receive. As

shown by Mishkin (2009) and numerous other authors, it decreases demand for bonds,

pushing bond prices down. A potentially opposing effect would arise as the outcome of

the effect of inflation on the changes of government debt to GDP, which was mentioned

in chapter 1.2. This effect would be controlled for if debt to GDP ratio is also used as a

part of the model along with the inflation rate. Therefore, we can expect a positive link

between inflation and government bond yields, where an increase in the inflation rate

would lead to higher government bond yields or spreads, ceteris paribus.9

4.4. Financial variables

Risk aversion and global financial volatility

Risk aversion was found by previous studies to be an important factor

influencing the changes of government yields over time. Risk aversion plays the role of

a common factor, which would lead government bond spreads against Germany higher

in response to a higher volatility in the global financial markets. The Chicago Board

Options Exchange Market Volatility Index (VIX) seems as a fitting indicator and it used

9 An opposite effect may also appear in periods of deflation, such as in Ireland and several other

countries during the year 2009 and in Greece in 2013. The possible effect of a deflation spiral

would, however, be captured by the coefficient for GDP growth.

43

by almost all preceding empirical articles. VIX is a forward-looking measure,

estimating volatility 30 days ahead of time. A positive relationship between VIX and

government bond spreads is thus expected.

Figure 7: The Chicago Board Options Exchange Market Volatility Index (VIX)

Source: own graphics based on combined BIS and CBOE data

Liquidity risk premium

The importance to distinguishing the effect of liquidity risk on government

bonds from the impact of credit risk for the selection of suitable policy responses is

summarized well by Ejsing, Grothe, and Grothe (2012, p.3): “If spread widening is caused

mainly by concerns about liquidity risk, measures to improve secondary market liquidity could

be considered. On the other hand, if wider spreads reflect mainly concerns about the

sustainability of fiscal positions, this would call for corrective fiscal policy measures.“

Theoretical background behind this exogenous variable is quite straightforward:

if an investor can sell and asset fast and with small costs of doing so, which is

essentially the definition of liquidity, he or she is willing to pay more for such

investment. In this situation, an investor is willing to receive a smaller interest on a

more liquid asset, ceteris paribus. Less liquidity would mean that the investor would

have to cope with transaction costs and bear the opportunity cost related to the time

44

value of money, since the asset cannot be converted to another financial use quite as

fast.

Liquidity could be measured through volume of assets traded at each moment, as

is customary for the stock market, or by the Bid-ask spreads or the amount of shares or

bonds issued. The Bid-Ask spread could potentially also reflect the credit risk of a

particular country, which would lead to the simultaneity bias. Therefore, it was not used

in this study. Ejsing, Grothe, and Grothe (2012) also dispute the idea that Bid-Ask

spreads and other traditionally used liquidity measures truly reflect the changes in

liquidity of government bonds. All these factors leave a problem of using a reliable

proxy for this component.

In the end, the approach of Klepsch (2011) was followed in this paper. This

approach uses the amount of government bonds issued by a respective government as a

proxy for liquidity. A precise composition of this variable will be explained in the next

chapter. An advantage of this approach is that is can count with all issued bonds, which

are traded either on organized exchanges or, more commonly, on over the counter

(OTC) markets.

Euribor-Eonia spread

This variable reflects interbank market stress and the solvency problems of

banks. (Angeloni and Wolff, 2012) It reached record high levels at the end of 2008,

when global financial markets were experiencing a credit crunch.

This measure has the same value for all countries, so it cannot be used to

distinguish between financial market problems in each country. But it could show the

times when banks were facing increased liquidity and solvency problems which could

require government intervention with the possibility of endangering the fiscal position

of the public sector of a respective economy. Since banks are also significant

government bond holders, their problems reflected by a rise of this variable could also

lead to a decreased demand for government bonds, implying higher spreads. It could

therefore be used together with banking indicators which are summarized below.

45

Financial situation of banks

The variables analyzed as those with a chance of reflecting the relationship

between the financial situation of banks and government bond yields examined in this

thesis were return on equity (ROE) as a bank profit measure, the amount of non-

performing loans (NPL), the average capital to assets ratio of banks and the size of the

banking sector compared to the size of the economy.

ROE would directly measure profits or losses of banks which could theoretically

measure the direct influence on public finance most closely, but there are several

problems associated with this indicator. First of all, as mentioned in the chapter about

banks, bank management has a large influence on this variable, making it a less reliable

indicator of the real situation of a particular bank. Secondly, the data on ROE are not

reported very systematically. Every country tends to report these data with its own

frequency and sometimes not to report them at all for longer periods of time – such as

Greek ROE data for 2012 and 2013. Right before Greece stopped reporting the ROE

data, this indicator showed extremely negative results. For the 3rd

quarter of 2011, a

ROE of -21.5% was reported and in the 4th

quarter, this ratio dropped even lower, to an

almost incredible level of -169.2%. (World Bank data, 2014) For the whole year 2011,

deposits in Greek banks dropped by a staggering 20%, which was probably one of the

reasons for massive losses. (EBF, 2012) The choice of not reporting this data is thus

likely to be influenced by the development of the data itself and so adding these data to

the model would not be advisable. One of the other reasons for bank losses was likely

the devaluation of bank assets such as loans and government bonds in their portfolios.

This direct impact of banks’ government bond holdings on ROE makes the variable

subject to endogenity. Including it in the model could thus bias the results, as it would

be difficult to distinguish the effect of ROE on government bond spreads from the

opposite effect.

NPL would quite naturally reflect the financial health of a bank, since loans to

households and corporations are the main assets of a bank and an increasing ratio of

non-performing loans to all bank loans indicates a rising risk of huge bank losses, which

would most likely require government action in order to save the respective banks,

leading to a predicted positive relationship between this variable and government bond

46

spreads. However, a significant problem for using this indicator in an empirical study is

that it is reported by banks themselves and thus not reliable enough in times when

threats to the stability of the banking sector are imminent.

The bank capital to assets ratio reflects the size of the safety net which could

prevent the bank from becoming insolvent in times of financial difficulties. The impact

of changes in this indicator on government bond yields is definitely very indirect, but

might be significant. Bank management can also influence this variable in the long

term, as a result of the trade-off between bank profits and safety. However, it is less

subject to manipulation, since it reflects historical outcomes and it is typically less

volatile. We could thus expect that in the times of economic expansion, an increase of

this variable would not have very significant effect of government bond valuation, but

there might be a significant effect in the periods of financial crisis, when a larger safety

net represented by a higher capital to assets ratio could decrease the need of government

intervention in case of large bank losses. Therefore, a higher level of this ratio might

decrease the credit risk associated with government bonds, leading to a lower yield

demanded by the investors.

