UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2011 – 2012

Impact of Greek exposure on the securities of European banks during the

Greek debt crisis

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de

Toegepaste Economische Wetenschappen: Handelsingenieur

Annelies Deleersnyder

onder leiding van

Prof. Dr. Rudi Vander Vennet en Glenn Schepens

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2011 – 2012

Impact of Greek exposure on the securities of European banks during the

Greek debt crisis

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de

Toegepaste Economische Wetenschappen: Handelsingenieur

Annelies Deleersnyder

onder leiding van

Prof. Dr. Rudi Vander Vennet en Glenn Schepens

- Permission

Undersigned declares that the content of this master thesis may be consulted and reproduced, when

referencing to it.

Annelies Deleersnyder

I

I Word in advance

In spring 2009, I started to look for a subject for my thesis. As the Greek crisis was a hot topic, I

suggested this to Professor dr. Rudi Vander Vennet who immediately proposed related research topics.

At that point in time, I thought the crisis would have finished by the time I would start my analysis

(end of 2011). However, nothing turned out to be less true. This crisis has become even more

fascinating than it looked then and it has been extremely interesting to follow it so closely as it

involved a lot of unexpected events and turned out to be a major topic continuously occupying the EU

top.

I would like to thank a couple of people who helped me during the process of writing this thesis. First

of all, I would like to thank Professor dr. Rudi Vander Vennet, which lectures fascinated me so much

that I chose “Finance” as my specialization. He guided me by providing me with relevant literature as

well as general directions on what to do. Next to that, also a lot of thanks to Glenn Schepens who was

always there to answer my questions and to help me with practical details. I am also very grateful to

the people of the research department of BNP Paribas as they taught me a lot during my internship in

January 2010 and as they helped me collecting part of the data. On top of that, I owe my parents, Hilde

De Brouwer and Jean-Luc Deleersnyder, a lot of thanks, not only because they’ve corrected my thesis,

but also because they have been a huge support and guide during my studies and beyond. To end with,

I would like to thank my family and friends, especially Jens Ponnet and Piet Peene, as they supported

me in many ways, even without knowing it sometimes.

II

II Table of Content

I Word in advance .................................................................................................................................. I

II Table of content ................................................................................................................................ II

III Used abbreviations......................................................................................................................... IV

IV List of tables and exhibits ............................................................................................................... V

1. Introduction ................................................................................................................................... 1

2. Related literature ........................................................................................................................... 6

2.1 The Greek crisis....................................................................................................................... 6

2.1.1 Likely causes of the Greek debt-crisis ............................................................................. 6

2.1.2 The chronology of the crisis ............................................................................................ 8

2.2 Reaction of policy holders and markets ................................................................................ 15

2.2.1 Reaction of the policy holders: the stress tests .............................................................. 15

2.2.2 Reaction of markets on the Greek debt crisis ................................................................ 16

2.3 The influence of the lenders’ exposure ................................................................................. 19

2.4 Factors influencing the risk profile of a bank ........................................................................ 21

3. Data ............................................................................................................................................... 27

3.1 Bank stock returns ................................................................................................................. 28

3.2 Bank CDS returns .................................................................................................................. 30

3.3 Control variables and exposure data ...................................................................................... 32

3.2.1 Greek exposure ratio (GEXPR) ..................................................................................... 32

3.2.2 Capital ratio ................................................................................................................... 32

3.2.3 Size ................................................................................................................................ 33

3.2.4 Funding ratio ................................................................................................................. 33

3.2.5 Asset structure ratio ....................................................................................................... 33

3.2.6 Country dummy ............................................................................................................. 34

4. Methodology................................................................................................................................. 36

4.1 Summary of hypotheses ........................................................................................................ 36

4.2 STEP 1: Market model and average abnormal returns .......................................................... 38

4.2.1 Not-time split dummies ................................................................................................. 38

4.2.2 Time split dummies before and after the stress test results of 2010 .............................. 40

4.3 STEP 2: Regression of the average abnormal returns ........................................................... 42

5. Results .......................................................................................................................................... 47

5.1 Stock return ........................................................................................................................... 47

5.1.1 STEP 1: market model and average abnormal returns .................................................. 47

5.1.2 STEP 2: regression of the AARs ................................................................................... 52

5.1.3 Conclusion stock return ................................................................................................. 66

5.2 CDS ....................................................................................................................................... 67

III

5.2.1 STEP 1: market model and average abnormal return .................................................... 67

5.2.2 STEP 2: regression of the AARs ................................................................................... 68

5.2.3 Conclusion ..................................................................................................................... 68

6. Limitations and directions for further research ....................................................................... 69

7. Conclusion .................................................................................................................................... 72

V Sources .............................................................................................................................................. IX

VI List of Appendices

Appendix 1: Input control variables and Greek exposure stock return sample............. Appendix 1

Appendix 2: AARs from stock and CDS return resulting from step 1

Appendix 2.1 AARs for stock return ................................................................................ Appendix 2.1

AARs for the not-time split dummies for stocks .................................................. Appendix 2.1

AARs for the time split dummies for stocks ........................................................ Appendix 2.1

Appendix 2.2 AARs for CDS return ................................................................................ Appendix 2.2

Appendix 3: Results regressions second step stock return

Appendix 3.1 Regressions based on all AARs (significant and insignificant) ................. Appendix 3.1

Appendix 3.1.1 General regressions for (51) AARs from not-time split dummies ... Appendix 3.1

Appendix 3.1.2 General regressions for (102) AARs from time split dummies ....... Appendix 3.1

Appendix 3.1.3 Regressions for AARs from events BEFORE July 2010 ................ Appendix 3.1

Appendix 3.1.4 Regressions for AARs from events AFTER July 2010 ................... Appendix 3.1

Appendix 3.2 Regressions based on significant AARs only ....................................... Appendix 3.2

Appendix 3.2.1 General regressions for (51) AARs from not-time split dummies (only sign

AARs) ......................................................................................................... Appendix 3.2

Appendix 3.2.2 General regressions for (102) AARs from time split dummies (only sign AARs)

......................................................................................................... Appendix 3.2

Appendix 4: Dutch summary/ Nederlandse samenvatting ............................................... Appendix 4

IV

III Used abbreviations

AAR: Average Abnormal Return

AR: Average Return

BIS: Bank for International Settlements

CDS: Credit Default Swap

CEBS: Committee of European Banking Supervisors

CT1R: Core Tier 1 capital Ratio

Dep/TA: Deposits/Total Assets

EBA: European Bank Authority

EMU: European Monetary Union

GDP: Gross Domestic Product

GEXPR: Greek Exposure Ratio

LDC: Less Developed Countries

PIIGS: Portugal, Ireland, Italy, Greece and Spain

PIIS: Portugal, Ireland, Italy and Spain

TA: Total Assets

TBTF: Too-Big-To-Fail

T1CR: Tier 1 Capital Ratio

V

IV List of tables and exhibits o Tables

Table 1: List of events with expected influence (positive/negative) and strength classification .......... 14

Table 2: Summary statistics of bank stock return ................................................................................. 29

Table 3: Summary statistics of bank CDS return .................................................................................. 31

Table 4: Summary statistics of control variables and Greek exposure data .......................................... 34

Table 5: Correlation matrix of control variables and Greek exposure .................................................. 35

Table 6: Summary data sources ............................................................................................................. 35

Table 7: Different scenarios to be teste ................................................................................................. 41

Table 8: Summary statistics of the not-time split AARs of the stock returns ....................................... 48

Table 9: Summary statistics of the time split AARs of the stock returns .............................................. 51

Table 10: Summary table with average (and proportion of significant) coefficients of equations 6 to 10

based on the AARs from the not-time split sample ............................................................................... 56

Table 11: Regression coefficients and significance from equation 9 for all 12 scenarios, based on the

not-time split sample of 51 AARs. ........................................................................................................ 59

Table 12: Regression coefficients and significance from equation 10 for the GEXPR for the different

levels of T1CR, based on the not-time split sample of 51 AARs. ......................................................... 60

Table 13: Regression coefficients and significance from equation 9 for the strong scenarios, based on

the time split sample of 102 AARs. ...................................................................................................... 63

Table 14: Regression coefficients and significance from equation 10 for the strong scenarios, based on

the time split sample of 102 AARs. ...................................................................................................... 64

Table 15: Summary statistics of the AARs of the CDS returns ............................................................ 68

Table 16: Summary of hypotheses and findings ................................................................................... 74

VI

o Exhibits

Exhibit 1: Time line of events ............................................................................................................... 13

Exhibit 2: Greek-German sovereign bond spread ................................................................................. 17

Exhibit 3: Sovereign CDS prices of European countries (2010-2011) ................................................ 18

Exhibit 4: Greek sovereign CDS spread and bond spread..................................................................... 18

Exhibit 5 Visual summary of methodology........................................................................................... 46

Exhibit 6: AARs of not-time split sample and their significance .......................................................... 49

Exhibit 7: Averages of the AARs for the different scenarios for several categories of banks depending

on their origin ........................................................................................................................................ 50

Exhibit 8: average AARs "before" versus "after"................................................................................. 51

Exhibit 9: coefficient GEXPR equation 9 ............................................................................................. 60

Exhibit 10: coefficient GEXPR equation 10 ......................................................................................... 60

Exhibit 11: GEXPR coefficient before versus after equation 9............................................................. 64

Exhibit 12: GEXPR coefficient before versus after equation 10........................................................... 64

1

1. Introduction

The Greek debt crisis is currently a hot topic and it has been one for the last two years. There seems to

be no simple solution and what has previously been described as “impossible” by several politicians,

became reality only a few months later. While writing this (January 2012), Greece (and the whole

EMU) is still facing major problems and everyone is watching it closely to see what will happen. This

crisis should probably not be called the “Greek crisis” since several other countries suffer from similar

problems (which is often attributed to the contagion effect). However, since this paper will focus

primarily on Greece, the term “Greek crisis” will be used frequently.

One could argue, as was the initial statement of many politicians, that most banks are too-big-to-fail

(TBTF). This means that it is impossible that a large institution would default or go bankrupt because

the consequences would be too dramatic. This has been experienced during the sub-prime crisis of

2008 when governments primarily subsidized large and complex financial institutions (Panetta, Faeh

Grande, Ho, King, Levy, Signoretti, Taboga and Aghini, 2009). Brewer and Jagtiani (2009) argue that

banks are willing to pay a substantial premium to pass the (subjective) TBTF threshold of $100 billion

in assets, a threshold which Greece with its $469.8 billion gross debt (in 2010) has definitely passed.

The main reason for being TBTF is contagion: once a large institution would go bankrupt, plenty of

other companies, banks and investors would lose a substantial amount of money as well, possibly

getting them in trouble or even into bankruptcy. This is a feared domino-effect caused by a flight to

quality. This reasoning can also be applied to countries as their default could have devastating

consequences both for the investors as well as for the E(M)U. Even though only a year ago many

politicians have claimed that Greece is TBTF, a restructuring has become, at the time of writing,

reality as the European supervisors have run out of expedients and the issues remain unsolved.

During and after the crisis of 2008, new initiatives were taken to try to restore the confidence of

investors. One of these was the EU stress tests of banks, conducted by the European Bank Authority

(or by the ECBS in 2010). One of the main purposes of these tests was to restore the confidence by

creating transparency. While the first two tests (published in September 2009 and in June 2010) are

generally seen as “not being strict enough” (Steinhauser, 2011), the stress test of July 2011 had a little

more credibility. Together with the results, data about the sovereign debt exposure of each bank to

each country were published (in 2010 and in 2011). Until the publication of the stress test results of

2010, only information about the exposure of the whole country’s banking sector (and not of the

individual banks) to other countries was publicly available via the Bank for International Settlement

(BIS). So from July 2010 on, investors should have been able to make better decisions as they are

more informed. Therefore, this research aims to prove that the impact of the Greek crisis on the

European banking sector has increased since this publication.

2

The Greek sovereign debt crisis has many causes, but also many consequences. One of these

consequences affects banks. If Greece were to default, this can cause some banks, holding a lot of

Greek sovereign bonds, to get in trouble or even to file for bankruptcy. It seems logical that the

securities of banks which hold a lot of Greek sovereign bonds, will react heavily on the day of an

important Greek announcement (further called “event”). On the other hand, it also seems self-evident

that the valuation of banks will be less or not affected if they hold (almost) no Greek sovereign bonds.

This heavy reaction causes an abnormal return (AR). When considering multiple events, one should

look at the average of these abnormal reactions, further called average abnormal returns (AARs).

Therefore, the main hypothesis this thesis seeks to verify is that the investors take into account the

Greek exposure when valuing the bank, i.e. the size of the AR of a bank on the day of a Greek event is

positively correlated to the size of this bank’s sovereign exposure towards Greece. A second

hypothesis is that this relationship will be more reliable after the stress test, as these provided the

investors with useful information about the size of this exposure of each bank. Next to these, there will

be several smaller hypotheses, which emerged during this research after incorporating other external

influences. One of these hypotheses is that banks from some countries are more sensitive to Greek

events (or the Greek market in general) than banks from other countries, even independent of the size

of their exposure. It is possible that banks from peripheral countries will react more heavily because

the PIIGS (Portugal, Ireland, Italy, Greece and Spain) are quite interrelated and banks are in general

heavily exposed towards their home country (Bolton & Jeanne, 2011) (Blundell-Wignall & Slovik,

2010). On top of that, the influence of the Greek exposure might be different for Greek banks as they

can experience an event in several ways, independent of their portfolio of sovereign bonds and they

are a lot more exposed to the Greek banks. Another interesting hypothesis is that the link between the

banks’ average abnormal returns (AARs) and its Greek exposure is positively influenced by the

proportion of capital it holds. So, low capitalized banks are expected to react more fiercely per unit of

Greek exposure they hold. Next to these, there are some other hypotheses to be tested which concern

the influence of a bank’s strategic choices on their risk profile.

In order to assess these hypotheses, I have used the event study methodology where dummies are

chosen to represent the events following Binder (1998). In the first step of the event study, the AARs

have been calculated per bank and this for several scenarios based on different combinations of

assumptions like event window, time period of the sample and choice of events. This is done in order

to check whether the results are independent of these assumptions. It is expected that, on average, the

size of the average abnormal return (AAR) of a bank on the day of a Greek event can be partially

explained by the size of its Greek sovereign debt exposure. However, this requires that the investors

know the portfolio composition of each bank, which was, until the second stress test, not publicly

known. Therefore, it seems useful to conduct another event study using two separate dummy series for

3

these two periods: the first period before the stress test and the second one after the publication of the

results in July 2010 (this is done by making one dummy series for the events before and one for the

events after). If the stress test disclosed new information to investors, the reaction of the market should

be different and more correct afterwards. So for this study, it is expected that in the first timeframe, the

relation between the AARs and the exposure is less pronounced than after the disclosure. In order to

isolate the effect of the exposure in the AARs, a cross-section will be applied with multiple control

variables (like a capital variable, a funding variable, an asset structure variable and country dummies)

which might also influence the size of the abnormal reaction on the day of an event.

This study is applied to a sample of EU banks based on the list of 91 banks included in the EU stress

test of 2010 (as their exposures are available and those of other banks in general are not). Both the

reaction of the stock prices and of credit default swap (CDS) prices have been tested. Due to non-

publicly available or illiquid security data, the sample of banks for the stock returns shrank to 51 while

the CDS study only includes 38 banks. Unfortunately, the first step of the methodology resulted in

only a small number of AARs for the CDSs, which is why the second research step would not return

reliable results. Hence, this paper has no further results for the CDS sample.

Fortunately, the first step of the methodology on the stock returns was successful as it resulted in a

sufficient number of significant AARs. On average, an abnormal reaction of one percent was

measured (independent of the underlying assumptions like event window or event selection).

However, this size was very dependent on the nationality of the bank. Banks from the PIIGS reacted

more than twice (Greek banks even four times) as strong as the non-PIIGs banks. In the second step of

the analysis, it was investigated whether this difference in reaction was due to the fact that these banks

had a portfolio with more Greek sovereign bonds, a higher risk profile or whether this difference was

due to domestic forces.

The second step of the analysis rendered some interesting, though mainly expected, results. The main

hypothesis stating that there is a positive correlation between the size of the exposure and the AARs, is

accepted. However, the size of the exposure only explains about 13% of the size of the AARs. In case

of the strong events only, the average proportion of the Greek exposure in the AARs rises to 19%

which is why the further results are based on the AARs coming from the event dummy with the 9

strong events. On top of it, this positive correlation depends on several factors. First, the capital ratio

influences this link. Banks with a lower capital are more vulnerable and therefore, their investors will

react more fiercely, resulting in a larger Greek exposure coefficient. Next, the nationality also has an

impact. Investors punished the non-Greek banks about ten times more per unit of Greek exposure than

Greek banks. This can be explained by two factors. First, the Greek banks have on average 40 times

more exposure towards Greece than the other banks included in the sample. So, as the reaction per unit

4

of exposure is ten times smaller, this results in an abnormal reaction caused by the exposure size

which is, on average, still four times bigger for Greek banks than for non-Greek banks. However, as

the AARs of the Greek banks were on average as well four times bigger than for non-Greek banks,

this still results in a similar proportion of 13.5% of the AAR explained by the Greek exposure. Second,

the Greek banks experienced large economic and domestic forces, independent of their bond portfolio.