The use of this variable in the model was suggested by Klepsch (2011), who

used it together with the measure of the relative size of a country’s financial sector,

represented by the ratio of aggregate bank assets divided by the GDP. Together, both

variables might provide a better picture about the probability and potential impact of

banking sector rescue on the sustainability of public finance of a respective country,

linking the situation of banks and governments in a more robust way than the indicators

mentioned above would. As in Klepsch (2011), both of these variables are listed

separately in the final model.

Generally, it can be said that multiple problems concerning financial sector data

were revealed. First of all, a lot of historical data is not publically available in periods

before 2010, so such indicators could be used only for a part of the crisis. Secondly,

most of the data is reported only annually or semiannually and interpolation could lead

to distortion of the information, due to a big volatility of the underlying indicators, such

as the ROE. Lastly, it is also key to determine which of the data could be considered

47

trustworthy. This data is reported to statistical offices by banks themselves and banks

have a lot of space for discretion when it comes to adjusting most of them. For this

reason, only two of these variables were used in the econometric model - the capital to

assets ratio, which can show how prepared a bank is to withstand losses on its assets and

deposit outflows, and the size of the banking sector relative to the size of the

economy10

, which could illustrate the potential magnitude of the effect of saving a

troubled banking sector on the credit risk attached to the yields that a respective

government has to pay on its debt.

10 measured as the ratio of total assets of the banking sector to GDP

48

5. Description of data

This chapter will present the measurement and a statistical description of the

data mentioned in the theoretical model. For the econometric model, quarterly panel

data for 11 countries of the Eurozone, mapping the time from 2000Q1 to the third

quarter or 2013 were used.

Figure 8: Data description and sources

Source: own table; variables with the asterisks (*) have been seasonally adjusted

Figure 8 summarizes data sources, variable meanings and the units in which they

are measured throughout this study. Data for VIX were combined from two sources -

CBOE and BIS – since each organization did not publically provide all of its historical

data. Some of these variables were calculated as ratios of indicators which were

publically available. The 3 month nominal money market interest rates take the value of

the interest rates prevailing in the Eurozone for the period when a particular country

was an EMU member or the rate observed in a particular country in a given quarter

prior to its entry to the Euro Area.

The variable listed as IssuedP_ma might need further explanation. It was derived

as a percentage of debt issued by a particular country in each quarter on all debt issued

in the Eurozone during the same quarter, following the approach of Klepsch (2011). A

49

one percentage point increase of this variable therefore means that the percentage of

total debt issued in the Eurozone in a given quarter which can be attributed to a

particular country has risen by one percentage point.

Some variables were obtained in the monthly frequency and converted to

quarterly data. Several of them were available in the form without seasonal adjustments

and were corrected accordingly. Some other exceptions were present and are

summarized as follows. The gross debt issued data for Ireland, unlike for other

countries, include debt denominated in EUR only. Statistics of total GDP, which were

necessary for the calculation of the Bank assets to GDP indicator, were also slightly

different for several countries – for Greece, Portugal, and Ireland, the data were not

work-day adjusted, for Greece, they were also not seasonally adjusted. This was

partially corrected by smoothing the data through moving averages before the

calculation was conducted. The precise smoothing process is described in the next

chapter.

As mentioned before, several countries, namely Luxembourg, Cyprus, Slovakia,

Malta, and Germany, were left out from the original list of 16 EMU members. Based on

the analysis of debt issued, Luxembourg was left out, as its government bond market is

very small and in 50 out of 55 quarters, the new debt issued amounted to zero. A

financial market with low liquidity is generally considered less efficient in reflecting the

true value of assets. Cyprus also proved to be a problem, as the country does not

provide a good quality of data about its government bond yields. In the last years, yields

were rounded to the nearest integer, and thus very imprecise. For Slovakia and Malta,

the statistics of bank capital to assets ratio did not seem reliable. Since it was one of the

key variables for testing the first hypothesis, both countries were left out of the final

version of the model, while being included to some of the model specifications which

were tested before. Germany was naturally left out since the model was investigating

the bond spreads over the bond yields of this country.

The descriptive statistics which are presented in Figure 9 are therefore based

only on the final dataset comprising 11 countries. Based on the table, we can notice that

an average spread of government bond yields compared to Germany was about 1.15 %

50

or 115 basis points. An augmented table would show that during the European Debt

Crisis, this average value rose to 269b.p. and its standard deviation increased as well.

Figure 9: Descriptive statistics of variables

Source: output from Stata

The average government budget in the time period from 2000 to 2013 was in a

deficit of 3.24%. and the average Debt to GDP ratio stood at 71.6%. Both of these

figures are above the values required for entry to the Eurozone by the Maastricht

criteria. Statistics of the budget balance were, however, influenced by large one-time

deficits connected to the rescue of the banking sector of Ireland, which reached to more

than 30% of Ireland’s GDP. During the debt crisis, the average budget deficit was

bigger than 6.5% and government debts rose to 86.4% of GDP on average. Variability

also increased somewhat. The trends for the current account balance were different –

during the economic crisis, differences among countries in terms of this variable started

decreasing.

Proceeding to the correlation analysis of variables used in various model

specifications, we can notice the following. We would logically expect a negative

relationship between the size of government debt to GDP ratio and the budget balance

to GDP. The correlation coefficient of -0.5 for these two variables confirms this

relationship, but doesn’t show it as too strong. This might be due to the factors

51

mentioned in the chapter about government debt dynamics. A positive correlation of

0.34 can be observed for the size of government debt to GDP and the size of new debt

issued in the form of bonds relative to other Eurozone countries. Inflation and interest

rates had a relatively high correlation coefficient of 0.6. Based on this fact, model

specifications omitting one or the other of these variables will be tested and discussed in

the section on the robustness of the model.

The Current Account balance does not show high correlation with any other

independent variable. This is better than expected because it will be possible to

distinguish the effect of the Current Account balance on yields from the effect of the

ULC and changes of the real effective exchange rate. However, unit labor costs are

correlated with the real effective exchange rate, with a correlation coefficient slightly

above 0.4.

52

6. Econometric model

This section will attempt to capture the empirical relationships between the

dependent variable in the form of government bond yield spreads over Germany and a

set of independent variables based on the theoretical model and the availability of

reliable data. The chapter is ordered into three subchapters, where the first one

introduces a simplified model of the relationship between government debt and spreads.

The second part tests the first hypothesis of the study and the last section examines the

second hypothesis.

6.1. Basic econometric model of fiscal fundamentals

The simplistic analysis in this subsection aims to illustrate the relationship

between Debt to GDP ratio and government bond yields. It is based on a similar

analysis conducted by De Grauwe and Ji (2012). Debt to GDP as a single exogenous

variable in their research managed to explain only a small proportion of yield

variability. The relation also showed that deviations from the fitted values line were not

random – in fact they were very time dependent and moreover, the biggest deviations

were those for just 3 countries – Greece, Portugal and Ireland, at specific time moments

during the crisis. (De Grauwe, Ji, 2012, p.5)

Results of the simplistic model in this thesis have essentially the same result.

Compared the analysis by De Grauwe and Ji, the same simple model with the inclusion

of the latest data brought even higher deviations from the fitted values, as it captured the

peak of the European Debt Crisis, when the government bond spreads of the periphery

countries of the Eurozone could not be explained by the surge of government debt

levels alone. Some differences also arise because of the inclusion of Slovenia in the

model. This country was an outlier especially in the pre-crisis era, as shown on the left

side of the graph. Also, when a regression is tested after excluding Greece, Portugal and

Ireland as 3 countries with much larger outliers, Slovenia produces most outliers after

the onset of the crisis, along with Spain and Italy, the last remaining PIIGS countries.

53

The nature of the data used in this research makes them prone to problems with

heteroskedasticity and autocorrelation. To account for these problems, all models

presented in the empirical section were all estimated using heteroskedasticity robust

errors and clustering by country.

After accounting for these problems, the size of government debt is not found to

be a significant regressor. Moreover, based on the F-test, this model is not significant as

a whole. But it was only after clustering that the model became insignificant, while the

relationship was significant when controlled for heteroskedasticity only. Augmented

regressions for the pre-crisis period and the period after 2007 revealed that Debt was not

a significant regressor before the onset of the crisis, but became significant afterwards.

This would be consistent with the results of other authors and will be examined in the

subsection 6.3.

This corresponds to the fact that the coefficient measuring the effect of changes

in the debt level on government spreads over Germany could be biased through the

omitted variable bias (OVB), wrong functional form11

or strongly influenced by

outliers. Since this simple model explained approximately 21.95% of variation of

spreads, it is likely that other factors also play a role in determining the price that

governments pay for their debt.

Figure 10: Results of the basic model

Source: own model using Stata

11 for example a linear form for a quadratic relationship

54

Let us look at this simple relationship based on a graph to examine the model

residuals. As shown on Figure 10, a positive relationship between government debt and

spreads is predicted by the model. However, the regression result is likely to be

influenced by the outliers in the upper right corner. Since these outliers are only the

observations for periphery countries during the crisis, we can notice that these countries

were probably punished harder for a greater level of debt, while investors demanded a

much smaller risk premium from other countries. That would hold as long as there are

no other significant differences among the countries apart from the variance in debt

levels. Since it is likely that there are other significant differences between countries,

such as a different size of the capital to assets ratio of banks and current account

balance, this statement is merely an idea for what needs to be checked in the following

subsection.

Figure 11: The relationship between government bond spreads and government debt

with fitted values from the basic model and selected outliers

Source: own graphics using Stata

55

De Grauwe and Ji (2012) also revealed that the regression line was much sharper

after the onset of the crisis than in the pre-crisis years. Similar pattern also holds with

the data used in this work and this finding is consistent with the works of other authors

as well. When the crisis era was separated according to the time dummies used in this

study12

, a significant change was revealed between the relationship in the period of the

global financial crisis (2007-2009) and the following period of European Debt Crisis

(from 2009 onwards), suggesting that investors started to punish countries with high

debt levels more than before. There doesn’t seem to be such a profound shift for the

period after the ECB intervention.

A very strong linear relationship between the residuals of this basic model and

the dependent variable was also revealed. For observations with the smallest spreads

compared to Germany, the model generated only very small residuals, while for

countries with the highest spreads, very big residuals were present. This suggests

problems with heteroskedasticity, which thus needs to be checked in every model

specification.

Overall, it can be said that this simplistic model would generate huge residuals,

especially for the periphery countries of the Eurozone during the European Debt Crisis.

For example, for Greece, up to 20 percentage points would remain unexplained at the

worst point of the crisis. When the model is not supplemented with a lagged dependent

variable, almost the whole increase of government bond spreads from the onset of the

crisis is marked as not explained by the surge of sovereign debt.

6.2. The baseline model

The selected model specification corresponds to the following regression equation:

YLD_diffit = α + β0 YLD_diffit-1 + β1 VIXit + β2 IssuedP_mait + β3 Debtit + β4

Debt_sqit + β5 Budget_mait + β6 IRit + β7 GDPit + β8 HICPit + β9 EuriborEonia

+ β10 CAchit + β11 ULC_mait + β12 capital_assetsit + β13 AssetsGDPit + Uit

12 These dummy variables will be explained later in this chapter

56

Where YLD_diffit corresponds to the spread of quarterly average of government

bond yield of country i at time t over the yield paid by the German government.

IssuedP_ma is a variable showing the amount of government debt issued by the

particular country compared to the rest of the EMU. The variables marked _ma were

seasonally adjusted through Stata using the values from the actual quarter and 3

previous quarters. The variable Budget_ma stands for the government budget balance

and takes on negative values whenever there is a government budget deficit. IR stands

for the 3 month interest rates prevailing in the chosen country. GDP variable represents

the annualized GDP growth rate of each economy, comparing the actual quarter to the

same quarter of the last year. HICP represents the annualized inflation rate, accordingly.

EuriborEonia stands for the 1 month Euribor spread over the Eonia rate.

The model also includes two variables which are intended to capture the effects

of competitiveness of a certain country on government bond spreads. CAch stands for

the change of the current account balance of a respective country compared to the

previous quarter13

, while ULC_ma represents the index of Unit labor costs for each

country, where 2005 is selected as the base year with the value of 100. Last two

variables attempt to model the relationship between banks and sovereigns.