This domestic factor explains 75% of the AARs of the Greek banks. So the domestic forces

themselves are the main factors driving the average of the AARs for Greek banks four times higher

than that of non-Greek banks. Also for banks from the PIIS (Portugal, Ireland, Italy and Spain), there

is this large “domestic” factor, possibly caused by the fear for contagion, driving the abnormal

reaction twice as high as for the non-PIIGS countries. A last interesting finding concerns the split

between the impact before and after the publication of the stress test (and the sovereign exposures of

the banks) in July 2010. Before publication, the AARs were already partly driven by the exposure size,

implying that the investors estimated the exposures quite well. After publication, exposure coefficient

was 2.4 times bigger than before resulting in the acceptance of the hypothesis that the link after July

2010 has grown stronger. Last, there were some hypotheses based on the control variables, of which

only the size coefficient had a statistically significant positive effect on the abnormal reaction.

This research is relevant for three main reasons. (1) First of all, it adds to the existing literature on the

link between country exposure and bank securities. Most of this current research dates from the ‘80s

and is focused on the Mexican default (as only or main event) in 1982. This paper, on the other hand,

is (one of) the first with a European country in crisis as subject, using the events leading up to a

possible default instead of the default event itself. (2) Second, it investigates the market efficiency and

the value of the publication of the Greek exposure by the EBA. If no change in AARs can be attributed

to a change in reaction of the Greek exposure, this can imply that the market had found other ways to

estimate the sovereign exposure of the banks, making the publication of it quite irrelevant. (3) A last

factor which makes this research valuable is that the outcome could enable banks (and their investors)

to better understand the movement of their securities. They should be able to define which part of the

AARs is caused by their Greek exposure, by possible contagion effects, by external domestic effects

(for Greek countries), by their risk profile (like capital ratio, funding ratio and asset structure ratio),etc.

In short, it looks as if this research should have practical and theoretical relevance in several ways.

Even though this research includes a lot of robustness checks, it has some limitations which need to be

taken into account when interpreting and generalizing the results. I have identified five of them. (1) A

first one is that the sample only includes listed EU banks which might give a biased view because they

tend to be bigger banks which sometimes behave differently as they are often put under pressure by

their shareholders to create short term value. (2) Second, the process of selecting the events is quite

subjective. On top of that, the results were slightly dependent on the choice of events. As a result, it is

5

possible that an event study based on other events might give different results (and maybe more

significant results for the CDS returns). (3) Another restriction is that only a limited number of banks

is included resulting in a small amount of significant AARs for the second step. Next, much of the

control variable data come from a snapshot at the end of the year, while it would be more accurate to

have a continuous measure. (4) As this research only focuses on Greece and there is not a lot of

complementary literature available, caution is required when generalizing the results to other countries

as debtors. (5) A last important constraint is the fact that this research cannot ensure that a change after

the stress test is caused by the publication of the Greek exposure only. After the publication, the threat

of a default has grown which might also have caused investors to pay more attention to the exposure

ratio of each bank. This can be an interesting topic for further research.

This paper is structured as follows: section 2 provides an overview of relevant literature related to the

Greek crisis, literature concerning the reaction of markets and banks related to Greece as well as

literature on the effect of sovereign exposure on bank stock movements. It also includes some

literature on control variables to be considered which helps building the hypotheses to be tested.

Section 3 describes the data while section 4 explains the general methodology used for this event

study. Next, empirical results are given in section 5, as well as a discussion about the robustness of

these results. Finally, some restrictions about this study and recommendations for further research are

presented in section 6 and a conclusion is given in section 7.

6

2. Related literature

This section will briefly review some literature related to the Greek crisis as well as the relationship

between exposure and the lenders’ valuation. First, there is some literature on the Greek debt

describing the causes as well as the main events which will be used further in the event study. The

second part relates to the reaction of policy holders to the financial crisis of 2008 (e.g. the stress tests)

and that of the markets on the Greek financial crisis (e.g. the widening of the bond spread and the CDS

spread of the Greek sovereign bonds). The third part reviews some research on the influence of a

bank’s exposure on its valuation as judged by its investors. Most of these studies are based on data

related to the LDC (less-developed-country) crisis and in particular the Mexican default in 1982.

Finally, I will give some literature related to the potential factors that may influence the risk profile of

a bank and therefore also, its abnormal returns on Greek event dates. This literature will help building

the reasoning behind the hypotheses of this paper.

2.1 The Greek crisis

2.1.1 Likely causes of the Greek debt-crisis

The Greek crisis timelines of Reuters and the Financial Times both start at the end of 2009 (Culter,

2010) (Cadman & Minto, 2011). However, it is clear that there has been a long lead to the crisis.

Several authors have been trying to explain its causes. Yet, it is impossible to point at one determining

cause in particular since there are as many explanations as authors. I limit myself to six main causes,

identified in literature.

(1) Diversification. First of all, Bolton and Jeanne (2011) argue that due to diversification, people

create benefits at first (by spreading the risk), but they become increasingly interconnected, leading to

contagion effects afterwards. Due to this diversification, countries render a public benefit to their

investors by closely monitoring their debt position. However, since each country on its own does not

internalize the cost of its financial fragility, countries are reluctant to obtain only big amounts of safe

debt and prefer instead a large proportion of risky debt (Bolton & Jeanne, 2011).

(2) Absence of action. Secondly, many have criticized the ECB and eurozone countries for waiting

too long to interfere and for not acting convincingly enough. At the beginning of the crisis, investors

believed the ECB would not give a collateral after the ratings were being lowered, causing panic and

even less confidence. Kouretas and Vlamis (2010) believe this is due to the lack of political union in

the EU. Even before the crisis, EU leaders have systematically failed to summon Greece for missing

out on the EU’s public deficit targets. If Greece would have been more incentivized to followed these

7

targets (and the EU would have obliged Greece to do so), their public deficit would have been a lot

smaller.

(3) Rating agencies. Another potential cause related to the sub-prime crisis are the rating agencies.

They have failed to accurately estimate the risk before the banking crisis of 2008, by systematically

being too loose. From the crisis on, they have switched behavior to judging very strictly and

negatively (De Grauwe, 2010). Kouretas and Vlamis (2010) argue they have failed to predict the

financial crisis and therefore, they were eagerly looking to discover possible sovereign debt crises

resulting in a vicious circle of lost trust towards Greece and other peripheral countries. Hence, it looks

as if agencies reinforce the existing market sentiment, i.e. in good times they upgrade more than they

downgrade which adds to the overall market feeling that everything is going fine. Research of

Kaminsky and Schmukler (2002) has provided evidence of this presumption. They proved that rating

agencies add instability to the market by acting pro-cyclically.

(4) The sub-prime crisis. Next to that, Gerlach, Schulz and Wolff (2011) argue that the intervention

of the government in the sub-prime crisis of 2008 is one of the main causes for the decreasing

confidence of investors in Greece. In the crisis, several banks got in trouble and many local

governments interfered to reduce the accumulated private debt thereby stabilizing the domestic

banking system. This happened with tax money, increasing the country’s deficit (which is typically

already higher in crises due to lower income and more unemployment). Hence, many countries within

the eurozone have suffered from an increase in their gross debt, although not equal in size. Greece

suffered the second largest increase in gross debt-to-GDP ratio (38.2 percent) (Kouretas & Vlamis,

2010). This huge deficit and growing outstanding debt influenced the investors’ perception of default

risk negatively, pushing up the sovereign bond spread (Gerlach et al., 2011). Although the previous

link might be caused by a third intermediate factor influencing both. Attinasi, Checherita and Nickel

(2009) discovered that it is not the size of the commitment that significantly influenced the sovereign

bond spreads, but rather the credibility of its commitment. Regardless of the size or credibility

however, research on the crises of the past two centuries from Reinhart and Rogoff (2010) confirms

that it is common that a sovereign debt crisis follows or coincides with a banking crisis partly because

of the accumulation of the public debt during the banking crises.

(5) Inflation. On top of all the previous arguments, the origin of the Greek crisis may also date from

before the sub-prime crisis. Arghyrou and Tsoukalas (2010) argue Greece has had issues ever since the

start of the EMU (2001). Greece systematically suffered from an unsustainable fiscal policy as its

inflation surpassed the EMU average, resulting in a change in their purchasing power parity, a loss in

competitiveness and increasing deficits (Aeghyrou & Chortareas, 2008). Moreover, the EMU lost

credibility due to a lack of commitment and no fiscal guarantees (Arghyrou & Tsoukalas, 2010).

8

(6) Greece itself. To end with, the reasons are not all to be found externally. Greece itself has been

known for having a cumbersome government sector and a weak political system (Kouretas & Vlamis,

2010). On top of that, it systematically had a large gross debt-to-GDP ratio, not falling below 97.4

percent since 2000, and for the past eight years, it has failed to push its public deficit below 5 percent

(Eurostat). This accumulated public debt and large current deficit are the consequences of a country

living beyond its means. On top of this, Greece cheated on its euro-entry exam in 2000 to enter the

eurozone and the European leaders have failed to punish Greece adequately for these lies (Wienberg,

2011). If Greece would have acted more responsible towards its government budgets, a large part of

the existing threat of default would not have been there.

2.1.2 The chronology of the crisis

Now that some likely causes of the crisis are explained, I move on to the time line, to explain the most

important things (further called “events”) that happened during the time frame of this research (from

December 2009 until October 2011). These events are chosen based on the interactive timeline of the

Financial Times (Cadman & Minto, 2011), which, at this moment (March 2012), still holds 51

important events. Since I will use several event windows of which some include multiple subsequent

days, I have deleted the contradicting (positive and negative) events that took place on two

consecutive days. These events probably influenced each other a lot such that it would not be possible

to filter out the exact effect of one of them. On top of that, some of the events which did not seem

powerful enough or of which it was not clear whether their influence would be positive or negative,

have also been deleted. Next, I also added four other events which, even though they were not

included in the timeline of the Financial Times, caused a lot of media attention and therefore might

have had a significant impact on the banks’ stock or CDS return. All of this resulted in a total of 29

events.

In this research, there is a distinction between “strong” and “normal” events. Strong events are those

which I expected to have a much bigger impact. These are the most important events, which received a

lot of media attention1. This is relevant as for an event study, one needs to make the tradeoff between a

few very big events (with a bigger average impact and therefore a larger coefficient, but usually also a

bigger variance) and more, under which also smaller events (resulting in a lower average abnormal

return, typically with less variance). Exhibit 1 (infra, p.13) displays the events on a time line. I have

identified nine strong events (further referred to as “the strong events”) which can be found below the

timeline (displayed on exhibit 1) and twenty other “normal” events above it. The events expected to

have mainly a positive influence are depicted in black while the negative events are in red. Table 1

1 I need to admit this breakdown has been performed on a subjective basis, but I believe most will agree that the

events chosen are the most important ones.

9

(infra, p.14) clearly summarizes the information that can be drawn from exhibit 1 by listing the dates

of the events with the indication of whether these were negative or positive events and whether I

classified them as strong or not.

Here follows a very brief chronological description of what happened on the day of each of the 29

identified events:

1. On December 8 2009, Fitch downgraded the rating of Greek debt, for the first time in ten

years below A, to the lowest level in the eurozone (BBB+). After the downgrade it kept a

negative outlook. The other rating agencies warned they would do the same (Oakly & Hope,

2009, 8 December). As many time lines consider this downgrade as the start of the crisis

(because it is the first clear sign to the public), this is the first strong event.

2. On January 28 2010, the markets were upset because China did not want to raise its exposure

to Greece and investors asked the highest premium since the start of the EMU for Greek bonds

(Cadman & Minto, 2011).

3. On April 12, the eurozone ministers of finance agreed to provide Greece with a three-year

fixed loan of €30bn at an interest rate of 5 percent. This was lower than the 7.45 percent

demanded by the investors in the market at that time (Chaffin, Pignal & Hope, 2010, 12

April).

4. On April 20, the publication of weak economic conditions, such as the highest unemployment

level in six years, pushed up the Greek interest rates (Dennis, 2010, 20 April).

5. A week later, S&P downgraded the Greek bond even more. This time it received the junk

status (Cadman & Minto, 2011). As they were the first to assign Greece a junk rating, this

event is considered a strong one.

6. On April 30, reports showed that there is an agreement with Greece about a €24bn austerity

package of which one of the factors is a three-year wage freeze for the public sector workers.

(Hope, 2010, 30 April).

7. On May 5, fears for a debt restructuring or default kept increasing as violent protest against

the tough austerity measures in Greece rose again (Cadman & Minto, 2011).

8. Only a few days later, on May 10, the markets in general recovered thanks to the €750bn aid

package provided by the EU and IMF. This package included loan guarantees and would be

used to buy European sovereign bonds (Cadman & Minto, 2011). “Yields on two-year Greek

government bonds fell a record 1,269 basis points to 5.48 percent as many investors dismissed

previous fears of a debt restructuring.” (Budden & Peel, 2010, 10 May). Even though

10

everyone was expecting some kind of agreement, it was big news and therefore, this is another

strong event.

9. Moody’s downgraded the Greek rating on June 14, just like S&P the month before, to a junk

status (Cadman & Minto, 2011). Unlike one would expect, this provoked a big market

reaction because it went against the intuition that Greece would be safer thanks to the loan

package announced in May. This unexpected downgrade was therefore considered to be a

strong negative event.

10. On June 24, worries about the ECB’s long-term financing operations and sovereign debt fears

pushed up the spread (Cadman & Minto, 2011).

11. On August 12, another series of bad economic figures on Greece was made public.

Unemployment kept on rising mainly due to layoffs by the smaller companies under the

pressure of the crisis (Hope, 2011, 12 Augustus). On top of that (and probably because of

that), the economy had shrunk during the preceding months (Hope, 2010, 12 Augustus).

12. On September 1, a report of the IMF was published saying that Greece’s default was seen as

“unnecessary, undesirable and unlikely”. The IMF authors further said that as long as Greece

cuts it debt to zero, the chance of a default was extremely small (Harding, 2011, 1 September).

13. On October 4, Greece came up with an ambitious draft budget aiming to get the budget deficit

to 7,0 percent, which is below the 7,6 percent benchmark set by the European Commission,

ECB and IMF. The goal of this plan was to return to the bond market during 2011

(Kontogiannis, 2010, 4 October).

14. On November 5, there were renewed concerns about the Greek political stability, mainly due

to upcoming elections (Cadman & Minto, 2011).

15. The first event in 2011 took place on January 14, when Fitch followed the other rating

agencies by downgrading the Greek debt to junk (Cadman & Minto, 2011).

16. On March 14, the effective capacity of the European emergency fund was raised to €440bn

(coming from €250bn). Greece was rewarded for its hard efforts made in the previous months

since its interest rate went down by one percent and the duration of the loans was extended

from 3,5 years to 7,5 years (De Greef, 2011, 14 March).

17. On March 29, S&P downgraded Greece’s rating once more by two notches to BB- (Oakley,

Wise & Hope, 2011, 29 March)

18. On April 20, a wave of new rumors about debt restructuring started. Some of them even said

that Greece was about to default during that weekend (Cotterill, 2011, 20 April).

19. On April 23, data from the European commission showed that the Greek budget deficit of

2009 was 13.6%, almost one percent higher than the previous prediction of the Greek

11

government. This triggered the perception that Greece would very soon be forced to activate

the aid package (Garnham, 2011, 23 April).

20. On May 24, the Greek government announced it would sell parts of its state-owned companies

in an attempt to raise revenues and hence, to cut the budget deficit (Cadman & Minto, 2011).

21. On June 9, new bad economic figures in Greece dashed the hope that an export-driven

approach was the way out of this crisis (Cadman & Minto, 2011).

22. On June 16, a new deal was made between the EU and the IMF under the condition that

Greece would agree with another wave of new tough austerity measures (Cadman & Minto,

2011). A day later, the Greek prime minister announced he would replace his finance minister

since he had become the symbol of the tough austerity measures and provoked a lot of anger

from the Greek public (Hope, 2011, 17 June). This two-day event gave a strong signal to the

market and is therefore classified as a strong positive event.

23. On July 21, European leaders agreed on another Greek bail-out of €109bn (Spiegel, Peel,

Jenkins & Milne, 2011, 21 July). Furthermore, they also committed to support Greece until it

would be capable of returning to the financial markets on its own (Spiegel et al., 2011, 21

July). This very strong commitment together with its significant support gave a strong signal

to the market which is why this event is assigned to the strong ones.

24. Only 4 days later, Moody’s said this support would weaken the credit rating of Italy and Spain

and would result in a Greek default (Milne, 2011, 25 April). As a consequence, Moody’s

downgraded Greece by another three notches meaning they saw Greece as “a country that is

very poor or in default” (Milne, 2011, 25 April). This is a strong negative event since, until

then, a default was still excluded (by the politicians) in every possible way.

25. Around September 12, the media attention around Greece leaped up again as a Greek default

was no longer considered a taboo. A default had only just been avoided, but many expected

that sooner or later, a Greek default would become inevitable. In general the banks were the

main victims from these rumors. As a result, their stock returns took a deep dive (De Tijd,

2011, 12 September).

26. On October 3, it became public that Greece would miss its budget benchmarks. This could

have devastating consequences as attaining these benchmarks was a prerequisite to get a bail-

out payment of €8bn (Spiegel, 2011, 3 October).

27. On October 19, the Greek parliament approved its latest package of austerity measures at first

sight without demanding any adjustments (Cadman & Minto, 2011).

28. On October 27, the EU agreed on a deal that would force private investors to face a 50 percent

cut on the face value of their bonds (Spiegel, Pignal & Barker, 2011, 27 October). In

12

exchange, the EU and the IMF committed to another aid package of €130bn (Spiegel, Pignal

& Barker, 2011,27 October). This was the first time private investors were involved in the

deal, turning this into a strong event.

29. The last event of this research took place on October 31, when the Greek prime minister

shocked the world by announcing he would hold a referendum for the Greek public to decide

whether they should accept this second bail-out deal made a few days earlier (Hope, Spiegel &

Demos, 2011, 31 October). Since this announcement came totally out of the blue, the whole

world was stunned and as a result, it should be classified as a strong negative event.

The time frame of this study ends at the end of October since at the beginning of November, I started

gathering the data to do my analysis. Of course, this does not mean that the Greek crisis stopped at that

point in time.

13

Exhibit 1: Time line of events (below the graph = strong event, red = negative event / black = positive event) (source: Financial Times, author’s selection,

interpretation and draft)

14

Date pos neg strong

event?

1 8/12/2009 x x

2 28/01/2010 x

3 12/04/2010 x

4 20/04/2010 x

5 27/04/2010 x x

6 30/04/2010 x

7 5/05/2010 x

8 10/05/2010 x x

9 14/06/2010 x x

10 24/06/2010 x

11 12/08/2010 x

12 1/09/2010 x

13 4/10/2010 x

14 5/11/2010 x

15 14/01/2011 x

16 14/03/2011 x

17 29/03/2011 x

18 20/04/2011 x

19 23/04/2011 x

20 24/05/2011 x

21 9/06/2011 x

22 16-17/06/2011 x x

23 21/07/2011 x x

24 25/07/2011 x x

25 12/09/2011 x

26 3/10/2011 x

27 19/10/2011 x

28 27/10/2011 x x

29 31/10/2011 x x

Total number of

events in this

category

11 18 9

Table 1: List of events with expected influence (positive/negative) and strength classification (source:

Financial Times, author’s selection and interpretation)

15

2.2 Reaction of policy holders and markets

In this part, I’ll start by briefly describing what the stress tests are, where they come from, as well as

their goals and credibility. These stress tests are particularly important for this research as they

provided an essential piece of input, i.e. the Greek sovereign debt exposure of the participating banks.

Without this information this research would have been less accurate as proxies would have been used.

In the second part of the section, I will briefly describe the mechanisms through which the market’s

reaction on an event can be observed.

2.2.1 Reaction of the policy holders: the stress tests

Many have claimed that Basel II substantially contributed to the financial crisis of 2008. By

encouraging banks to hold risk-free assets, it triggered the securitization race as a means to turn a risky

portfolio of assets in an apparently risk-free one (N.M., 2010, 13 September). It were these apparently

risk-free assets that caused a bubble which burst in 2008. After the crisis showed the deficiencies in

the Basel II agreement, the Basel Committee of Banking Supervisors agreed upon a new agreement:

Basel III. In general, Basel III aims to further strengthen the three pillars Basel II is based on. Part of

this, is to get more supervisory discretion to ensure that banks do not operate in their buffer range

(close to the capital limit) during normal times (Basel Committee on Banking Supervision, 2010). On

top of it, “The (Basel) Committee (on Banking Supervision) also required that banks conduct stress

tests that consider the downward migration of their credit portfolios in a recession” (Basel Committee

on Banking Supervision, 2010, p.5). The European Bank Authority (EBA), founded at the end of

2010, is one of the institutions which helps implementing these Basel III requirements. It “acts as a

hub and spoke network of EU and national bodies safeguarding public values such as the stability of

the financial system, the transparency of markets and financial products and the protection of

depositors and investors” (EBA, 2011c). It conducts yearly stress tests in order to temper the market

by restoring the investors’ confidence while in the mean time identifying those banks that have not

enough capital. “The aim of such tests is to assess the resilience of financial institutions to adverse

market developments, as well as to contribute to the overall assessment of systemic risk in the EU

financial system.” (EBA, 2011b)

These stress tests are conducted by modeling two “what-if” scenarios: a benchmark scenario assuming

a mild recovery after the crisis of 2008, as well as an adverse scenario with a double- dip recession due

to a sovereign risk shock (CEBS, 2010). A bank passed the test if it maintained a Core (in case of the

2011 tests) Tier 1 capital ratio above the benchmark in both scenarios. If the bank failed, it had to

propose a plan to solve the weaknesses revealed by the tests (CEBS, 2010).

Three EU stress tests have been conducted so far. (1) The first one was published at the end of 2009 in

an attempt to calm down the markets by giving them more confidence in the banking system.

16

However, these tests did not disclose a lot of information and were based on only 22 banks. Hence, it

was not a very successful attempt and it received almost no credibility. As a result, the 2009 tests are

not very relevant for this research. (2) On July 23, 2010, the Committee of European Banking

Supervisors (CEBS) published the inputs as well as the results of the second stress test2. Unlike the

first edition, these tests disclosed more important information, like the sovereign exposure of each

bank to each country (Kirkegaard, 2010, July 28) which is valuable for this research. The tests were

conducted at 91 banks, representing 65 percent of the assets of the EU banking system (CEBS, 2010).

Seven out of 91 banks fell below the 2010 benchmark of 6% Tier 1 capital ratio after the application

of the adverse scenario (CEBS, 2010). However, the tests still had quite some shortcomings; the main

one being the fact that the tests only included trading book bond losses (and no bank book bond

losses) (Meera & MacAskil, 2010) and that it assumed no sovereign default. In general, the investors

were very skeptical about the results, especially since some of the banks that passed the stress test

needed a bail-out only a few weeks later (mainly due to liquidity issues). As a result, these tests also

failed to win the investors’ confidence (Steinhauser, 2011). (3) In June 2011, the third stress test

results were published, disclosing a more detailed level on banks’ exposure. This edition of the tests

was more severe than the previous ones (Steinhauser, 2011) as it did included sovereign haircuts (but

still no default) (EBA, 2011a). However, critique remained, mainly due to the lack of modeling a

Greek default, which the majority of investors expected (Jones and Dowsett, 2010). Eight out of the 90

banks did not maintain the 5 percent Core Tier 1 capital Ratio (CT1R) which was set as a benchmark.

In addition, 16 banks “nearly succeeded” displaying a CT1R between 5 and 6 percent (EBA, 2011a).

As explained above, the stress tests received quite some criticism, both from the investors and the

banks themselves. Moreover, they do not aim to be a prediction of what will happen. Despite this

criticism, the tests should have been valuable, even if it were only for the inputs they disclosed,

thereby creating more transparency in the market. Whether these inputs were really new and whether

they were close to the exposure values estimated by the investors, will be investigated further on.

2.2.2 Reaction of markets on the Greek debt crisis

A first way to look at the reaction of the market on the Greek crisis, is the rising sovereign bond

spread. Institutes with lower risk are able to lend money at cheaper rates than riskier institutes. The

spread between an institute’s interest rate and the one of a “safe benchmark” (in case of the EU: the

German sovereign interest rate) is therefore a relative measure of risk. By looking at the rising trend in

the spread of Greece, one can see that Greece is increasingly getting in trouble. Greece’s spread has

risen above thirty percent (in November 2011), meaning they should have paid thirty percent more

2 Although the sovereign exposure of the last seven banks was only published on July 26, 2010 (Kirkegaard,

2010, 28 July)

17

interest on a bond than Germany if they would have returned to the market at that point (both with a

maturity of 10 years) (exhibit 2). Whether the troubles in Greece are pushing up the spread, or the

rising spread is getting Greece more and more into trouble, is not unambiguous. Both effects influence

and reinforce each other like a vicious circle.

Attinasi et al. (2009) confirm the existing literature, by proving that sovereign bond yield spreads in

the eurozone are a reflection of the liquidity risk, credit risk and international risk aversion. This last

factor means that the investors’ appetite for risk, independent of the change in risk, also plays a role.

In the period between 31 July 2007 and 25 March 2009, this international risk aversion explained

more than half of the widening of the spreads (Attinasi et al., 2009). However, exhibit 2 shows that it

is mainly after the period of this study and around the start of the Greek crisis (event 1) that the Greek

sovereign spread significantly increased. Thus, it is not only the international risk aversion which is to

blame, but probably and mainly the credit risk of Greece which pushed up the spread from the end of

2009 on (the beginning of the time frame of this study). All of this shows the importance of keeping

the market’s confidence in the healthiness of a country’s financial situation.

Exhibit 2: Greek-German sovereign bond spread (source: Bloomberg)

Next to the sovereign bond spread, the price of a credit default swap (CDS) on an underlying

sovereign bond is also a measure of that country’s risk, as perceived by the market. A CDS acts like an

insurance: the person who buys a CDS on a sovereign bond will pay a fixed amount to the seller of the

CDS3. This seller on the other hand, has the obligation to back-up and pay the par value of the bond in

case the issuer of that bond would default (Federal Reserve Bank of Atlanta, 2008). Hence, the price

3 Unlike an insurance however, the buyer of the CDS does not have to hold the underlying asset of the agreement

(the bond) (Morgenson, 2008).

18

of a CDS reflects the perceived risk of the underlying bond. Independent of the fact that there was a

rising trend in the price of the CDSs of most European countries between January 2010 and January

2012, Greece was clearly the biggest loser resulting in an enormous increase in its CDS prices (exhibit

3). On exhibit 4, one can see that the price of the Greek sovereign CDS and bond spread are

extremely correlated since they are both measures of the risk of credit default.

Exhibit 3: Sovereign CDS prices of European countries (2010-2011) (source: Bloomberg)

(a): A level of 1,000 basis points means it costs $1 million a year to protect $10 million of debt for five

years.

Exhibit 4: Greek sovereign CDS spread and bond spread (source: Thomson Reuters’ Datastream)

19

2.3 The influence of the lenders’ exposure

In this section, I will review some literature which links the size of the stock return of lenders to their

exposure. Quite some papers use an event study to relate the exposure of a bank to its securities during

an event period. Those that are most comparable to the study I aim to fulfill, are mainly situated in the

80’s during the less developed country (LDC) debt crisis. They aim to detect the influence of cross-

border exposure to Mexico (or Latin-America as a whole) on the bank securities at the time of the

Mexican default announcement. The results of these papers are quite mixed, possibly because of the

different assumptions used (event window, proxies of exposure, etc.).

I will start with the literature on the short term effect of the Mexican exposure. Schoder and Vankudre

(1986) found no significant effect of the exposure on the bank valuation on the announcement day of

the Mexican default itself. Research of Bruner and Simms (1987) showed a similar result; even though

they found a negative return for all stocks on the announcement day, this return was not related to the

banks’ exposure. However, within ten days, the exposure became a significant determinant of the

cumulative excessive reaction. Turning to the longer term effect, Smirlock and Kaufold (1987) came

to the result that the Mexican default announcement generated negative abnormal returns, which were

significantly related to the size of Mexican exposure within sixty days after the default announcement.

Next, based on a sample of monthly stock returns from 1979 to 1984, Kyle and Wirick (1989) also

confirmed that, even though the banks carried the Latin American bonds at full value in their books,

the market valued them differently. This caused a (6- to 9-months) delayed decrease in market value

of the banks which was related to the size of their exposure. Another paper confirming the relationship

between the exposure and market securities on the long term is written by Cornell and Shapiro (1985)

who concluded that the Latin-American exposure impacted the annual returns of the banks

significantly. However, the effect on the monthly or daily returns is mainly insignificant. In

conclusion, it looks as if exposure had a significant effect on the bank’s valuation, however, only after

a period of time (and not on the announcement day itself).

There are also papers related to this topic which are not situated in the LDC debt crisis. James (1990)

verified the Heterogeneous Creditor Hypothesis for a sample of 23 banks from January 1986 and June

1987. This hypothesis states that the value of the loan also depends on the identity of the lender (and

e.g. the size of exposure he holds). He finds that bank stock returns in general have a positive

relationship with the return on the bank’s loans to LDC. However, banks belonging to the “core

group” (lead lenders) are less sensitive to changes in the LDC prices than banks with less exposure.

This result is opposite to the previous papers which found that banks having more exposure react

stronger on an event. James (1990) explains this by reasoning that highly exposed banks (called lead

lenders) have more power over these countries and are therefore more able to bargain and to punish

them in case they “misbehave”.

20

Next, there are also papers on the relationship between securities and exposure to corporate (instead of

sovereign) borrowers. One example is the research paper of Dahiya, Saunders and Srinivasan (2003)

investigating bank stock returns around a default or “file for bankruptcy”-event. They found

significant negative average abnormal returns (AARs) for the lead banks during these events. The size

of these AARs are related to the exposure of the banks as the stocks of banks with low exposures did

not significantly decline.

As can be seen from the examples given above, many researchers have been investigating the effect of

exposure on the market valuation of a bank. These papers all differ from the research I aim to

investigate in several ways. First of all, they only focus on stock return, none of them looks at CDS

return. This is due to the fact that CDSs have only gained popularity quite recently. Next, almost none

of the papers (except for Cornell and Shapiro (1995)) uses bank specific control variables. Moreover,

market efficiency might have changed since the LDC crisis as more data and communication channels

(as well as programs to transfer these data into information) have become available4. Therefore, it is

possible that the market has become more efficient and that the reaction rate has increased. Another

reason to perform additional research is that I have not found many research on exposure towards non-

LDCs as well as almost none in a recent setting. As a result, I want to verify whether this link is

generalizable towards other countries as well. On top of that, all these papers focus on the default

announcement as the main event. In contrast, I aim to check whether this relationship also holds for

events before, but possibly leading up to a default.

A last thing I want to highlight concerns the fact that the bank exposure towards the LDC countries

was not known until the end of 1982 (Bruner & Simms, 1987) (Cornell & Shapiro, 1985). Therefore,

the investors were, just like before the publication of the stress test in 2010, not accurately informed

about each bank’s exposure. However, in general the conclusion is that the exposure already had a

significant influence, even before 1983. Or as Bruner & Simms (1987, p54) wrote “In the absence of

reliable information on loan exposure, investors appear to respond to surprising news rationally and

quickly”. As a consequence, it is acceptable to expect that the banks’ AARs on the Greek events

before the stress test can also be significantly correlated with their Greek exposure.

4 Chordia, Roll and Subrahmanyam (2008, p.6) confirm this increasing efficiency, based on a sample from ’93

until ’02, as they write “Overall, the data suggest that the liquidity improvement following the tick size reduction

has not only been accompanied by an increase in the efficiency of accommodating order flows, but has also

enhanced informational efficiency by allowing better incorporation of private information into prices.”

21

2.4 Factors influencing the risk profile of a bank

Next, I will display some factors which can drive the risk profile of a bank (in particular towards a

Greek event). In an event study, abnormal returns (AR) are measured as the “systematic excessive”

reaction on top of the “normal expected return” of the bank’s stock or CDS. This normal expected

return is based on the usual market reaction of this bank during an estimation period (Binder, 1998).

When looked at several events in a row, the average of the ARs is calculated, further called AAR

(average abnormal return). Now, the purpose of this research is to extract the influence of the Greek

exposures of the banks from these AARs. However, the Greek exposure is not the only factor that will

influence them. I hereby give some factors which will be used to regress the AARs. Based on some

relevant literature, I will explain how I expect them to be correlated to the size of these AARs and turn

them into hypotheses to be tested5. Many of these factors are related to the risk profile of a bank since

one can expect that as the risk profile of the bank is higher, the market will react more heavily (both as

part of the normal reaction as well as part of the abnormal reaction). Therefore, the factors influencing

the risk profile could also influence these AARs.

In this section, I will introduce six factors which can be relevant for this research: (1) the Greek

exposure, (2) the capital ratio, (3) the size, (4) a funding ratio, (5) an asset structure ratio and (6) the

origin (nationality).

(1) Greek exposure size. The first factor which I expect to have a significant influence is the size of

the exposure towards Greek sovereign bonds. This is the factor which will verify or reject the main

hypotheses of this research. The more Greek exposure a bank has, the more its own results (or even

bankruptcy) will depend on events concerning the Greek default risk. Hence, as explained above, it

seems logical that if a bank holds more Greek sovereign bonds, its security prices will react more

heavily on Greek events. As mentioned in the previous section on research similar to this study, the

abnormal bank equity reaction on a default announcement of their lender is often correlated to the size

of their exposure (Smirlock and Kaufold, 1987) (Dahiya et al, 2003).

This provides the first main hypothesis6 of this research:

5 As the factors are linked to the size of the AARs, it does not matter whether the AARs are positive (which is

expected for the stock return) or negative (CDS return). As a result, the hypotheses are formulated

independent of the fact whether it are the AARs from the bank stock or from CDS returns.

6 The first two hypotheses assume that the reaction per unit of exposure will be more or less similar for each

bank. However, as I will explain further, there might be other factors causing the banks to react differently per

unit of Greek exposure.

22

Hypothesis 1a: There is a positive relationship between the bank’s exposure towards

Greek sovereign debt and the size of its abnormal reaction on Greek event dates.