Capital_assets is the average bank capital to assets ratio, which tries to capture the

health of the banking sector and AssetsGDP is the measure of total bank assets as a

proportion of the gross domestic product, which stand for the size of the banking sector.

An important feature of the econometric model of government bond yields is the

inclusion of a lagged yield as a explanatory variable. This approach is taken essentially

by all authors. Not including this term to the model would mean not accounting for the

high persistence of bond yields and risking that the error term and the regression results

could be significantly biased through the omitted variable bias. (Klepsch, 2011)

This regression function is tested with different econometric methods – pooled

OLS, fixed effects (FE), two stage least squares (2SLS) and 2SLS complemented with

fixed effects. OLS treats panel data as if it were unrelated units, therefore it might not

13 this was conducted after seasonal adjustment of the original variable

57

be appropriate for this research. Because of that, other econometric techniques are also

tested. Fixed effects methods add factors which are constant in time, which might

account for for unobserved country-specific effects. 2SLS is additionally able to control

for endogenity in the model. 2SLS is constructed by using the past lags of dependent

variables going back one quarter for the lag of government bond spreads and one, two

and three quarters for the other dependent variables. A potential disadvantage of this

technique is that the earliest observations have to be left out from the model to check for

possible endogenity through 1st, 2

nd and 3

rd lags of all dependent variables.

Figure 12: Results of the baseline econometric model obtained through OLS (1), FE (2),

2SLS (3) and 2SLS with FE (4) - shortened

(1) (2) (3) (4)

VARIABLES YLD_diff YLD_diff YLD_diff YLD_diff

YLD_diff_lag 0.958*** 0.945*** 0.906*** 0.895***

VIX 0.00958*** 0.00878** 0.00671** 0.00816**

IssuedP_ma -0.00379** -0.0191* -0.00652*** -0.0202**

Debt -0.00726 -0.00372 -0.0230* -0.0177

Debt_sq 4.92e-05 6.79e-05* 0.000157* 0.000209***

Budget_ma -0.0470*** -0.0557*** -0.0694*** -0.0753***

IR 0.0428** 0.0767* 0.0892** 0.130***

GDP 0.00585 0.0109 -0.0197 -0.00463

HICP 0.0790 0.0775 0.0768 0.0746

EuriborEonia -0.0471 -0.0520 -0.597 -0.419**

CAch -0.0616 -0.0636 -0.473* -0.477**

ULC_ma 0.0132*** 0.0188** 0.0203*** 0.0244***

capital_assets -4.901 -10.91 -3.126 -11.28

AssetsGDP -0.00788*** -0.0271 -0.00386 -0.0354*

Fixed effects

Controlled

Controlled

Observations 571 571 538 538

R-squared 0.946 0.938 0.934 0.939

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: own model results obtained from Stata

In the Figure 12, the shortened results of Model 1 obtained through all the

aforementioned techniques are listed. Standard errors are reported in the full table in the

Attachment. The biggest focus in the model interpretations will be on the fourth

econometric technique – 2SLS with inbuilt FE.

58

When it comes to fiscal variables, both government debt in its quadratic form

and government budget were significant regressors in this model. Government debt

became significant when more advanced econometric methods were used, suggesting a

presence of endogenity. A worsening government budget would also lead to an increase

in the spreads, which was thus one of the most likely explanations for the surge of

yields for Ireland. The coefficient for this variable tells us that an improvement of the

budget balance by 1% of GDP is typically associated with a 0.075 percentage point

decrease in spreads. For a country with an initial debt level of 100% of GDP, a one

percentage point increase of government debt would lead to approximately 0.024

percentage point rise in the spread. The higher absolute value of the coefficient for

government budget compared to government debt shows that investors focus more on

the changes in government budget deficits rather than the overall government debt level.

Both indicators of a country’s international competitiveness were also

significant. A higher change of the current account, implying a shift towards a more

positive or less negative balance would lead to lower government bond spreads. An

increase in unit labor costs, which makes a country less competitive in international

trade, would also lead to an increase in spreads for the respective country, ceteris

paribus.

The issue of multicollinearity was already mentioned in the section on Data with

respect to these variables. Based on the findings, current account balance was used

together with unit labor costs in some model specifications, but the real effective

exchange rate was not tested together with the other two variables. When it was tested

as the sole representative of the impact of competitiveness on government bond spreads,

no interesting findings appeared and the coefficient was declared insignificant. Based

on the correlation coefficient of -0.5 between government debt and the budget balance,

a model specification without the budget variable was also tested, but debt remained

largely insignificant when tested for the whole period from 2000 to 2013.

Among banking sector indicators, only the size of the banking sector,

represented by the Bank assets to GDP ratio, was significant, although only on 10%

confidence level. Also, the effect would be very small and the coefficient of this

59

variable went in the unexpected direction, claiming that the bigger the banking sector of

a particular country, the lower are its government bond spreads over Germany. No

relationship was found between the Capital to assets ratio and government bond

spreads.

Looking at other financial variables, we can observe that VIX was shown as

significant by all the econometric techniques. A one point increase in this measure is

therefore associated with 0.008 percentage point increase in government bond spreads

on average. This would confirm the positive relationship between global risk aversion

and volatility of financial markets and government bond spreads, which was also

present in every previous empirical study which included this variable. Liquidity on

government bond markets was also revealed as significant with a coefficient going in

the predicted direction, although the coefficient differed somewhat when various

techniques were employed. The Euribor-Eonia spread was declared significant by the

model, albeit with the opposite sign. Since there could have been important shifts in the

direction of the relationship across different time periods, this issue will be analyzed in

the second model.

When it comes to the control variables of the model, both GDP growth and

inflation were not significant in this model. The short term interest rates prevailing on

the money market, however, were found significant. The model suggests that a 1%

increase in this rate corresponds to an increase of government bond spreads by

approximately 1.3 percentage points over the whole period from 2000 to 2013.

The overall results show that variables related to the fiscal fundamentals were

found to be significant, similarly to the proxies of a country’s international

competitiveness. For both groups, hypothesis one was thus confirmed. On the contrary,

the relationship between banks and government spreads was rather not confirmed by the

model.

6.3. Model accounting for a potential structural break

In this section, the second hypothesis of the study is tested using the 2SLS

method complemented with fixed effects, which is expected to be the most robust

60

technique with respect to endogenity from model 1. It uses an augmented model

consisting of the variables used previously and complemented by interactions of each

independent variable with dummy variables accounting for potential structural breaks.