As explained earlier in this paper, there was no accurate information about the individual exposure of

all the banks (included in the stress test) towards a country before July 2010 (Kirkegaard, 2010, July

28). However, there were some exposure data available like the domestic exposure towards Greece (on

the BIS website) as well as announcements of individual banks. Therefore, the market might have had

a rough estimate of the banks’ Greek exposure such that there might have been some kind of

correlation before the stress test. This is similar to the Mexican default in 1982 when the investors did

not yet have accurate information on all individual bank exposures (Bruner & Simms, 1987) (Cornell

& Shapiro, 1985). Nevertheless, even without this disclosure, the link between the exposure and the

equity returns was already present. Also Peristian, Morgan and Savino (2010) found similar proof as

the investors have been able to decipher largely on their own part of the outcomes from the US stress

test in 2009. However, they conclude that that stress test still produced some vital information about

banks. Hence, it is possible that also in Europe during the Greek crisis, the investors were able to

estimate some vital information resulting in a (probably weak) link between the Greek exposure and

the bank stock and CDS return. As of 26 July 2010, precise data on all the participating banks’

sovereign exposures are publicly available thanks to the stress tests (Kirkegaard J.F, 2010, July 28).

As a result, it is expected that the relationship between the Greek sovereign bonds and the abnormal

reaction, has become more accurate from then on.

Hypothesis 1b: This relationship (explained in hypothesis 1a) becomes more significant

or “accurate” after the publication of the stress test results in July 2010.

(2) Capital ratio. A second factor possibly influencing the reaction of the market towards a particular

event, is the capital ratio. A smaller capital ratio leads to higher risk, meaning that the bank does not

have a lot of “reserves”. As Rime (2001, p. 791) says “With more capital and less risk-taking, the

effect is clearly a decrease in the bank’s default probability”. Banks with a small capital buffer have an

incentive to raise capital and reduce the risk in order to prevent the capital ratio from falling below the

regulatory minimum and thereby avoiding costs (Rime, 2001). Therefore, a bank with less capital,

which is close to the regulatory benchmark should be more sensitive to news concerning the bank’s

future revenue stream (and therefore also default risk) since an event can cause the capital ratio to

either relieve the threat of falling below this regulatory requirement (positive event) or to fall below

the limit (negative event). Or in short: “banks with a high capital ratio have a low risk profile” (Vander

Vennet, 2009, Ch.3 p.15). So banks with a lot of reserves should be less affected by events concerning

a possible default of Greece.

23

The capital ratio however, can also influence the abnormal reaction in a different way. It is possible

that there is some kind of interaction between the Greek exposure and the capital ratio. To put it

simple, I expect that banks with a smaller capital ratio will react more heavily per unit of Greek

exposure. Hence, banks with a big capital ratio will be punished less for holding a similar exposure

than banks with a smaller capital ratio as these bank can “afford” to take on more risk.

Given the previous arguments, the hypothesis about the capital can be split into two sub-hypotheses:

Hypothesis 2a: Ceteris paribus, there is a negative relationship between the capital ratio

and the size of the AARs.

Hypothesis 2b: The influence of the Greek exposure differs along with the capital size of

the bank, ceteris paribus. A bank with a lower capital ratio will get a bigger abnormal

reaction per unit of Greek exposure than the ones with a bigger capital ratio.

(3) Size. A third factor related to the probability of bankruptcy, is the size of a bank (usually measured

by their total assets). One could argue that bigger banks will be saved more easily since they are “too

big to fail” (Brewer & Jagtiani, 2009) resulting in lower idiosyncratic risk. On the other hand, there are

also arguments saying that risk rises with the size. First of all, as banks become bigger, they can

become too complex to monitor. On top of that, smaller banks are often not publicly quoted and

therefore might have less pressure to constantly generate good results. They will therefore tend to take

on more risk than the publicly quoted banks. Demsetz and Strahan (1997) verified that asset size is the

most important predictor of firm-specific risk. They show that size holds a negative relationship with

idiosyncratic risk and a positive one with systematic risk. In this research, size will be used as a

control variable to explain the average abnormal returns. As these AARs do not include the

idiosyncratic error term, the hypothesis is based on the relationship between size and systematic risk

so that a positive relationship can be expected.

Hypothesis 3: The bigger the size, the more a bank will be affected by a Greek event, so

(ceteris paribus) there is a positive relationship between the size of a bank and the size of

the AARs.

(4) Funding ratio. Next, a funding ratio might also affect the risk of a bank. In general, the liability

side of a bank balance sheet mainly exists of deposits, equity and loans. These loans are often short

term and the lenders are mainly other banks. The rate at which the bank can borrow depends on its

probability of default. If the bank is doing badly or there is a crisis, it can experience trouble

refinancing these short-term debts at a reasonable rate of interest. Deposits on the other hand,

24

appear to be quite stable. Ivashina and Scharfstein (2010) follow a similar logic in their paper on bank

lending during the financial crisis of 2008. They argue that “Concerns about bank solvency made it

difficult for banks to roll over short-term debt and raise additional long-term debt. (…) Thus, banks

with a large and stable base of deposits (particularly if they are insured) should be less dependent on

financing from short-term debt markets, and therefore less credit-constrained.“ (p.2-3) Therefore,

the proportion of funding coming from deposits is often seen as a measure of risk, where more

deposits mean a lower risk, so smaller AARs. Wheelock’s study (1992) verified that “banks with

higher deposits-to-assets ratio were less likely to fail” (p. 541). More recent research of Demirguc-

Kunt and Huizinga (2009) confirms this by proving that banks with more deposit funding are less risky

than their competitors relying more on the money market. However, this relationship is not absolute

as banks need to strive towards a balanced mix of funding sources and too many or too few of one of

these sources is not good.

Hypothesis 4: Ceteris paribus, the more funding coming from deposits, the less a bank

will be sensitive to news concerning its revenue stream or risk profile (and the smaller

the AARs).

(5) Asset structure ratio. A bank’s assets mainly consist of investments/securities and loans.

Conform the well known tradeoff between risk and return: as loans are in general more profitable than

investments (since the investments are mainly sovereign bonds7), they should also imply a higher risk.

As a result, Wheelock (1992) proved that the bank’s risk rises with an increasing loan-to-asset ratio.

As stocks mainly focus on the long-term profitability potential and CDSs mainly on the risk side of a

bank (King, 2009), the relationship concerning the loans might depend on the security type. Hence, the

loans-to-asset ratio could be perceived differently as bondholders might prefer a lower ratio than

stockholders. So, past the maximum loans-to-asset ratio of the bond holders, there could be, ceteris

paribus, a positive relationship between the loan-to-asset ratio and the size of the AARs from the stock

returns; and a negative relationship with the size of the AARs of the CDS returns. However, there are

also arguments for a negative relation (for the stock return) between the loans-to-asset ratio and risk

since banks primarily involved in lending are less exposed to financial market shocks (and more to the

business market). Given the duality of these arguments, I cannot make any reliable prediction about

the sign of the coefficient. However, the asset structure ratio may have some kind of influence, which

is why it should be included as a control variable.

7 At least, this accounts in general for the Belgian banking system (Vander Vennet, 2009)

25

Hypothesis 5: Ceteris paribus, the amount of loans influence the risk profile of a bank

and should therefore also affect the AARs.

(6) Nationality. A last factor considered is the domestic country of the bank. I found two reasons to

justify the incorporation of this factor. First, there can be an extra effect for banks from other

peripheral countries. Not only are the PIIGS heavily exposed to each other, there is also the notorious

contagion effect, caused by the high degree of financial integration (Bolton & Jeanne, 2011).

“Interconnection among the peripheral countries constitute a further channel for contagion in the euro

area” (Weistroffer & Möbert, 2010, p.1). This means that the market becomes worried about other

similar countries (in this case peripheral) when bad news comes from one of them, as they are

perceived to be similar. Therefore, a flight to quality takes place: the investors flee from more risky

obligations to obligations of safer countries, thereby judging similar countries equally (like the PIIGS).

On top of that, Bolton and Jeanne (2011) point out that while “only” 30% of the European sovereign

debt is held by the European banks, 14.9%, or nearly half of the debt in the European banking system,

is to be found in the domestic banking system in Europe. Thus, since banks are in general heavily

exposed towards the sovereign debt of their own country and the contagion effect raises the perception

that the probability of a default of the other peripheral countries increases as well, it is possible that

banks from the PIIGS have an extra strong abnormal reaction. Following this reasoning8, this reaction

is independent of their exposure towards Greece (they don’t even need to have any Greek exposure).

To conclude, an extra strong abnormal return can be expected for banks from Portugal, Ireland, Italy,

(Greece) and Spain.

Furthermore, banks from Greece will probably even have a bigger abnormal reaction because they are

influenced in more ways than only by the possibility of a sovereign default (independent of their

exposure towards their domestic country). An event can cause new government regulations (like

higher taxes, changes in subsidies…) or can be caused by bad economic figures (like a shrinking

economy which invokes a higher probability of default for other borrowers of the bank as well).

Hence, a Greek event can affect Greek companies, even those which don’t hold any sovereign bonds.

Hence, it is possible that Greek banks experience an extra market reaction, independent of their

domestic exposure.

Building further upon this last reasoning, the relationship between the size of the abnormal reaction

and the Greek exposure might be dependent on the condition whether it is a Greek or non-Greek bank.

Or put differently: the abnormal reaction per unit of Greek exposure can depend on the nationality of

the bank. This asks for a conditional hypothesis (6c) as described by Brambor, Clark and Golder

8 Another reason might be that some events not only influence Greece, but also other countries (most often the

PIIS).

26

(2006). As explained above, I expect a big part of the abnormal reaction of the Greek bank’s stock (or

CDS) return to be independent of their exposure towards Greece. On top of that, the Greek banks have

in general about forty times more Greek sovereign debt exposure than the other banks in the sample

(EBA data, 2010). Therefore, I expect that for Greek banks, one unit of exposure will affect the returns

less than for other banks9.

Hypothesis 6 is split into three sub-hypotheses:

Hypothesis 6a: If the bank’s domestic country is one of the PII(G)S, the AARs will be,

ceteris paribus, bigger.

Hypothesis 6b: Greek banks will react even more than banks from any other country,

independent of their exposure. So, ceteris paribus, the AARs of Greek banks should be

bigger.

Hypothesis 6c: The influence of the Greek exposure differs between Greek and non-

Greek bank, ceteris paribus. For the Greek banks, the influence should be smaller per

unit of exposure.

Of course there are also other factors influencing the CDS movements, like the risk aversion of

investors, the economic growth and exchange rate volatility (Yeyati and Micco, 2003). For banks

stock return, other variables like the short-term and long-term interest rates as well as their volatilities

can also have an influence (Elyasiana and Mansur, 2003). However, it is impossible to include all of

them.

The hypotheses stated above will be repeated and further specified in section 4.1 Summary of

hypotheses (infra, p.36).

9 This does not mean that the total abnormal reaction accounted to their exposure is smaller, as it needs to be

multiplied by the total number of ‘units exposure’. As a consequence, for the Greek banks, the effect of one unit

(so the size of the coefficient) of the Greek exposure should be more than forty times smaller in order to come to

an absolute impact (caused by the exposure) which is smaller than for the non-Greek banks

27

3. Data

The previous topic describes the relevant literature as well as some relevant control variables to take

into account. While that part is the theoretical basis of this research, this part starts with the real

research by answering some practical questions like which data are used, where they come from and

what the correlation is between the separate data items.

As many banks are exposed to sovereign bonds, the banks’ market valuation might be affected by

Greek events. In general, the reaction of the banks’ investors can be observed by looking at the banks’

stock return as well as the changes in the CDS price. In a way, both CDS and stock return are

measures of the risk of a bank. Since the effect of Greek crisis on the bank will be primarily noticeable

through the adjustment of the bank’s risk, both CDS prices and stock prices will be used for this

research. However, as BIS research from King (2009) and Panetta et al. (2009) point out, these

measures are not always aligned. Abnormal CDS returns should show the impact of the events on the

creditors while stock prices should show the effect on the shareholders (King, 2009). Panetta et al.’s

research (2009) showed, based on a study set in the financial crisis of 2008, that the reaction is not

always similar: while the government rescue packages in general reduced the probability of a default

and hence, pushed down the CDS spread, the impact on the bank stocks was less clear. The bank

stocks in general showed a negative reaction partly due to the perceived effect on the long term

profitability and the uncertainty about the duration of the support as well as a credible exit strategy

(Panetta et al, 2009). As a result, it is possible that the CDS spread and the stock returns will react

differently.

I would prefer to use CDS returns for this research since stock returns are influenced by other factors

than risk and a Greek event should mainly affect the bank (especially the non-Greek or non-PIIIGS

banks) due to a change in the probability of a default. However, there are less banks which have liquid

CDS spreads publicly available. As a result, it is possible that, due to a lack of enough observations (in

this case CDS series of banks), this research can only be done with stock returns. Next to the

availability of the stock return data, using both data types is also useful to check the robustness of the

results in order to see whether they are dependent on the type of security. If the research on both CDS

and stock return would give similar results, there is a higher probability that these results are

generalizable and stable. If not, then the difference might reflect, next to a lack of robust results, a

different impact of the events on creditors than on shareholders (King, 2009) (as explained in the

previous paragraph).

The first section explains how the data for the bank stock returns were gathered, while the second

section focuses on the CDS returns. Table 2 and 3 give their summary statistics. The third section

28

shows the data gathering procedure for the Greek debt exposure as well as the control variables used.

Table 4 gives the summary statistics of these control variables and the Greek exposure while Table 5

shows the correlation between them. Table 6 summarizes the data types and their sources.

In this research, I aim to verify hypotheses which should then be generalizable to the entire for the

European banking system in total. Unfortunately, it is not possible to include all European banks in the

sample as many do not have liquid, publicly available data. The two main bottlenecks limiting the size

of my sample are the availability of the bank’s Greek sovereign debt exposure as well as that of the

stock price and/or CDS price data. However, I aim to keep my sample as broad as possible in order to

be able to generalize the conclusions for this sample to the population of European banks.

3.1 Bank stock returns

The initial sample of banks was based on the banks included in the stress test of 2010, since I do not

have data on the Greek sovereign debt exposure of other banks. Those 91 banks included cover 65

percent of the total assets of the EU banking sector as well as at least 50 percent of the local market of

every EU member state (EBA, 2011a). However, since not all of them have publicly available data, I

had to drop almost 44 percent of the banks. In total, there are 51 banks included in the part based on

the stock return. For the market model, I used “Europe-DS” which is a general European stock market

index calculated by Datastream, a database of Thomson Reuters.

Daily stock prices were collected from Datastream, starting at the beginning of 2006 until 18

November 2011, the starting day of my analysis. Equation 1 provides the logarithmic stock return data

on a certain day t (Rt) based on the price of that day (Pt) and the previous day (Pt-1). Logarithmic

returns (and not arithmetic returns) are used as this common practice for researchers.

1t

tt

P

PlnR (1)

Table 2 provides the summary statistics of the stock return of every bank and the market index (called

“Market return”). Remarkable is the minimum return of -111% of the Royal Bank of Scotland (RBS).

This happened on January 19, 2009 when RBS reported a loss of £28bn for 2008 (Dunkley &

Griffiths, 2009, 19 January). This induced a loss of almost 67% when calculated arithmetically. As

these summary statistics look quite normal (taking into account that they are logarithmic returns), no

adjustments were made.

29

SUMM. STATISTICS DAILY STOCK RETURNS (2/1/2006 - 18/11/2011)

Mean Maximum Minimum Std. Dev. Observations Country

AGRIC BANK OF GREECE -0,27% 25,9% -30,1% 4,1% 1534 Greece ALLIED IRISH BANKS -0,33% 36,1% -87,7% 6,3% 1535 Ireland ALPHA BANK -0,19% 26,2% -21,6% 3,7% 1534 Greece BANCO BPI -0,12% 23,0% -11,6% 2,3% 1534 Portugal BANCO COMERCIAL PORT -0,17% 15,4% -14,6% 2,4% 1534 Portugal BANCO DE SABADELL -0,04% 16,8% -8,0% 1,7% 1534 Spain BANCO PASTOR, S.A. -0,07% 19,1% -10,2% 1,9% 1534 Portugal BANCO POPOLARE -0,15% 15,5% -17,9% 2,9% 1534 Italy BANCO POPULAR ESPAÑOL -0,06% 18,8% -10,1% 2,3% 1534 Spain BANCO SANTANDER -0,02% 20,9% -12,7% 2,4% 1534 Spain BANK OF CYPRUS PUBLIC -0,10% 17,1% -14,1% 2,9% 1534 Cyprus BANK OF IRELAND -0,27% 40,0% -78,7% 6,3% 1534 Ireland BANK OF VALLETTA -0,01% 26,7% -22,3% 2,0% 1534 Malta BANKINTER, S.A. -0,04% 13,5% -8,4% 2,4% 1534 Spain BARCLAYS -0,08% 54,3% -28,1% 4,1% 1534 UK BNP PARIBAS -0,04% 19,0% -18,9% 3,0% 1534 France COMMERZBANK AG -0,17% 19,5% -28,2% 3,4% 1534 Germany CREDIT AGRICOLE -0,09% 23,4% -14,3% 3,2% 1534 France DANSKE BANK -0,05% 22,3% -18,1% 2,9% 1534 Denmark DEUTSCHE BANK AG -0,06% 14,0% -17,2% 2,5% 1534 Germany DEUTSCHE POSTB AG -0,05% 14,1% -27,2% 2,8% 1534 Germany DEXIA -0,26% 28,9% -35,2% 3,8% 1534 Belgium EFG EUROBANK ERGASIAS -0,22% 25,8% -22,6% 3,8% 1534 Greece ERSTE GR BANK AG -0,07% 17,0% -20,0% 3,4% 1534 Austria ESPÍRITO SANTO FIN GR -0,07% 19,3% -20,8% 1,7% 1534 Portugal HSBC HOLDINGS PLC -0,03% 14,9% -21,7% 2,2% 1534 UK ING Bank -0,09% 25,7% -32,1% 3,8% 1534 Netherlands INTESA SANPAOLO -0,06% 18,0% -18,5% 2,8% 1534 Italy JYSKE BANK A/S -0,04% 11,1% -12,5% 2,3% 1534 Denmark KBC GROUP -0,12% 40,5% -28,7% 4,2% 1534 Belgium LB BERLIN AG 0,02% 26,2% -16,3% 2,6% 1534 Germany LLOYDS BANKING GR -0,15% 41,4% -42,9% 4,2% 1534 UK MARKET RETURN -0,01% 8,2% -8,1% 1,3% 1534 Europe MARFIN POPULAR B. -0,14% 14,9% -16,8% 2,9% 1534 Cyprus MONTE DEI PASCHI DI SIENA -0,13% 12,3% -11,9% 2,2% 1534 Italy NAT BANK OF GREECE -0,17% 25,6% -23,3% 3,7% 1534 Greece NORDEA BANK 0,00% 17,5% -13,6% 2,7% 1534 Sweden OP-POHJOLA GROUP -0,01% 19,6% -18,2% 2,6% 1534 Finland OTP BANK NYRT. -0,06% 23,2% -22,2% 3,5% 1534 Hungary PIRAEUS BANK GR -0,22% 25,1% -25,0% 3,5% 1534 Greece PKO BANK POLSKI 0,02% 13,7% -13,9% 2,7% 1534 Poland RAIFFEISEN ZB OESTERRREICH -0,07% 17,1% -28,3% 3,5% 1534 Austria ROYAL BANK OF SCOTLAND -0,21% 31,1% -111,0% 5,0% 1534 UK SKANDIN. ENSKILDA BANKEN -0,04% 24,8% -23,3% 3,4% 1534 Sweden SNS BANK -0,16% 21,0% -20,6% 3,2% 1437 Netherlands SOCIETE GENERALE -0,10% 21,4% -17,7% 3,3% 1534 France SVENSKA HANDELSB. 0,01% 14,9% -12,7% 2,5% 1534 Sweden SWEDBANK -0,04% 18,9% -21,0% 3,4% 1534 Sweden SYDBANK A/S -0,21% 25,9% -32,8% 3,9% 1424 Denmark TT HELLENIC POSTB -0,03% 16,3% -15,6% 2,3% 1534 Greece UNICREDIT -0,11% 19,0% -14,1% 3,1% 1534 Italy UNIONE DI BANCHE ITALIANE -0,10% 11,5% -13,2% 2,3% 1534 Italy

Table 2: Summary statistics of bank stock return (source: Datastream, EBA, author’s calculations)

30

3.2 Bank CDS returns

For the data collection of the CDS returns, the same logic was applied. CDS data were available for 52

out of the 91 banks from the stress test of 2010. However, CDSs have only gained popularity quite late

for most banks and some only recently noted (liquid) quoted CDS prices. Therefore, I had to exclude

those banks whose data over the whole period (and especially during the time frame of the events)

showed too many gaps or were not sufficiently liquid. After this selection (further specified in the next

paragraph) only 38 banks were left.

I collected the data starting from 1 July 2008 until 16 November 2011 from Bloomberg. This is a

shorter time frame than the one considered for the calculation of stock returns, because the CDS data

before 2008 were in general very illiquid. Unlike for the stocks from Datastream, I first needed to

modify the data so that the same dates were present in all the series. This was necessary since it looks

as if, for the earlier years, Bloomberg just followed the opening days of the local stock exchange

market (while many countries have different working days). As a result, there were some banks which

did not list a CDS price on a particular date. Hence, I have modified the data so that, if there was only

one day in a row missing, the average of the CDS price the day before and the day after was taken to

fill in the gap. If there were several subsequent days missing, I just left it blank which meant that the

CDS returns for those days (and the day following that set) could not be calculated. On top of it, I

added the restriction that at least 30% of the data needed to be liquid (so with daily returns different

from zero). Where this requirement was not fulfilled (on a sample of at least ten days), I deleted the

data as if there were no data available for that day since it could bias the market model. After

modifying these data, I left out those banks which did not have CDS prices available before the third

event (December 2009) and those which missed more than 5 (out of the 29) events.

For the market model, I used a general European CDS market index: the iTraxx European 5-year

investment-grade index. This is an “equal-weighted index based on the 125 most liquid financial and

non-financial CDS contracts” (King, 2009, p10).

Again, equation 1 was used to calculate the daily CDS returns of day t (Rt) from the CDS prices of day

t (Pt) and from the day before (Pt-1).

Table 3 provides the summary statistics of the CDS return of every bank included as well as the

market index (“iTraxx 5 yr”).

31

SUMM. STATISTICS DAILY CDS RETURNS (16/11/2008 - 16/11/2011)

Mean Maximum Minimum Std. Dev. Observations Country

ABN/ FORTIS BANK NL 0,27% 19,2% -15,7% 2,8% 530 Netherlands

ALLIED IRISH BANKS 0,27% 32,5% -77,5% 5,5% 776 Ireland

ALPHA BANK 0,32% 20,2% -17,8% 3,4% 603 Greece

BANCO COMERCIAL PORT 0,33% 26,6% -33,5% 4,7% 881 Portugal

BANCO POPOLARE 0,23% 32,3% -18,9% 4,1% 881 Italy

BANK OF IRELAND 0,24% 23,3% -82,0% 5,8% 881 Ireland

BARCLAYS 0,08% 22,8% -47,3% 4,9% 881 UK

BAYERISCHE LANDESBANK 0,15% 31,4% -13,1% 3,0% 852 Germany

BNP PARIBAS 0,16% 29,7% -31,5% 5,2% 881 France

CAIXA 0,37% 21,1% -22,1% 3,4% 591 Spain

COMMERZBANK AG 0,11% 39,7% -38,8% 5,2% 881 Germany

CREDIT AGRICOLE 0,12% 30,7% -39,5% 5,0% 881 France

DANSKE BANK 0,21% 61,8% -17,9% 3,9% 864 Denmark

DEUTSCHE BANK AG 0,09% 34,3% -48,5% 5,0% 881 Germany

ERSTE GR BANK AG 0,12% 24,0% -24,7% 3,8% 881 Austria

ESPÍRITO SANTO FIN GR 0,26% 24,6% -35,4% 4,7% 881 Portugal

HSBC HOLDINGS PLC 0,09% 29,1% -22,4% 4,3% 881 UK

HSH NORDBANK AG -0,07% 17,3% -35,7% 3,0% 520 Germany

ING Bank 0,12% 26,7% -33,6% 3,9% 849 Netherlands

INTESA SANPAOLO 0,24% 28,7% -28,8% 5,5% 881 Italy

ITRAXX 5yr 0,06% 21,3% -26,0% 3,6% 881 Europe

KBC GROUP 0,20% 48,4% -32,7% 3,9% 832 Belgium

LANDESBANK BADEN-WÜRTTEMBERG

0,22% 18,6% -8,6% 2,4% 657 Germany

LANDESBANK HESSEN-THÜRINGEN GZ

0,14% 12,5% -9,7% 1,8% 510 Germany

LLOYDS BANKING GR 0,15% 35,9% -37,3% 4,4% 881 UK

MONTE DEI PASCHI DI SIENA 0,21% 21,3% -38,2% 5,2% 881 Italy

NORDDEUTSCHE LANDESBANK 0,15% 12,3% -8,4% 1,7% 521 Germany

NORDEA BANK 0,10% 25,5% -22,6% 3,1% 811 Sweden

RABOBANK GROUP 0,08% 22,2% -16,8% 3,7% 881 Netherlands

RAIFFEISEN 0,12% 20,8% -27,5% 3,4% 863 Austria

ROYAL BANK OF SCOTLAND 0,12% 35,7% -72,4% 5,1% 881 UK

SKANDIN. ENSKILDA BANKEN 0,06% 22,6% -42,5% 3,7% 855 Sweden

SNS BANK 0,14% 19,9% -32,8% 3,3% 813 Netherlands

SOCIETE GENERALE 0,15% 27,4% -32,6% 4,9% 881 France

SVENSKA HANDELSB. 0,06% 15,7% -28,2% 3,3% 811 Sweden

SWEDBANK 0,13% 13,8% -14,4% 2,3% 424 Sweden

UNIONE DI BANCHE ITALIANE 0,17% 24,9% -29,0% 4,2% 675 Italy

UNICREDIT 0,23% 38,0% -33,1% 5,3% 881 Italy

WESTLB AG 0,16% 38,1% -30,2% 4,5% 881 Germany

Table 3: Summary statistics of bank CDS return (source: Bloomberg, EBA, author’s calculations)

32

3.3 Control variables and exposure data

This section is about the data which will be used to further regress the AARs (of the events). As these

data are not continuously available, I took data reported at the end of the year. I collected them for (the

end of) 2009 and 2010 and I also calculated the average of these two10

.

Data from each bank in the sample needed to be collected for the factors identified in section 2.4

Factors influencing the risk profile of a bank (supra, p. 20).

3.2.1 Greek exposure ratio (GEXPR)

In order to find the relationship between the Greek sovereign exposure of a bank and the AARs, a

variable representing the exposure is necessary. As the same amount of exposure could harm a larger

bank less than a smaller bank, a ratio which weighs the exposure according to the size of the bank

would be more correct. Cornell and Shapiro (1985) also used this variable11

. As already mentioned,

the stress test of 2010 (and also the one of 2011) revealed the sovereign bond exposure of the 91

participating banks. Since the net exposure is not published for all 91 banks, the gross exposure

(GrEXP) (net of impairment) is used to make a ratio by dividing it by the bank’s total assets (TA). So

the Greek exposure ratio of bank i (GEXPRi) is calculated like this:

i

ii

TA

GrEXPGEXPR (2)

While the GrEXP is collected from the EBA website, the TA (see below) comes from Bankscope.

3.2.2 Capital ratio

As a measure for capital the Tier 1 capital ratio (T1CR) is used. This ratio is also used as an input in

the stress tests to see whether, after modifying the T1CR by several scenarios, the bank passed the test

or not. Banks failed the stress test if their Tier 1 capital ratio after the scenarios was too small to meet

the benchmark (CEBS, 2010). It is also possible to use this outcome (whether the bank failed the test

or not) as a way of indicating whether the bank has a sufficient capital buffer to cope with setbacks.

However, incorporating this distinction between failed and not-failed banks in the analysis would ask

for a dummy (0 for those banks that passed and 1 for those that failed), while there are more data

10

For data from the stress tests, the variables reported as input for the stress test published half of 2010 (2011),

are end-of-year variables of 2009 (2010). So for the data taken from the EBA website, these are used.

11 James (1990) and Smirlock and Kaufold (1987) weigh their exposure against the amount of capital. However,

as a capital ratio is already included, it seems better to weigh it against the total assets like the other ratios which

are included as control variables.

33

available. A dummy does not differentiate between those banks that just passed, passed with some

reserve, passed “with distinction”, etc. Hence, it is better to use a ratio since this differentiates more.

On top of this, using a “GAP bank”-dummy or even the capital ratio after a scenario of the stress tests,

would require some kind of proof that these tests and scenarios make sense and that the resulting Tier

1 capital ratio is a better measure than the initial one. Since I do not have such proof, I prefer to stick

with the initial unmodified T1CR used as input for the stress test. These are published on (and

collected from) the EBA website.

3.2.3 Size

As a measure for size, I used the total assets (TA) on a logarithmic scale (ln(TA)). I applied a

logarithmic scale since this is standard practice in most research. I could also have used a dummy for

those banks passing an arbitrary too-big-to-fail (TBTF) threshold as suggested by Brewer & Jagtiani

(2009). However, this is quite uncommon for this type of study and again, a lot of information would

be lost while this TBTF threshold is only arbitrary and might even be volatile over time (e.g. while

they didn’t save Lehman Brothers in 2008, they might decide differently in a similar situation now).

The data was collected from Bankscope, a global databank with detailed information about banks

gathered from financial statements. However, these data needed to be corrected for mistakes as e.g. the

PKO bank suddenly appeared to be the largest European bank in the sample as there was probably a

mistake in the units used. I have corrected this by comparing the data with the risk weighted asset size

disclosed by the stress test of 2010, found on the EBA website.

3.2.4 Funding ratio

Since Wheelock (1992) proved that risk rises with a decreasing deposit-to-asset ratio (Dep/TA), this

measure will be used as funding ratio. The banks’ deposit data were collected from Bankscope. After

checking and adjusting these data for mistakes in units when necessary, the ratio was calculated by

dividing the amount of deposits by the total assets.

3.2.5 Asset structure ratio

For the asset structure ratio, the loans-to-asset ratio (Loans/TA) was in accordance to Wheelock’s

study (1992). Again, the data on the amount of loans held by each bank were available on Bankscope.

After checking this data for mistakes in units, the ratio was retrieved by dividing the amount of loans

by the total assets.

34

3.2.6 Country dummy

The initial idea to just add a country dummy for each separate country did not make sense in this

sample as it is so small that there are some countries which only have one bank causing

multicollinearity issues. Therefore, I used several dummies to test the different hypotheses. The first

hypothesis is that banks from the peripheral countries would be more sensitive. To investigate this, a

PIIGS dummy is made. A second hypothesis is that, next to the peripheral influence, banks in Greece

will also experience an extra influence given that they are affected in more ways than through the fear

of a default on the Greek bonds. In order to test this hypothesis, two dummies are made: one for the

PIIS (Portugal, Ireland, Italy and Spain) and a separate one for Greece. These dummy series will be

further referred to as PIIGS, PIIS and GREECE.

Appendix 1, tables 4 and 5 are based on the data of the banks included in the sample of the stock

returns. As the banks included in the CDS sample are largely the same, the summary statistics of their

control variables are very similar and therefore not displayed. Table 4 gives the summary statistics of

the Greek exposure and the control variables. As can be seen, 41% (12%) of the banks (included in the

stock return sample) originate from the PIIGS (Greece). Appendix 1 shows the explicit input values

for each bank of the stock sample. Table 5 shows the correlation between them12

. As can be seen on

the table, there is a high correlation between the Greek exposure ratio and the “Greece” dummy. This

can be explained by the fact that banks in general have a lot of exposure to their domestic countries

(Bolton & Jeanne, 2011). Next to that, there is also a high correlation between the PIIS and PIIGS

dummy. This is logical as these dummies include the same banks, with the exception of the Greek

ones in the PIIS dummy. Table 6 ends this chapter by summarizing the data sources of the data series.

CONTROL VARIABLES AND GREEK EXPOSURE DATA

Mean Maximum Minimum Std. Dev.

2009 2010 AVG 2009 2010 AVG 2009 2010 AVG 2009 2010 AVG

GEXPR 1,8% 1,8% 1,8% 22% 24% 22% 0% 0% 0% 4,7% 4,8% 4,7%

T1CR 10,4% 9,1% 9,7% 17% 19% 18% 7,0% 3,7% 5,4% 2,1% 2,6% 2,3%

LN(TA) 5,534 5,482 5,509 7,994 7,890 7,943 2,209 2,157 2,183 1,420 1,440 1,429

DEP/TA 43% 47% 45% 62% 69% 65% 20% 27% 25% 11% 11% 11%

LOANS/TA 58% 58% 58% 84% 82% 83% 17% 21% 19% 15% 14% 15%

PIIGS 41% 41% 41% 100% 100% 100% 0% 0% 0% - - -

PIIS 29% 29% 29% 100% 100% 100% 0% 0% 0% - - -

GREECE 12% 12% 12% 100% 100% 100% 0% 0% 0% - - - Table 4: Summary statistics of control variables and Greek exposure data (source: EBA, Bankscope,

author’s calculations)

12

This is the correlation table for the averaged variables from 2009 and 2010. As the correlation between the

variables of 2009 and the correlation of those of 2010 are similar, no separate tables are shown for them.