Three such dummy variables indicating specific conditions in the financial

markets, which appeared with the global financial crisis, are used. The first dummy

variable, labeled _C for crisis is used for the period between 2007Q3 and 200901, when

the global financial crisis was taking place. For the period beginning in 2009Q2 and

lasting until the ECB intervention in 2012Q2, second dummy variable, shown as _EC in

the interactions, is used. Both dummy variables are based on the work of Afonso,

Arghyrou and Kontonikas (2012, p. 14).

The last dummy, “Post_ECB” or _P attempts to model the structural break after

the interventions of the ECB and the corresponding period of decreasing yield spreads,

which was mentioned previously. The interactions of independent variables with this

dummy variable are what interests us the most when investigating the second

hypothesis of this study, especially in comparison to the coefficients for the period on

the European Debt Crisis. They attempt to measure the additional effect of a particular

variable on government bond spreads in the specified time period.

In this section, a total of five model specifications are used. The first three

models are testing the additional effect of each variable in the period of the global

financial crisis (model 1), European Debt Crisis (model 2) and the period after the

intervention of the ECB (model 3) separately. The fourth model specification combines

the effects of the financial crisis and the European Debt Crisis and the fifth one uses

these variables together with the interactions for the period after the ECB intervention.

This model interests us the most, as it tries to measure recent changes among

government bond yield determinants after accounting for potential structural breaks

during the two preceding periods of the crisis.

61

Figure 13: Results of the model accounting for potential structural breaks (shortened)

(1) (2) (3) (4) (5)

VARIABLES YLD_diff YLD_diff YLD_diff YLD_diff YLD_diff

YLD_diff_lag 0.860*** 0.944*** 0.901*** 0.881*** 0.971***

VIX 0.00949* 0.0155*** 0.00314 0.0107** 0.00581*

VIX_C 0.000727

0.00803 0.00371

VIX_EC

0.00217

-0.0186 0.0257**

VIX_P

-0.235*

-0.272

IssuedP_ma -0.0140 0.0203** -0.00741 -0.0205 -0.0170

Issued_C -0.00139

-0.0101*** -0.00858***

Issued_EC

-0.00468

-0.00844 -0.00309

Issued_P

0.0145

0.00897

Debt -0.0252** 0.0107 -0.0333* -0.00427 -0.0133

Debt_C 0.0170

-0.0144 -0.00505

Debt_EC

-0.0673**

-0.00731 -0.0388***

Debt_P

0.0423

0.00650

Debt_sq 0.000284*** -5.55e-05 0.000397*** 0.000190* 0.000242***

DebtSQ_C -4.41e-05

0.000112 6.03e-05

DebtSQ_EC

0.000483***

-1.02e-05 0.000209***

DebtSQ_P

-0.000364*

-0.000170

Budget_ma -0.0782*** -0.0272 -0.0168 0.0453* 0.00751

Budget_C 0.0698***

-0.0139 0.00494

Budget_EC

0.00751

-0.146*** -0.0141

Budget_P

0.00196

-0.0701

IR 0.238*** -0.00988 0.101*** 0.0208 -0.0312

IR_C -0.216

0.393 0.0331

IR_EC

0.257

0.789** 0.0126

IR_P

5.293*

5.094*

CAch -0.596* -0.262 -0.0610 0.252 0.0568

CAch_C 1.097*

0.0308 0.110

CAch_EC

0.510

-0.549 0.121

CAch_P

-0.184

-0.655

ULC_ma 0.0355*** 0.0134** 0.0148** 0.0507*** 0.00800

ULC_ma_C -0.00322

-0.00378 0.00568

ULC_ma_EC

0.00166

0.00381 0.00480

ULC_ma_P

0.0241*

0.0341*

capital_assets -10.29 -6.714 2.654 7.218 1.161

capital_assets_C -1.120

-16.50** -9.303

capital_assets_EC

9.808

-16.76** -2.041

capital_assets_P

-1.556

-1.260

AssetsGDP -0.0281 -0.00402 -0.00619 -0.0165 -0.00156

AssetsGDP_C 0.0137

-0.0111 -0.000772

AssetsGDP_EC

0.0367

-0.0263 0.0158

AssetsGDP_P

-0.0813***

-0.0502

Observations 546 546 546 546 546

R-squared 0.935 0.958 0.959 0.955 0.963

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: own model results obtained from Stata

62

Results of this estimation, which can be seen in Figure 13, include only the

variables which were essential for evaluating the second hypothesis. All model

variables and standard errors can be found in the complete model table in the

Attachment.

For the fiscal variables, the results are somewhat mixed. Even the baseline effect

of government debt on government bond spreads was found to be a significant

regressor. This suggests that the level of government debt did matter even before the

outbreak of the crisis. An additional effect, approximately doubling the strength of the

positive relationship between bonds and debt appeared in the period of 2009 to 2012.

After the ECB intervention, this additional effect expired and was not replaced

by any other one, suggesting that the relationship between government debt and bond

spreads returned back to its pre-crisis strength. The third model specification, which

was not controlling for the effects of either crisis period, estimated a somewhat

significant weakening of this relationship during this period. When the effect of the

government budget balance was similarly decomposed into the baseline effect and

additional effects, no significant results were obtained.

The same is true for the coefficients associated with the changes in the Current

account balance, the first of the measures of competitiveness used in this thesis. The

most complete model also showed no baseline effect for Unit labor costs and an

additional positive effect in the period after the ECB intervention. Model specification 3

shows a significant baseline effect and an additional effect in this period, both going in

the same direction. Both model specifications would thus indicate that investors became

more mindful of the ULC after the intervention. To understand this additional effect, we

must realize that unlike the pre-crisis era, when significant imbalances were developing

in the periphery countries of the Eurozone, they were already decreasing in the period

after the ECB intervention. With the ULC decreasing for countries such as Greece or

Spain, we can draw a conclusion that after August 2012, investors began to

acknowledge that the unit labor costs of these countries are decreasing and to demand

lower bond spreads from their governments.

63

The fifth model specification does not attribute any significance to the

coefficients of banking sector indicators whatsoever. Only the fourth model

specification, which did not account for a structural break after the ECB intervention,

brought a significant additional effect approximately of the same size during the

financial crisis and the European Debt crisis. Since the variable is measured as a ratio,

this effect would mean that a 1 percentage point increase of this ratio would lead to a

0.166 decrease in government bond spreads.