35

GEXPR T1CR ln(TA) dep /TA loans/

TA PIIGS Greece PIIS

GEXPR 1,000 0,265 -0,375 0,584 0,058 0,354 0,866 -0,230

T1CR 0,265 1,000 -0,116 0,146 -0,349 -0,419 0,221 -0,609

ln(TA) -0,375 -0,116 1,000 -0,697 -0,534 -0,268 -0,314 -0,067

dep /TA 0,584 0,146 -0,697 1,000 0,335 0,192 0,545 -0,177

loans/TA 0,058 -0,349 -0,534 0,335 1,000 0,470 0,132 0,415

PIIGS 0,354 -0,419 -0,268 0,192 0,470 1,000 0,436 0,772

Greece 0,866 0,221 -0,314 0,545 0,132 0,436 1,000 -0,236

PIIS -0,230 -0,609 -0,067 -0,177 0,415 0,772 -0,236 1,000 Table 5: Correlation matrix of control variables and Greek exposure (source: EBA, Bankscope, author’s

calculations)

Data type Data source

Stock prices Datastream

CDS prices Bloomberg

Greek Exposure Ratio EBA website

Tier 1 Capital Ratio EBA website

Size, funding ratio and asset structure ratio Bankscope

Banks + domestic countries EBA website Table 6: Summary data sources

36

4. Methodology Now that the data collection has been explained, it is time to move on to the methodology used to

modify these data in order to investigate the hypotheses. The main question to be answered is whether

there is a relationship between the size of the abnormal market reactions of a bank and the size of the

Greek sovereign debt exposure. The event study methodology will be used to assess this relationship

since it “has become the standard method of measuring security price reactions to some announcement

or event” (Binder, 1998, p.111).

To measure this relationship there are two big steps: the first one concerns the least squares regression

of a market model in order to get the average abnormal return of each bank i (AARi) on the event

dates. The second step concerns the regression of these AARs on the exposure and some control

variables in order to identify the coefficient and reliability of the relationship between the AARs on

the chosen Greek events and the banks’ exposure. This second step only makes sense on condition that

the first regression resulted in enough significant coefficients.

However, before jumping to the methodology, the first section of this chapter summarizes the

hypotheses/expectations I aim to test. Next, step 1 and step 2 of the methodology are explained. The

same methodology will be applied twice, once for the bank stock returns and once for the CDS

returns. Since the methodology is similar for both, I will just explain it in general for the “returns”,

independent whether they are stock returns or CDS returns.

4.1 Summary of hypotheses

The reasoning behind these hypotheses has been explained/constructed with the help of related

literature and can be read 2.4 Factors influencing the risk profile of a bank (supra, p.20). Here, they

are translated into the expected results (sign of the relationship with the average abnormal returns).

The first two hypotheses are the main ones I aim to verify. The other hypotheses are expectations

which will be more the result than the goal of this research.

The expected results are dependent on the type of security. When there is good news, in general, the

abnormal stock returns should be positive. As the events are formulated positively (since the negative

events received the value “-1”), there should be positive AARs for the stock returns on the day of an

event. The CDS returns however, should be negative when there is good news as the risk premium

should drop. Therefore, I expect negative AARs for CDS returns on the day of a (positive) event.

Due to this difference in reaction, the expected sign for the results will be the opposite for stocks

versus CDSs. I formulated them here for the stock returns, while the expected sign for the CDS returns

is mentioned between brackets.

37

Hypothesis 1a: There is a positive (negative) relationship, ceteris paribus, between the exposure

a bank has towards Greek sovereign debt (GEXPR) and its abnormal reaction on Greek event

dates (AAR) of the stock (CDS) returns.

Hypothesis 1b: This relationship becomes more significant or accurate after the publication of

the stress test results in July 2010.

Hypothesis 2a: There is a negative (positive) relationship between the capital ratio, T1CR and

the AARs of the stock (CDS) returns, ceteris paribus.

Hypothesis 2b: Ceteris paribus, there is a negative (positive) relationship between the capital

ratio of a bank and the AARs per unit of Greek exposure of the stock (CDS) returns. So, the

higher the capital ratio, the smaller the effect of the Greek exposure on the AARs.

Hypothesis 3: The bigger the size, the more a bank will be affected by a Greek event, so, ceteris

paribus, there is a positive (negative) relationship between ln(TA) and the AARs of the stock

(CDS) returns.

Hypothesis 4: There should be a negative (positive) relationship between the Dep/TA ratio and

the AARs of the stock (CDS) returns.

Hypothesis 5: Ceteris paribus, the amount of loans influence the risk profile of a bank and

should therefore also affect the AARs.

Hypothesis 6a: If a bank’s domestic country is one of the PII(G)S, there will be, ceteris paribus,

a positive (negative) relationship between the PIIGS dummy and the AARs of the stock (CDS)

returns.

Hypothesis 6b: Greek banks will react more on Greek events than the other countries,

independent of their exposure, ceteris paribus. Meaning that there will be a positive (negative)

relationship between the Greek dummies and the AARs of the stock (CDS) returns.

Hypothesis 6c: The influence of the Greek exposure differs between Greek and non-Greek

banks, ceteris paribus. For the Greek bank, a smaller effect per unit of exposure is expected.

38

4.2 STEP 1: Market model and average abnormal returns

The methodology concerning this first part, the calculation of the average abnormal returns (AARs), is

based on Binder (1998). He provides a method with which the AAR(s) per bank can be calculated in

one regression by including a dummy, which represents the events, in the market model. However, as

there are several questions this research aims to answer, two main types of event dummies will be

needed. For the first hypothesis which looks at the impact of the Greek exposure over the whole

period, one (further called “not-time split”) dummy representing all events can be used. However, for

hypothesis 1b, about the difference in effect of the Greek exposure before and after the stress test

publication, two (further called “time split”) dummies need to be included: one with the events before

and one with those after July 2010. These two sub-steps will be further explained below.

4.2.1 Not-time split dummies

As can be derived from the hypotheses, I first need the AARs for each bank. Most often abnormal

returns are calculated as the prediction errors from the normal market model, based on an estimation

period which excludes the event windows. However, I modeled the AARs from bank i as a coefficient

(γi) of an event dummy (Dt) so that it can be included in the least squares regression with the market

model (Rit=αi+ βi*Rmt + εi) (Binder, 1998). The event dummy (Dt) equals “1” (or “-1” in case of a

negative event) if that date is included in the event window and 0 if not. That way the market model

can be estimated based on the whole time frame (event windows included). Equation 3 shows the

model being regressed in accordance to Binder (1998). In this equation, Ri,t resembles bank i’s return

on day t which is regressed on Rm,t, the market return on the say day.

Ri,t = αi+ βi*Rm,t + γi*Dt + εi,t (3)

This regression needs to be repeated for every bank included in the list. The results needed for the next

step of the analysis are the average abnormal return coefficients for each bank i (AARi), which are in

this model the dummy coefficients (γi).

AARi = γi (4)

To ensure the robustness of these results this regression is solved for several times per bank as the

coefficients might be dependent on some of the assumptions made.

The first assumption concerns the time frame of the market model. The use of dummy coefficients on

top of the market model allows that the period of the events itself is included in the market model (as

well as periods before or after it) (Binder, 1998). This is important, since the market sensitivity might

have changed during the crisis. Sandoval and De Paula (2010) show that in times of higher market

39

volatility, there are also strong correlations, meaning that in times of crises the correlation grows

stronger. This is why I used two time frames for this study to check whether the results are dependent

on changing correlations (and therefore coefficients). The first time frame starts at the beginning of

2006. The second timeframe starts on 18 November 2008 so that the time frame is exactly three years

long. However, comparing these two time frames was only possible or necessary for the stock returns

since the sample of CDS returns only starts mid 2008.

A second type of assumption concerns the way the events are chosen. As explained in 2.1.2 The

Greek crisis: the chronology of the crisis (supra, p.8), the choice of the events was based on a

timeline of the Financial Times, where some less important or contradicting events have been deleted

while some other important events, which were not yet included, were added. The outcome of this

research can be dependent on the inclusion or exclusion of some events. Hence, I have used several

types of events. First of all, the strong events which should have had a major impact. However, I

identified only nine of them, meaning that there will probably be a higher variance due to a smaller

number of observations. Next, there is also a group of 29 events (9 “strong” + 20 “normal”) as

described in 2.1.2 The Greek crisis: the chronology of the crisis (supra, p8). As the inclusion of an

extra event changes the event dummy, each regression needs to be repeated twice (once for each event

selection). So for the whole analysis, I have worked with both groups (further referred to as “strong”

and “all” events) to identify whether the results are dependent on the choice of events.

Once the events were set, some assumptions or decisions about the event windows remained. In order

to keep the horizon as broad as possible, I chose to use several event windows to compare the results.

When choosing an event window, there is always a tradeoff between a big window so that the longer

term effects can be measured and a smaller one so that there is less influence of other events

happening in between. In general the sign as well as the magnitude of the returns are very volatile,

which might indicate that events usually have a short term effect. Moreover, the events are sometimes

really close to each other so that the event windows can overlap, especially when using big event

windows. Therefore, I have kept them quite short in general. The following ones are being used: [-

1,1]13

, [0], [0,1] and [-3,3]. For the CDS returns, I also included [0,3] and [0,5] as these seemed less

volatile. Dependent on the number of banks with significant coefficients for these event dummies, I

will decide which event window to use in the next step. If a day is part of an event window, it needed

to be put into the dummy series where “1” indicated an expected positive abnormal reaction (due to

positive news) and “-1” a negative abnormal reaction (due to negative news) for the stock return data

13

[-1,1] means that the day before the event (-1), the day of the event (0) and the day after the event (1) are

included as a dummy. The same logic applies to the other event windows.

40

(for the CDS return the signs are different)14

. When events interfere with each other’s event window,

the latest event takes over starting from day 0. The days in between are averaged so that the days

belonging to the event windows of two negative or positive subsequent decisions still have values “-1”

or “1” and those of a negative and positive interaction result in a “0”. That way, each different window

will result in a different event dummy requiring to repeat the regressions per event window.

One of the things I have not tested is whether the results of these regressions are dependent on the

chosen market index and on the choice between an international or national index. However, King

(2009) wrote that national stock market indices are very correlated with each other (and therefore also

with the international aggregation) and that this correlation became even bigger (95-98%) during the

crisis period. Therefore, it did not seem necessary to test different market indices.

4.2.2 Time split dummies before and after the stress test results of

2010

In order to test whether the reaction of the market has changed due to the publication of the stress test

results of 2010, I have split the event dummies into two separate dummy series, one with the events

before July 2010 (Dbefore,t) and one with those after (Dafter,t). These two different dummy series were

made (with the same event windows as above) to measure whether the AARs changed and whether

this change (investigated in the next step) originates from a change in the relationship with the bank’s

exposure. The dummies were then regressed for several scenarios of both the “strong” and “all” event

series, with time frames starting from 2006 and from 2008 and this for several event windows.

Rit = αi+ βi*Rmt + γbefore,i*Dbefore,t + γafter,i*Dafter,t + εit (5)

While the not-time split regressions result in only one AAR per bank per regression (and therefore 51

AARs for all banks), these will result in two AARs for each regression (γbefore,i and γafter,i). This results

in a series of 102 (2*51) AARs.

Further on, I will refer to this regression and the results of this regression as the “time split” regression

and AARs. The results coming from equation 3 will be referred to as the “not-time split” ones. These

regressions will need to be repeated several times based on the different assumptions which will be

grouped in scenarios.

So in general, the first step of the methodology includes a set of regressions in order to calculate the

AARs. For each different dummy, these regressions need to be repeated 51 times in order to get the

14

Next to this, I also tried with two separate dummy series, one for the negative events and one for the positive

events as it is possible that positive events had a different impact than negative events. However, this was not

very successful as this diminishes the number of event observations in the series leading to a bigger variance and

therefore less reliable coefficients.

41

AAR (or two AARs for the time split dummies) of each bank. In order to verify the robustness of the

results, regression 3 needs to be repeated for all 51 banks for each “combination of assumptions”

(events group, time frame, event window and time split or not-time split dummies) as these

combinations result in a different dummy to be included. These “combinations of assumptions” will be

further referred to as “scenarios” where each scenario represents a different combination. Table 7

gives an overview of the different scenarios. For each of these scenarios, regressions will need to be

repeated for both the time split and not-time split dummies.

Potential Scenarios

Event group Time frame

Event window

1 All (29) 2006-2011

[-1,1]

2 [0]

3 [0,1]

4 [-3,3]

5 2008-2011

[-1,1]

6 [0]

7 [0,1]

8 [-3,3]

9 Strong (9) 2006-2011

[-1,1]

10 [0]

11 [0,1]

12 [-3,3]

13 2008-2011

[-1,1]

14 [0]

15 [0,1]

16 [-3,3] Table 7: Different scenarios to be tested (source: author’s selection)

42

4.3 STEP 2: Regression of the average abnormal returns

As stated before, the main goal of this second step (and from this research in general) is to find the

relationship between the banks’ AAR(s) on a Greek event date and their exposure. The first step of the

methodology was a regression of every bank’s returns on the market index and event dummies

(equation 3 and 5). This results in a list of 51 AARs (the dummy coefficients, the γi’s), or 102 AARs

in case of the time split, per scenario which should now be further regressed on the exposure of the

banks and some control variables. However, only those scenarios (combinations of event type, event

window and time frame) which resulted in a reasonable amount of significantly different than zero

coefficients15

were further regressed. This “reasonable” amount of significant coefficients is quite

subjective. For the not-time split dummies, I aim to get at least 50 percent significant coefficients.

However, in order to stay complete and consistent (so that I can compare the different scenarios), I

might make some exceptions. For the time split series, the amount of significant coefficients might

differ between the AARs before and those after the stress test. In order to stay consistent and be able to

compare the results of these “time split” AARs with the “not-time split” ones and further results, I

chose to use the same scenarios for the time split as those selected for the not-time split series.

Now more practically, the AAR(s) of all banks will be regressed on the Greek exposure ratio

(GEXPR) and a combination of control variables, i.e. the Tier 1 capital ratio (T1CR), the size

(In(TA)), the funding ratio (dep/TA), the asset structure ratio (loans/TA), the country dummies (either

PIIGS or Greece and PIIS) and possibly interaction variables (Greece*GEXPR and T1CR*GEXPR).

Since it is uncertain whether all these control variables need to be used, several series of variables will

be tested. These regressions need to be repeated for each series of AARs (resulting from the different

scenarios).

The first series which will be tested are those without country dummies nor interaction terms (equation

6).

i

i6

i

i5i4i3i21i

TA

loans*c

TA

dep*cTAln*cT1CR*cGEXPR*ccAAR (6)

However, since the PIIGS might experience an extra big abnormal reaction due to contagion, the

PIIGS dummy is added in the next regressions (equation 7).

15

When talking about results which are “significantly” different than zero in this research, this is always on the

10 percent level, unless stated differently.

43

i7

i

i6

i

i5

i4i3i21i

PIIGS*cTA

loans*c

TA

dep*c

TAln*cT1CR*cGEXPR*ccAAR

(7)

In equation 8, the PIIGS dummy is replaced by the PIIS dummy since a dummy for Greece is included

separately.

i 8i7

i

i6

i

i5

i4i3i21i

PIIS*cGreece*cTA

loans*c

TA

dep*c

TAln*cT1CR*cGEXPR*ccAAR

(8)

Now, as can be seen in the correlation table 5, there is a high correlation between GEXPR and the

Greece dummy. This is because the Greek banks on average have more than 40 times more exposure

to Greece than the other European banks (based on the sample of the 51 banks included in the stock

return research). Next to the Greece dummy which measures an influence independent of the exposure

size, the effect of the Greek exposure can be dependent on the condition whether it is a Greek bank or

not. In order to check this, an interaction term is added in equation 9, as described by Brambor et al.

(2006). As the Greece dummy is a dichotomous variable, the interpretation of the coefficients remains

relatively easy. c2 will show the effect of the Greek exposure on the non-Greek banks. c9 will show the

difference in effect between the non-Greek and the Greek banks so that c2+c9 is the coefficient of the

Greek exposure for the Greek banks. C7 remains the part of the AAR for Greek banks which is

independent of their exposure (or any other control variable) (Brambor et al, 2006).

i i9 i 8i7

i

i6

i

i5i4i3i21i

GEXPR*Greece*cPIIS*cGreece*cTA

loans*c

TA

dep*cTAln*cT1CR*cGEXPR*ccAAR

(9)

As clarified in 2.3 Factors influencing the risk profile of a bank, the capital ratio might also

influence the relationship between the AARs and the GEXPR since a fiercer reaction per unit of

exposure can be expected for banks with a low capital buffer. Hence, in equation 10 an interaction

term between T1CR and GEXPR is included following Brambor et al. (2006).

44

i i 9 i 8i7

i

i6

i

i5i4i3i 21i

GEXPR*T1CR*cPIIS*cGreece*cTA

loans*c

TA

dep*cTAln*cT1CR*cGEXPR*ccAAR

(10)

The interpretation of these coefficients is not as simple as for an interaction term with a dummy like

equation 9. Due to the interaction term, the effect of the capital ratio is now not just limited to c3. By

applying equation 11, we can calculate the marginal effect of the Greek exposure on the AARs

(δGEXPR

δAAR) (Brambor et al., p. 72). This marginal effect is dependent on the value of T1CR and will

be calculated for the following values: the mean of T1CR (in the sample of 51 banks), the mean ±

standard deviation of T1CR and the mean ± 2*standard deviation of T1CR. In order to find out

whether these marginal coefficients are significant, I need to calculate the standard error term of this

marginal effect (σδAAR/δGEXPR). Based on equation 12 (Brambor et al., p. 70), the standard error term

will be computed for the same values of T1CR. Once the error term and the coefficient of the GEXPR

are calculated for each of the values, the significance of these marginal effects is known by calculating

the T-value (equation 13).

T1CR*ccδGEXPR

δAAR92 (11)

9292

2

δGEXPR

δAAR c,cCov*T1CR*2cVar*T1CRcVarσ (12)

δGEXPR

δAARσ

δGEXPRδAAR

T (13)

For the series where there is no split for the stress test, there was one AAR for each bank. This means

that one value for each control variable per bank is needed. Hence, I have used the averages of the

values reported at the end of 2009 and 2010. For the time split series, there are two AARs per bank:

one for the events prior to July 2010 and one for those after that date. The values reported are normally

a snapshot of the bank at the end of the year. So the values of end 2009 will be used for the first series

of AARs and the second series of AARs will be related to the values reported at the end of 2010.

45

With the AARs from the time split regressions, there are two options. (1) The first option is a general

regression of all the AARs (so both before and after the stress result publication) on their variables.

Since each bank has two AARs (one before and one after July 2010) with different values for the

exposure and control variables, this results in 102 (51*2) observations, so double the amount of

observations than the regressions without the time split. These regressions will be done in order to

check the robustness of the regressions without a time split where the averages of 2009 and 2010 are

used as values for the variables. Since there are twice as many observations, the reliability of the t-test

should be higher. (2) The second option, which is actually the main purpose of this time split, is to

regress both time frames separately in order to get a series of coefficients for the AARs before and one

for the AARs after the publication of the stress test in 2010. The two series of coefficients coming

from these regressions will then be compared to see whether there has been a change in the reaction of

the markets concerning the relationship of the banks’ exposure and the AARs on Greek events (the

c2’s of each series, in combination with the c9 of equation 9 and 10).

These regressions have been performed both on all the AARs as well as only on those which were

significantly different from zero. For the regressions based on the significant AARs only, there will be

fewer observations, which should might in a higher variance and less significant coefficients. This is

also why there should be enough significant AARs for the scenarios used in this step.

So to conclude, this step of the methodology will regress equations 6-10 based on the series of AARs

from step 1 which had enough significant results. For the time split AARs, there will be three different

series of regressions: one based on all the AARs, one for the AARs from before July 2010 and one for

those after. All of these regressions will be done for all the AARs and repeated for only the significant

AARs in order to test the robustness of the results.

Exhibit 5 provides an overview of this section on the methodology.

46

Exhibit 5 Visual summary of methodology (source: author’s drawing)

47

5. Results

While the previous section describes the methodology of this research, this section will describe the

results forthcoming by following this methodology. In this chapter there is a split between the results

for the stock returns and those for the CDS returns. For the stock returns, there is a section about the

results for the not-time split AARs and one for the time split AARs. For the CDS returns there is no

time split as the sample only starts in 2008. Therefore, there is only one section for the CDS returns.

5.1 Stock return

5.1.1 STEP 1: market model and average abnormal returns

The general conclusion is that the events seem to be well chosen as there are a lot of significant AARs

in most event windows, independent of the events group or the time frame. On top of that, the AARs

are in general positive, as expected since this means that on average an extra positive return has been

measured when the event dummy equals “1” and an abnormal negative return for a negative event (-

1). Therefore, the first step can be called “successful” in such a way that the second step of the

methodology can start.

The AARs for the stock returns for each bank separately can be found in appendix 2.1.

o Not-time split dummies

First of all, I looked at the reliability of the AARs generated for each scenario (fourth column in table

8 or bars in exhibit 6). In general, it is clear from exhibit 6 that the AARs of the scenarios which

include event window [-3,3] always fall below the barrier of 50% significance. This is logical: if the

events are chosen well, during the days before the event, no abnormal reaction in line with the event

should be identified, making the abnormal returns in these event windows more diversified and less

significant). Therefore, these coefficients were not further regressed in step 2, as well as not used for

the time split regressions (in order to be as consistent as possible)16

. There is only one scenario based

on the event windows [-1,1] and [0,1] which did not generate enough significant coefficients (only 45

percent and 47 percent). However, since almost 50 percent of the coefficients were significant and in

order to stay consistent with the rest of the scenarios where these event windows did make it to the

barrier, these two scenarios are not deleted and will be further used in step 2.

16

Similar to the regressions with twenty events and those where there were two separate dummy series: one for

the positive and one for the negative events. These regressions did not have enough significant coefficients

either.

48

As a consequence, after elimination of the [-3,3] event window, step 1 resulted in nine scenarios of

which the AARs are further regressed in step 2. As can be seen from the graph and the table, the

averages of the AARs for event window [-1,1] are in general lower (and less significant) than for the

other two event windows. This is also because on the day before the event (-1) there should not yet be

much news about the event following the next day. Event window [0] is clearly the strongest. This

also makes sense as the strongest reaction is to be expected on the day itself.

The averages of the AARs displayed in column 2 of table 8 can be interpreted as follows: (for

example for row 10 in the table) for the 9 strong events, based on a market model with data from 2006

until 2011, averaged over all banks and all events, a positive (negative) event (on day 0) invoked an

extra gain (loss) of 2.0 percent on top of the “normal” market reaction.

SUMMARY STATISTICS OF THE NOT-TIME SPLIT AARS OF THE STOCK RETURNS

Event group

Time frame

Event window

Average AAR

Std. Dev. AAR Number of

signif AARs (as a % of total)

Further regressed in step 2

All (29) 2006-2011

[-1,1] 0,7% 0,6% 28 (55%)

[0] 1,1% 0,9% 34 (67%)

[0,1] 0,8% 0,7% 29 (57%)

[-3,3] 0,3% 0,3% 23 (45%)

2008-2011

[-1,1] 0,6% 0,6% 26 (51%)

[0] 1,1% 0,8% 33 (65%)

[0,1] 0,7% 0,7% 26 (51%)

[-3,3] 0,3% 0,2% 20 (39%)

Strong (9)

2006-2011

[-1,1] 0,9% 1,0% 27 (53%)

[0] 2,0% 1,4% 32 (63%)

[0,1] 1,2% 1,3% 29 (57%)

[-3,3] 0,3% 0,3% 14 (27%)

2008-2011

[-1,1] 0,9% 1,0% 23 (45%)

[0] 1,8% 1,4% 29 (57%)

[0,1] 1,0% 1,3% 24 (47%)

[-3,3] 0,3% 0,3% 9 (18%)

Table 8: Summary statistics of the not-time split AARs of the stock returns (source: author’s calculations

based on data from Datastream)

From the table 8 and exhibit 6, two first conclusions about the assumptions can already be derived.

(1) First, the time frame does not seem to make a big difference. When comparing the results of 2006-

2011 to those of 2008-2011 (based on the same event window and event group) the average AARs are

very similar. (2) Second, the event group does make a substantial difference. On average, the AARs of

49

the strong events are 56% higher than those of all events (even 63% if not considered the [-3,3] event

window). This confirms my intuition as it seems logical that the strong events had a much bigger

impact.

Exhibit 6: AARs of not-time split sample and their significance (source: author's calculation based on data

from Datastream)

To construct exhibit 7, the banks are divided into different categories depending on their origin

(PIIGS and non-PIIGS, PIIS, Greek and Non-Greek). Subsequently, the averages of the AARs for

these categories are calculated for each scenario. As can be seen on the exhibit, the Greek banks

clearly experienced the biggest abnormal returns. Averaged over all scenarios, the AARs of the Greek

banks (2.9%) is 3.4 times higher than that of non-Greek banks (0,7%). The same logic applies to the

banks from the PIIGS which have on average an AAR (1,6%) twice as large as the non-PIIGS banks

(0.8%). Next, the average of the AARs of the banks from the PIIS is comparable to the average of all

banks, indicating that (even without Greece) the banks from the PIIS have a higher average AAR (1%)

than the non-PIIGS banks (0.7%). This can be caused by several factors like a riskier profile, more

Greek exposure or contagion. This will be further investigated in the next step.

It can also be seen from this exhibit that the AARs based on the event dummy with the strong (9)

events (right side of the graph) are higher than those based on all (29) events (left side).

50

Exhibit 7: Averages of the AARs for the different scenarios for several categories of banks depending on

their origin (source: author’s calculations based on data from Datastream)

o Time split dummies before and after the stress test results of 2010

As mentioned above, it is harder to draw a line for the number of significant coefficients required for

this time split since, as one can see in Table 9, there are a lot less significant coefficients in the period

before the stress test. Therefore, I will use the same 12 scenarios for the time split as for the not-time

split results. As more than (or almost) 50 percent of the coefficients after the stress test are significant,

it looks worth investigating these scenarios. Again, the day before the event (-1) does not seem to have

a lot of effect, just as the day after the event. So it looks like, in general, the biggest effect of the event

is on the day itself.

On exhibit 8, one can see that the AARs after the publication of the stress test were, in general, bigger

than those before. Step 2 op the methodology can answer the question whether this is caused by a

stronger impact of the Greek exposure or not.

51

SUMMARY STATISTICS OF THE TIME SPLIT AARS OF THE STOCK RETURNS

Events group

Time frame

Event window

Average AAR Standard Deviation

Number significant AAR (as % of total)

before after before after before after

All (29) 2006-2011

[-1,1] 0,5% 0,8% 0,007 0,006 13 (25%) 31 (61%)

[0] 1,0% 1,3% 0,011 0,009 22 (43%) 32 (63%)

[0,1] 0,4% 0,9% 0,005 0,009 9 (18%) 29 (57%)

2008-2011

[-1,1] 0,4% 0,7% 0,007 0,006 12 (24%) 27 (53%)

[0] 0,9% 1,2% 0,011 0,009 16 (31%) 35 (69%)

[0,1] 0,3% 0,9% 0,005 0,009 6 (12%) 26 (51%)

Strong (9) 2006-2011

[-1,1] 0,4% 1,4% 0,008 0,014 10 (20%) 30 (59%)

[0] 1,4% 2,3% 0,017 0,017 16 (31%) 36 (71%)

[0,1] 0,4% 1,7% 0,010 0,017 11 (22%) 31 (61%)

2008-2011

[-1,1] 0,3% 1,4% 0,008 0,014 8 (16%) 28 (55%)

[0] 1,0% 2,2% 0,015 0,017 11 (22%) 33 (65%)

[0,1] 0,3% 1,6% 0,010 0,017 8 (16%) 28 (55%) Table 9: Summary statistics of the time split AARs of the stock returns (source: author’s calculations based

on data from Datastream)

Exhibit 8: average AARs "before" versus "after" (source: author’s calculations based on data from

Datastream)

52

5.1.2 STEP 2: regression of the AARs

In general, I have a lot of results as I repeated each regression several times for all the different

assumptions in order to check whether any of the assumptions has a defining impact on the results.

Hence, this resulted in more than a thousand coefficients which need to be interpreted. In order to not

overload the reader with an overdose of coefficients and results, only a limited selection of these

results will be shown, illustrating the essence of the outcome. In appendix 3 the whole range of results

is provided.

First, I will discuss the overall results. These confirm the first main hypothesis that the size of the

exposure to Greece has a positive impact on the size of the excess reactions on a Greek event day. On

top of that, they provide an answer to the country and control variable hypotheses. Next, I will show

that the event window, nor the time frame of the market data matters to the robustness of the results.

The scenarios with a different event group though, show (slightly) different results as the nine strong

events show more powerful coefficients. In the third part, I will give the results which verify

hypothesis 1b that the relationship between exposure and AARs has strengthened after the stress test

publication in July 2010. After that, I will display some extra research about the robustness of the

results. To finish, there is a small conclusion about these results.

o Overall results

As described in the methodology section (supra, p.42- 44), five equations with different compositions

of variables have been solved and this for twelve scenarios (based on event window, time frame and

event group). Table 10 (infra, p.56) shows the average of the coefficients found for each of the twelve

scenarios as well as the proportion of scenarios for which these coefficients were significantly

different from zero (still at the ten percent level). As will be shown further on, the coefficients are

quite similar and are mainly independent of the underlying assumptions. Therefore, it is justified to

just show the averages in this section and make some initial conclusions based on them (with the

underlying coefficients still in mind). That way things can be kept as clear and concise as possible. As

these regressions are based on the not-time split variables (and therefore only one AAR per bank),

they only have 51 underlying observations. As a result of this rather small sample, the coefficients are

often not very significant (at the 10% level). Hence, the acceptance of the hypotheses is not only based

on the number of significant coefficients; so, if a variable is consistent in sign for all scenarios

(although mainly insignificant), this could still lead to the acceptance of the hypothesis.

The average effect of each factor on an “average” bank can be calculated by multiplying the

coefficients of table 10 (infra, p.56) with the average values of these factors, which can be found in

table 4 (supra, p.34). For a particular bank, these coefficients should be multiplied with the values

53

found in appendix 1. As the country variables are dummies, the effect of these can be retrieved just by

looking at the coefficients themselves. If a bank’s domestic country is Greece, the coefficient of the

Greece dummy represents the extra abnormal return (multiplied by “-1” in case of a negative event) an

average Greek bank encounters.

First, I will focus on the findings about the Greek exposure and country variables. I will describe the

results from table 10 (infra, p.56) for each equation/column separately in the order of their

complexity, starting with equation 6 and ending with equation 10. As the first equations are of less

importance, they will get less attention. Later on, I will have an overall look at the control variables.

Equation 6 (with its results in the first column of table 10), is the simplest one since it does not

include any country nor interaction terms. Based on this equation, the initial (and main) hypothesis is

confirmed as the coefficients for the GEXPR are significant and positive for all scenarios.

Adding the PIIGS dummy like in equation 7 does not influence this finding. However, the GEXPR

coefficients become almost twenty percent smaller. The PIIGS coefficients are always positive and

they are significant for half of the scenarios, meaning that banks from these countries experience an

abnormal reaction independent of their exposure.

For equation 8, the PIIGS variable is split into a Greece and a PIIS dummy. The consequence of this is

substantial. First, the average Greece coefficient is about 3 times higher than that of the PIIS dummy

(and 2.5 times higher than the average PIIGS coefficient from the previous equation). These

coefficients should be interpreted as follows: on a Greek event, a Greek (PIIS) bank undergoes an

extra abnormal return of 1.2% (0.4%) compared to non-PIIGS banks. While the coefficients of the

PIIGS were only significant for half of the scenarios, those of Greece are significant for two third and

the amount of significant PIIS coefficients dropped to 25%. On top of that, the coefficients of the

GEXPR dropped by 35% (compared to the second column). As a result, it seems that the exposure is

not the main driver of the AARs for Greek banks as the average exposure level explains only 13% of

these average AARs. This can be explained by the fact that the Greek banks hold a lot of exposure,

which is why omitting this Greece dummy causes a biased view. Although not significant for all

scenarios, the GEXPR coefficients are positive for all scenarios resulting in the acceptance of the main

hypothesis.

In conclusion, based on these first three equations, it seems useful to add the country dummies as the

AARs are not only driven by the exposure of a bank, but also by the nationality of the banks. As a

consequence, both hypothesis 6a (contagion effect to PII(G)S) and hypothesis 6b (independent

influence of Greece) could be accepted based on this not-time split sample.

54

In the fourth column, the results of equation 9 are shown. The purpose of this equation is to distinguish

between Greek and non-Greek banks (in order to check hypothesis 6c). This equation can partly

explain why the Greek exposure in the previous equation is only significant for a minority; i.e. a split

for effect of the GEXPR based on the nationality of the banks seems to be justified and reduces the

variance of the coefficient, thereby increasing the significance. As can be seen from the table, the

average exposure coefficient for the non-Greek banks (0,386) is substantially higher (on average ten

times) than that of Greek banks (0,037). Hence, this confirms hypothesis 6c which states that the

influence of the Greek exposure is different for Greek banks than for other banks. The non-Greek

banks show significant GEXPR coefficients for all the scenarios, while those of the Greek banks are

only significant in one third of the cases. Notwithstanding the fact that the effect per unit of exposure

is smaller, this does not mean that the total effect of this exposure on Greek banks is also smaller. To

come to the overall size, the GEXPR coefficient needs to be multiplied by the size of the Greek

exposure. While the coefficient for Greek banks is on average ten times smaller, they have an average

exposure which is forty times bigger than the other European banks. This induces that Greek banks

still have an abnormal reaction four times higher than the other banks caused by the size of their

exposure. On average (in this equation), the Greek exposure factor explains 16.3% of the average

AARs (which is 0.8%) for the non-Greek banks, while it explains 13.5% of the average AARs (which

is 2.9%) for the Greek banks. As a result, hypothesis 1 should be accepted as all exposure coefficients,

are positive. Another interesting finding concerning this equation is the fact that the coefficient for the

Greece dummy is higher than in the other equations. This means that for Greek banks on average 75%

of their AARs is caused by the fact that they are Greek. So next to the exposure, there are major forces

driving the bank’s value due to factors like the bad economic conditions, changing government

regulations, etc. The PIIS ratio is also significantly positive in more than 90% of the cases. So next to

the Greek banks, the PIIS banks are also subject to an external (independent of the exposure size)

influence, which is most likely caused by the fear of contagion. On average, 52% of the AARs of the

PIIS banks can be explained by this external factor. In conclusion, this equation verifies hypotheses 1,

6a, 6b and 6c for this sample.

The coefficients for equation 10 with the capital interaction term are quite similar to those in the third

column. The Greek banks still experience a big influence due to other factors than their exposure.