All models but one show VIX to be significant in the pre-crisis era with the

expected sign. Moreover, model 5 estimates a significant additional effect of VIX

during the European Debt Crisis, suggested that the risk aversion of investors became a

much stronger factor during this period. In contrast with this strong relationship, here

was no significant additional effect of VIX after the ECB interventions.

The fifth model specification suggested no baseline link between the bond

market liquidity and government bond spreads complemented with a strong additional

effect in the era of the global financial crisis, which was significant even on the 1%

confidence level. Based on the model, no significant link between bonds and liquidity

was found after 2009.

Interesting results were estimated for the short term interest rate. Both models

featuring the additional effect of the interest rates on government bonds during the

period after the ECB intervention imply a significant strengthening of this relationship.

The more complex model would predict that a 1% increase of the 3 month money

market interest rate would lead on average to a 5.1% hike of government bond spreads

over Germany, ceteris paribus. Since short term interest rates are tightly related to the

actions of central banks, this effect is possibly a result of investor caution towards

potential attempts of the ECB to increase the interest rates.

Surprisingly, the model estimates a significant and positive relationship between

the GDP growth rate and government bond spreads. However, this might be in line with

similar results estimated for the growth of the potential GDP and such phenomenon is

further discussed in Poghosyan (2012). No significant results were discovered for

64

inflation and the Euribor-Eonia spread, which previously had an unexpected sign,

became insignificant after being decomposed into the selected time periods.

As we can see in the corresponding table, the results were quite different for

each model specification. This might be because the last period, which was not included

in other empirical articles in full, provides relatively few observations. For more precise

and robust results of the second line of models, observations with a higher frequency

would thus be recommended.

Overall, it is not possible to make a clear conclusion about the change of

government bond yield determinants after the ECB interventions in summer 2012, even

though results suggest that the relationship has significantly changed for some of the

variables, such as government debt or the unit labor costs.

The model used by Catharina Klepsch (2011) had higher residuals in the pre-

crisis period than after the outbreak of the crisis. On the contrary, the residuals of the

model used in this article, which can be observed as the difference between actual and

fitted values on Figure 14 in the appendix, were concentrated in the peak period of the

European Debt Crisis which was happening around the end of the year 2011, around the

inauguration of Mario Draghi as the new president of the ECB and the announcement of

the LTRO and specifically in Greece and, to a lesser degree, Portugal and Ireland. This

also corresponds to the residuals from the basic model of fiscal fundamentals, which are

shown on Figure 11, even though residuals of the more complex model are much

smaller and are not related to the dependent variable anymore.

The occurrence of residuals at the peak of the crisis might suggest that investors

were possibly overestimating the risk connected to the ownership of Greek and

Portuguese bonds for a brief period of time. A separate graph for Greece is also shown

in the Appendix. If that would be the case, it might support the view of De Grauwe

(2012) who concludes that “Systematic mispricing of sovereign risk in the Eurozone

intensifies macroeconomic instability, leading to bubbles in good years and excessive

austerity in bad years.”

65

7. Robustness checks

In the previous section, the model was tested through different techniques and

modifications of the key dummy variables were also checked. That already gives us a

lot of information about the robustness of the model. This chapter will test several other

specifications, concerning the choice of independent variables in the model. They will

be used to check whether the parameter estimates remain quite stable or whether they

change significantly when some of the variables are modified.

When the linear form of the relationship between government debt and

government bond spreads was tested on the 2SLS model with fixed effects from the

section 6.2., this relationship was not proved significant anymore, even though the

direction of the relationship remained the same. Apart from that, there were only very

minor shifts in coefficients of all variables and none of the signs have changed. The

same was true when the logarithmic specification of Debt to GDP was used instead of

the quadratic one.

When Greece was excluded from the model, however, bigger changes in

coefficients were revealed and the relationship of Debt and Spreads became

insignificant in both model 1 and model 2. Taking out Ireland or Portugal led to a

different coefficient sign for the size of the banking sector, which remained

insignificant. Similarly to previous studies, this research has found that countries with

big imbalances influence the model the most.

Due to a relatively strong relationship between the interest rates and inflation,

which is predicted by the economic theory and confirmed by the data analysis, model

specifications which omitted one of these two variables were also tested. When the

inflation rate was left out, only one insignificant variable, the spread between the 3

month Euribor and Eonia rate has changed its sign. Everything else stayed very much

the same. Omitting the interest rate yielded very similar results.

66

Conclusion

This thesis analyzed government bond spreads vis-à-vis Germany of eleven

Eurozone countries in the period from 2000 to 2013 combining the findings of previous

empirical articles on the topic with the arguments from theoretical literature on debt and

competitiveness crises.

A novel piece of this work was the usage of data from the second half of 2012

and 2013, which enabled an analysis of the change of government bond spread

determinants in this period. One of the less typical parts of empirical studies concerning

government bonds was also the inclusion and discussion of various indicators

documenting the state of the banking sector and unit labor costs, an additional measure

of a country’s competitiveness.

The main tested hypothesis of this work concerned the question whether selected

financial sector indicators, variables related to a government‘s fiscal policy and

indicators reflecting the competitiveness of an economy in international trade have

significant predictive power over government bond spreads. This hypothesis was

accepted for fiscal variables represented by the government budget balance and the debt

to GDP ratio and for indicators of a country’s competitiveness. On the other hand, the

link between selected banking indicators and government bond spreads, predicted in

theoretical literature such as Shambaugh (2012), was not confirmed by the model.

Based on these results, we should rather not expect a direct impact of policy tools aimed

at the banking sector which were mentioned in Chapter 2, such as the banking union, on

government bonds.

Based on the results, international competitiveness is one of the main factors

influencing government bond valuation. Current account balance changes were deemed

significant by the baseline model and both models showed that unit labor costs belong

to the determinants of government bond spreads. Large imbalances in these variables

are thus harmful for public finance, since government bond yields represent the costs of

servicing debt. The large persistence of such imbalances, which was observed in the

67

PIIGS countries prior to the crisis, is likely connected to the EMU membership, as

elaborated in the theoretical part of the thesis.

Additionally, the model revealed that an increased volatility on the financial

markets or lower liquidity of bonds of a particular government14

lead government bond

spreads higher. This corresponds to the findings of essentially all previous empirical

articles which were studied.

The second hypothesis of this article was whether there has been a significant

change in the composition of government bond spread determinants following the

actions and announcements of the European Central Bank (ECB) in summer 2012. As a

whole, the second hypothesis could not be accepted by the econometric model used in

this study. However, there was some evidence indicating significant changes in the

effect of government debt, where the relationship with government bond spreads

returned to its pre-crisis form, or in the influence of unit labor costs, which became a

more significant factor, following the ECB interventions.

The second model also suggested that there has likely been a positive

relationship of a high magnitude between government bond spreads and interest rates in

the period after the ECB intervention. One possible explanation for this link might be

that markets became very dependent on the ECB policy and development on the money

markets. Currently, short term interest rates are at very low levels and both financial

markets and the media currently do not expect an interest rate hike, especially not as

large as one percentage point or more. Therefore, the relationship might suggest that if

such unexpected change actually happened, markets might react very sharply.15

Based on the significance of the aforementioned variables in both models of this

thesis, it seems that a government intending to keep the yields on its government bonds

low can improve the likelihood of reaching this goal by keeping its public finance

14 apart from Germany

15 related issues are discussed in articles such as this one:

http://www.irishexaminer.com/business/features/ecb-unlikely-to-hike-rates-for-two-years-or-

more-255961.html

68

sustainable in terms of both debt levels and annual government budget balances.

Another key step for a government with this policy goal would be to prevent the

formation of significant macroeconomic imbalances, which could lead to the loss of

competitiveness and potential increase in government bond yields. This implication

holds as long as these policies do not go against each other. An improvement in the

functioning of the bond market of a particular country, leading to a higher liquidity of

government debt, might also help to decrease the yields paid on its government debt,

ceteris paribus.

Going back to the differentiation of debt crises by Manasse and Roubini (2005),

which was cited in Chapter 1, the Eurozone Debt Crisis seems to fulfill mostly the

characteristics of a period of high debt levels due to unsustainable public finance and

illiquidity. Some of the macroeconomic weaknesses such as large current account

deficits were also present in the PIIGS countries. GDP growth, which was mentioned by

these authors as one of the two factors influencing the willingness of a particular

country to undergo a default was largely insignificant in this study. The lower

willingness to default among faster growing economies was thus not confirmed by the

model.

Considering the results of this thesis in the context of Shambaugh’s article

(2012), it indeed seems that multiple interrelated crises were at play in the Eurozone.

The link between country competitiveness and the occurrence of a debt crisis was

suggested by the model. However, no effect of a banking crisis on the debt crisis was

found, possibly due to the data limitations. Apart from this issue, it would be advisable

to use data of a higher frequency in the future research of this topic.16

16 Such data can typically be obtained from paid databases on the internet.

69

List of graphs and figures

Figure 1: Total government debt to GDP for Eurozone member countries

Figure 2: Current account balance for PIIGS countries and Germany

Figure 3: The structure of government bond holders as of 2011

Figure 4: Development of government bond yield spreads vis-à-vis Germany

Figure 5: Decomposition of government yields

Figure 6: Comparison of German yield to maturity with the average YLD of

Eurozone16 countries

Figure 7: The Chicago Board Options Exchange Market Volatility Index (VIX)

Figure 8: Data description and sources

Figure 9: Descriptive statistics of variables

Figure 10: Results of the basic model

Figure 11: The relationship between government bond spreads and government debt

with fitted values from the basic model and selected outliers

Figure 12: Results of the main econometric model obtained through OLS (1), FE (2),

2SLS (3) and 2SLS with FE (4)

Figure 13: Results of the model accounting for potential structural breaks

Figure 14: Fitted values from the model accounting for potential structural breaks

compared to the actual government bond spreads

Figure 15: Potential investor overestimation of risk at the peak of the crisis

70

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74

Appendix

Figure 12: Results of the main econometric model obtained through OLS (1), FE (2),

2SLS (3) and 2SLS with FE (4)

(1) (2) (3) (4)

VARIABLES YLD_diff YLD_diff YLD_diff YLD_diff

YLD_diff_lag 0.958*** 0.945*** 0.906*** 0.895***

(0.0137) (0.0115) (0.0238) (0.0161)

VIX 0.00958*** 0.00878** 0.00671** 0.00816**

(0.00281) (0.00312) (0.00321) (0.00392)

IssuedP_ma -0.00379** -0.0191* -0.00652*** -0.0202**

(0.00158) (0.00913) (0.00246) (0.00977)

Debt -0.00726 -0.00372 -0.0230* -0.0177

(0.00606) (0.00690) (0.0136) (0.0111)

Debt_sq 4.92e-05 6.79e-05* 0.000157* 0.000209***

(3.32e-05) (3.36e-05) (8.23e-05) (7.26e-05)

Budget_ma -0.0470*** -0.0557*** -0.0694*** -0.0753***

(0.0147) (0.0144) (0.0144) (0.0132)

IR 0.0428** 0.0767* 0.0892** 0.130***

(0.0170) (0.0380) (0.0411) (0.0414)

GDP 0.00585 0.0109 -0.0197 -0.00463

(0.0272) (0.0251) (0.0334) (0.0223)

HICP 0.0790 0.0775 0.0768 0.0746

(0.0591) (0.0575) (0.0504) (0.0587)

EuriborEonia -0.0471 -0.0520 -0.597 -0.419**

(0.0418) (0.0446) (0.393) (0.209)

CAch -0.0616 -0.0636 -0.473* -0.477**

(0.0475) (0.0491) (0.250) (0.207)

ULC_ma 0.0132*** 0.0188** 0.0203*** 0.0244***

(0.00399) (0.00741) (0.00698) (0.00671)

capital_assets -4.901 -10.91 -3.126 -11.28

(3.477) (7.935) (2.773) (7.847)

AssetsGDP -0.00788*** -0.0271 -0.00386 -0.0354*

(0.00239) (0.0192) (0.00645) (0.0193)

Fixed effects

Controlled

Controlled

Observations 571 571 538 538

R-squared 0.946 0.938 0.934 0.939

Number of country_id 11

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Source: own model results obtained from Stata

75

Figure 13: Results of the model accounting for potential structural breaks

(1) (2) (3) (4) (5)

VARIABLES YLD_diff YLD_diff YLD_diff YLD_diff YLD_diff

YLD_diff_lag 0.860*** 0.944*** 0.901*** 0.881*** 0.971***

(0.0220) (0.0362) (0.0363) (0.0324) (0.0765)

VIX 0.00949* 0.0155*** 0.00314 0.0107** 0.00581*

(0.00513) (0.00565) (0.00542) (0.00490) (0.00331)

VIX_C 0.000727

0.00803 0.00371

(0.0103)

(0.0138) (0.00649)

VIX_EC

0.00217

-0.0186 0.0257**

(0.0177)

(0.0175) (0.0128)

VIX_P

-0.235*

-0.272

(0.139)

(0.200)

IssuedP_ma -0.0140 0.0203** -0.00741 -0.0205 -0.0170

(0.00915) (0.00915) (0.0138) (0.0183) (0.0218)

Issued_C -0.00139

-0.0101*** -0.00858***

(0.00342)

(0.00353) (0.00301)

Issued_EC

-0.00468

-0.00844 -0.00309

(0.0132)

(0.00758) (0.0101)

Issued_P

0.0145

0.00897

(0.0112)

(0.0126)

Debt -0.0252** 0.0107 -0.0333* -0.00427 -0.0133

(0.0126) (0.00802) (0.0170) (0.0120) (0.00996)

Debt_C 0.0170

-0.0144 -0.00505

(0.0128)

(0.0113) (0.00769)

Debt_EC

-0.0673**

-0.00731 -0.0388***

(0.0313)

(0.0139) (0.0115)

Debt_P

0.0423

0.00650

(0.0325)

(0.0169)

Debt_sq 0.000284*** -5.55e-05 0.000397*** 0.000190* 0.000242***

(6.90e-05) (5.23e-05) (8.92e-05) (0.000104) (7.32e-05)

DebtSQ_C -4.41e-05

0.000112 6.03e-05

(8.71e-05)

(8.04e-05) (6.34e-05)

DebtSQ_EC

0.000483***

-1.02e-05 0.000209***

(0.000179)

(8.92e-05) (7.91e-05)

DebtSQ_P

-0.000364*

-0.000170

(0.000193)

(0.000131)

Budget_ma -0.0782*** -0.0272 -0.0168 0.0453* 0.00751

(0.0132) (0.0337) (0.0179) (0.0238) (0.0143)

Budget_C 0.0698***

-0.0139 0.00494

(0.0133)

(0.0132) (0.0275)

Budget_EC

0.00751

-0.146*** -0.0141

(0.0741)

(0.0231) (0.0309)

Budget_P

0.00196

-0.0701

(0.0612)

(0.106)

IR

0.238***

-0.00988

0.101***

0.0208

-0.0312

(0.0836) (0.0251) (0.0264) (0.0510) (0.0328)

76

IR_C -0.216 0.393 0.0331

(0.223)

(0.404) (0.139)

IR_EC

0.257

0.789** 0.0126

(0.194)

(0.320) (0.215)

IR_P

5.293*

5.094*

(3.179)

(2.944)

GDP -0.00331 0.0613 0.00128 0.0449* 0.0559***

(0.0243) (0.0374) (0.0239) (0.0256) (0.0136)

GDP_C 0.0663

0.0332 -0.0286

(0.0611)

(0.0648) (0.0254)

GDP_EC

-0.0203

-0.0740*** -0.00977

(0.0357)

(0.0270) (0.0199)

GDP_P

0.0531

0.0721

(0.0969)

(0.0695)

HICP 0.0915 -0.0130 0.0455 0.0421 0.00799

(0.0630) (0.0414) (0.0385) (0.0538) (0.0205)

HICP_C -0.0555

-0.0594 0.0197

(0.0866)

(0.0460) (0.0341)

HICP_EC

0.163

0.133** 0.119

(0.103)

(0.0630) (0.0929)

HICP_P

-0.0138

0.0811

(0.176)

(0.264)

EuriborEonia -0.386 -0.123 -0.340*** 0.130 0.0283

(0.313) (0.0936) (0.129) (0.0887) (0.0355)

EuriborEonia_C 0.488

-0.204 0.0311

(0.534)

(0.204) (0.108)

EuriborEonia_EC

-0.132

-0.123 -0.418

(0.391)

(0.232) (0.425)

EuriborEonia_P

-15.48

-11.35

(14.49)

(14.70)

CAch -0.596* -0.262 -0.0610 0.252 0.0568

(0.347) (0.196) (0.0598) (0.163) (0.0504)

CAch_C 1.097*

0.0308 0.110

(0.607)

(0.179) (0.114)

CAch_EC

0.510

-0.549 0.121

(0.409)

(0.430) (0.102)

CAch_P

-0.184

-0.655

(0.267)

(0.415)

ULC_ma 0.0355*** 0.0134** 0.0148** 0.0507*** 0.00800

(0.0124) (0.00637) (0.00660) (0.0170) (0.00721)

ULC_ma_C -0.00322

-0.00378 0.00568

(0.0129)

(0.0165) (0.00949)

ULC_ma_EC

0.00166

0.00381 0.00480

(0.0105)

(0.00837) (0.00915)

ULC_ma_P

0.0241*

0.0341*

(0.0135)

(0.0181)

capital_assets -10.29 -6.714 2.654 7.218 1.161

(7.435) (4.085) (3.088) (4.867) (2.976)

capital_assets_C -1.120

-16.50**

-9.303

(5.594)

(7.456) (5.897)

77

capital_assets_EC

9.808

-16.76** -2.041

(9.323)

(8.482) (6.749)

capital_assets_P

-1.556

-1.260

(5.494)

(6.342)

AssetsGDP -0.0281 -0.00402 -0.00619 -0.0165 -0.00156

(0.0210) (0.0122) (0.0103) (0.0239) (0.00993)

AssetsGDP_C 0.0137

-0.0111 -0.000772

(0.0127)

(0.0114) (0.0107)

AssetsGDP_EC

0.0367

-0.0263 0.0158

(0.0287)

(0.0191) (0.0116)

AssetsGDP_P

-0.0813***

-0.0502

(0.0270)

(0.0364)

Fixed effects Controlled Controlled Controlled Controlled Controlled

Observations 546 546 546 546 546

R-squared 0.935 0.958 0.959 0.955 0.963

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Source: own model results obtained from Stata

78

Figure 14: Fitted values from the model accounting for potential structural breaks

compared to the actual government bond spreads

Source: own model results obtained from Stata

79

Figure 15: Potential investor overestimation of risk at the peak of the crisis

Source: own model results obtained from Stata