However, what is most important in this equation is the interplay between GEXPR and T1CR. In order

to quantify this interaction (and come to the different GEXPR coefficients and their significance

dependent on the T1CR value), equation 11 through 13 needed to be calculated. There are two

important observations. (1) The first one is a bit surprising as it appears that the significance of the

GEXPR did not change along with the capital ratio. Either a scenario shows significant GEXPR

coefficients for all T1CR value, either all GEXPR coefficients were insignificant (see the underlying

55

coefficients on table 12, infra p.60). So the significance does not seem to rise substantially with a

decreasing value of T1CR as was initially expected. (2) The second finding confirms the expectations:

the size of the GEXPR coefficient is dependent on the capitalization of the banks. As expected, banks

with a high capital ratio are less sensitive (per unit of exposure) to these events as they have more

buffer to compensate for. The difference in value of the GEXPR coefficients between the highly

capitalized banks (mean + 2*standard deviation) and the low capitalized banks (mean – 2*standard

deviation) is on average 37%, meaning that highly capitalized banks react 37% less fiercely per unit of

Greek exposure on an event than do those with only a small capital ratio. So, hypothesis 2b (that there

is a negative correlation between the size of the GEXPR coefficient and the capitalization of a bank) is

also verified based on this sample.

To end with, I want to take a look at the control variables. It is best to look at the last three columns as

they have less omitted variables. First, the capital ratio (T1CR) does not seem to play a big role. The

average of the coefficients alternates between positive and negative and is not significant. As a

consequence, the influence of T1CR is only minor if not non-existent. Therefore, this sample rejects

hypothesis 2a. Second, the size variable is positive for all equations and significant for a small

majority. So, the larger the bank, the bigger the abnormal reaction on the day of an event. Hence,

hypothesis 3 is accepted. Next, the underlying coefficients of the deposit-to-TA ratio, of which the

average is positive as displayed in the table, are alternately negative and positive as well as

insignificant. So, based on this sample the funding ratio is not a determining factor and hypothesis 4 is

rejected. The last control variable added is the loans-to-TA ratio. This factor shows both positive and

negative coefficients depending on the scenario and is insignificant leading to the conclusion that it

does not substantially affect the AARs on a Greek event date. Hence, hypothesis 5 can be rejected.

To conclude, hypothesis 1a, 2b, 3, 6a, 6b and 6c are accepted (or at least, could not be rejected)

based on this sample. Hypothesis 2a, 4 and 5 on the other hand, are rejected. Hypothesis 1b cannot

be tested based on this sample but will be looked upon further on.

The results of the last two equations/columns imply the results of the previous ones, as they contain

the same (and some extra) variables. So, the findings of these equations can provide the main

conclusions of this research. Therefore, in the rest of this section, I will mainly focus on the results of

these equations. In order to show only the most relevant information, the coefficients of the control

variables of equation 10 (column 5) will not be displayed in the next tables. So, only the results for the

GEXPR in interaction with the capital term will be shown. This is justifiable since, in general, the

coefficients of the control variables of this equation are quite consistent with those of equation 9 (as

can be seen from table 10) and they are not the main focus of this research.

56

Equation 6 Equation 7 Equation 8 Equation 9 Equation 10

AAR % signif AAR % signif AAR % signif AAR % signif AAR % signif

Constant -0,007 0% -0,014 0% -0,009 0% -0,009 0% -0,011 0%

GEXPR 0,150 100% 0,122 100% 0,080 42%

T1CR -0,055 17% -0,009 0% -0,028 0% 0,016 0% 0,002 0%

ln(TA) 0,002 58% 0,002 83% 0,002 50% 0,002 67% 0,002 42%

dep/ TA 0,011 0% 0,017 33% 0,013 0% 0,002 0% 0,013 0%

loans/TA 0,042 17% 0,003 0% 0,001 0% 0,002 0% 0,001 0%

PIIGS 0,005 50%

Greece 0,012 67% 0,021 100% 0,013 58%

PIIS 0,004 25% 0,006 92% 0,005 17%

GEXPR for

Not-Greek banks

0,386 100%

Greek banks 0,037 33%

GEXPR for

banks with

T1CR =

mean - 2*stdev

0,103 50%

mean - stdev

0,097 50%

mean

0,090 50%

mean + stdev

0,084 50%

mean + 2* stdev

0,077 50%

R² 55% 58% 61% 68% 61%

≤ 1/3 of the scenarios is significant

≤ 2/3 and > 1/3 of the scenarios is significant

> 2/3 of the scenarios is significant

Table 10: Summary table with average (and proportion of significant) coefficients of equations 6 to 10 based on the AARs from the not-time split sample. (source:

author’s calculations based on data from Datastream, Bankscope and EBA website)

57

o Comparison of different scenarios

For table 10 (supra, p.56) the averages of the coefficients of the twelve scenarios were calculated.

Relying on these averages to draw conclusions, is only meaningful in case the coefficients are

consistent in sign (and preferably in size as well). In order to find to what extent the assumptions

matter, table 11 (infra, p.59) displays the individual coefficients for each scenario for equation 9. This

is still based on the same sample of 51 AARs from the not-time split dummies. These different

scenarios are included in order to check the robustness of the results to assumptions like the event

window, the time frame and the event group. In this section, I will show that the only assumption that,

until now, has an impact is the whether the analysis is based on only the strong events or on all.

In general, the findings are that the choice of event window and the time frame do not have a big

influence. However, the one-day event window ([0]) has a lower R² and when looking at the

coefficients of the 9 strong events, this event window is the only one which did not display a

significant GEXPR coefficient for the Greek banks. Nevertheless, as the coefficients are still similar in

size, the difference is negligible. Next, whether the market model is based on a sample starting from

2006 or from 2008 hardly makes any difference.

While the results do not depend on the event window or the time period, the event group obviously

plays a more important role. This could already be seen from table 8 (supra, p.48) where it was shown

that the AARs from the strong events were more than 50% higher than those of all 29 events. This

implies that there will also be a difference in underlying forces driving these AARs. From exhibit 9

(infra, p.60) and table 11, one can see that the biggest difference between both event series is the

effect of Greek exposure, both in size and significance. A bit surprising is that the investors of the

Greek banks “only” (based on the significance of the coefficients) take the Greek exposure into

account during strong events. Nevertheless, for the smaller events, this does not mean the Greek

banks’ valuation is not affected by these Greek events as the Greece dummy largely drives the AARs.

As already mentioned above, this dummy explains on average 75% of the abnormal reaction for Greek

banks. Even though the coefficient of the Greece dummy is 20% bigger in size during the strong

events, this factor is in relative terms more important during the smaller events as 84% of the AARs is

caused by this, while it only applies for 66% of the abnormal returns on the strong event dates. The

Greek exposure on the other hand, explains on average 7% of the AARs during all events, while

during the strong events, 19% of the AARs can be explained by the Greek exposure. So in conclusion,

during the smaller events, the Greek banks’ valuation is in relative terms more driven by the fact that

they are Greek, while the Greek exposure only has a minor influence. During the major events, the

Greek external factor is still the most important one, although its relative impact drops, while the

Greek exposure becomes more impactful (compared to all 29 events).

58

On exhibit 9, it is once again shown that the coefficients of the Greek exposure for the Greek banks is

substantially smaller than that of the non-Greek banks (hypothesis 6c).

Table 12 (infra, p.60) shows the coefficients of the GEXPR for the different levels of T1CR for all

twelve scenarios (equation 10). Exhibit 10 (infra, p.60) helps visualizing these. Even though there are

some differences between the different event windows and time frames, it is obvious that the biggest

difference in coefficients is again caused by the choice of event group. On the graph, the two groups

can be clearly distinguished. In combination with table 12 it is clear that, for the strong events (the

upper group on the graph), the GEXPR coefficients are significant for all levels of T1CR, while they

are not when the smaller events are added. Independently from the chosen scenario, the graph on

exhibit 10 clearly shows a decreasing slope, once again confirming hypothesis 2b that the Greek

exposure coefficient is negatively related to the T1CR.

Based on both tables, it seems acceptable to conclude that neither the event window, nor the time

frame affect the results substantially. The event group however, does; for the strong events, the

exposure plays a more important role in affecting the bank’ valuation. As the purpose of this

research is to define the effect of the exposure to a country in trouble on the bank evaluation, I will

further mainly focus on the findings based on the strong set of events.

59

REGRESSION COEFFICIENTS AND SIGNIFICANCE OF EQUATION 9

All events Strong events

2006-2011 2008-2011 2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1] [-1,1] [0] [0,1] [-1,1] [0] [0,1]

Constant -0,006 -0,021 -0,008 -0,003 -0,013 -0,005 -0,004 -0,029 -0,002 0,000 -0,020 0,004

GEXPR (not-Greek bank)

0,210** 0,338** 0,287** 0,211** 0,333** 0,289** 0,425** 0,412** 0,622** 0,434** 0,436** 0,640**

GEXPR (Greek banks) -0,002 0,009 0,000 -0,006 0,003 -0,006 0,067** 0,087 0,086** 0,060* 0,070 0,074*

T1CR 0,005 0,049 0,003 0,006 0,046 0,005 -0,009 0,091 -0,044 -0,009 0,094 -0,043

ln(TA) 0,001* 0,002** 0,001** 0,001 0,002* 0,001 0,002* 0,005** 0,002* 0,001 0,004** 0,001

dep/TA 0,001 0,005 0,004 0,002 0,002 0,005 -0,002 -0,001 0,001 -0,001 0,000 0,002

loans/TA 0,005 0,013 0,004 0,003 0,007 0,002 -0,002 0,007 -0,004 -0,005 -0,001 -0,010

Greece 0,018** 0,020** 0,019** 0,018** 0,020** 0,019** 0,021** 0,023** 0,023** 0,022** 0,025** 0,024**

PIIS 0,002* 0,006** 0,003* 0,003** 0,006** 0,003* 0,004* 0,011** 0,005 0,005** 0,012** 0,006*

R² 82% 57% 74% 83% 55% 75% 75% 51% 68% 77% 52% 71%

** significant at the 5% level

* significant at the 10% level

not significantly different than 0 at 10% level

Table 11: Regression coefficients and significance from equation 9 for all 12 scenarios, based on the not-time split sample of 51 AARs. (source: author’s calculations

based on data from Datastream, Bankscope and EBA website)

60

REGRESSION COEFFICIENTS AND SIGNIFICANCE OF EQUATION 10

All events Strong events

2006-2011 2008-2011 2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1] [-1,1] [0] [0,1] [-1,1] [0] [0,1]

GEXPR for

banks with

T1CR =

mean - 2*stdev 0,037 0,068 0,049 0,022 0,062 0,046 0,149** 0,162* 0,229** 0,110** 0,154* 0,153**

mean - stdev 0,033 0,063 0,045 0,021 0,057 0,041 0,139** 0,152** 0,207** 0,109** 0,143* 0,150**

mean 0,030 0,058 0,041 0,021 0,051 0,037 0,128** 0,142** 0,186** 0,108** 0,132** 0,148**

mean + stdev 0,026 0,053 0,037 0,021 0,046 0,033 0,117** 0,133** 0,165** 0,106** 0,121* 0,145**

mean + 2*stdev 0,023 0,048 0,033 0,020 0,041 0,029 0,107** 0,123** 0,144** 0,105** 0,110** 0,142**

** significant at the 5% level * significant at the 10% level not significantly different than 0 at 10% level

Table 12: Regression coefficients and significance from equation 10 for the GEXPR for the different levels of T1CR, based on the not-time split sample of 51 AARs.

Significance is calculated based on equation 11, 12 & 13 b. (source: author’s calculations based on data from Datastream, Bankscope and EBA website)

b The coefficients of the control variables are not shown here as they are not substantially different from those in table 10

Exhibit 10: coefficient GEXPR equation 10 (source: table 12) Exhibit 9: coefficient GEXPR equation 9 (source: table 11)

61

o Results before versus after the stress test

The previous part investigated the influence of the Greek exposure on the bank’s valuation over the

entire event period. Based on this sample, most of the hypotheses could be checked. However,

hypothesis 1b concerning the difference in influence of the Greek exposure due to the publication of

these exposures in July 2010 cannot be answered based on the previous sample. Therefore, another

sample with two events dummies was necessary to check whether the disclosure of the individual bank

exposure might have had any informative value and whether this disclosure could have changed the

exposure’s impact during events17

. As described before, these two dummies resulted in two AARs per

bank (one before and one after) which will now be further regressed.

The results of the regressions (of the strong events) on these time split AARs are displayed in table 13

and 14 (infra, p.63 & 64). Based on these, two main conclusions can be drawn. First, the Greek

exposure was already a significant driver before the stress test, though only for non-Greek banks.

Second, after July 2010, the investors relied substantially more on the banks’ Greek exposure to

value them. This can be clearly observed on exhibit 11 and 12 (infra, p. 64) where an upward shift of

the Greek exposure can be noted.

Let’s first have a look at table 13 which displays the coefficients of equation 9 where the interaction

term with the Greece dummy is included. Before the publication of the stress test, the Greek exposure

factor was already significant for the non-Greek banks. The Greek banks however, were not valued

based on their exposure. On top of that, it seems that the Greece dummy already had a significant

impact before the stress test as well. After July 2010, a couple of things seem to have changed. First,

the reaction per unit of Greek exposure became significantly bigger18

(exhibit 11). This also led to

more significant coefficients as well as the fact that the exposure afterwards did matter for the Greek

banks. Based on these observations, hypothesis 1b is verified. Next, the PIIS coefficients might

suggest that the contagion effect before July 2010 was more important than afterwards. Another

finding concerns the control variable deposits-to-asset ratio. While before the stress test, deposits

added more risk, afterwards they had a negative effect on the abnormal returns. A possible explanation

is that, as banks should strive towards a balanced mix of funding sources, more deposits will not

always mean less risk and the optimal liability mix might change over time. A last difference

17

It needs to be taken into account that even if there is a difference in impact of the Greek exposure after July

2010, it is not proven that this is due to the publication (see section 6.Limitations and directions for further

research).

18 For the non-Greek banks, the hypothesis that both series of six coefficients (for the strong events) before and

after are similar, was rejected by a Wilcoxon and a sign test (as the coefficients were not normally distributed) at

the 5 percent level. For the Greek banks, the Wilcoxon test rejected it as well, while the sign test did not.

However, this is a small sample (two times six coefficients) which decreases the power of the test.

62

concerns the size coefficients, which are only significant (and positive, as expected positive) before

the stress test. After, it appears that investors don’t take them into account. Possibly, the investors

started focusing more on other things (like the exposure), so that the relevance of the size at that point

decreased.

Table 14 gives the GEXPR coefficients for equation 10 which included the interaction term with the

capital ratio. In general, I can conclude that before the stress test, there was no significant effect

coming from the Greek exposure. This conclusion is different from the one in the previous paragraph

and table 13 (based on equation 9). This is probably caused by the aggregation (as there is no split

anymore) of the Greek and non-Greek banks, resulting in a bigger standard deviation and as a

consequence, a lower T-statistic. The same trend as in the general (not-time split) regression can be

found regarding the interaction between capital and Greek exposure (also observable on exhibit 12).

Before July 2010, the influence of the Greek exposure is negatively correlated with the capital base,

meaning that banks with a small capital buffer experience a fiercer (even though statistically not

different from zero) effect from their portfolio of Greek obligations. This conclusion also holds for the

period after July 2010 (verifying hypothesis 2b once again). However, here the link is more

pronounced since for all scenarios the banks with the small capital base display significant coefficients

which are larger than the (insignificant) coefficients of the more capitalized banks. From exhibit 12 or

table 14, it is obvious that after the stress test, the impact per unit of Greek exposure has grown

significantly. Averaged over all scenarios and all ranges of capital, the GEXPR coefficients “after” are

2.4 times bigger than those “before” based on which hypothesis 1b can be accepted.

63

REGRESSION COEFFICIENTS AND SIGNIFICANCE FROM EQUATION 9 FOR STRONG SCENARIOS

2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1]

Before After Before After Before After Before After Before After Before After

Constant -0,031** 0,011 -0,093** 0,016 -0,054** 0,044 -0,027** 0,015 -0,051** 0,039 -0,037** 0,046*

GEXPR (Not-Greek bank) 0,343** 0,572** 0,319 0,528** 0,400** 0,773** 0,340** 0,588** 0,365* 0,523** 0,414** 0,763**

GEXPR (Greek bank) 0,011 0,136** 0,088 0,081 0,008 0,144** 0,006 0,127** 0,062 0,084 0,018 0,134**

T1CR 0,044 0,013 0,163 0,106 0,069 -0,107 0,044 0,015 0,110 0,054 0,032 -0,116

ln(TA) 0,002** 0,002 0,006** 0,004 0,003** 0,001 0,001 0,002 0,003* 0,002 0,002 0,000

dep/ TA 0,024** -0,045** 0,072** -0,058* 0,048** -0,042* 0,023** -0,045** 0,049** -0,073* 0,035** -0,033

loans/TA 0,009 0,007 0,026* -0,008 0,012 -0,020 0,006 0,003 0,006 -0,014 0,011 -0,026*

Greece 0,016** 0,024** 0,018 0,029* 0,014* 0,032** 0,016** 0,026** 0,023** 0,032** 0,011 0,032**

PIIS 0,006** 0,002 0,015** 0,011* 0,009** 0,004 0,006** 0,003 0,010** 0,011* 0,006* 0,005

R² 67% 66% 59% 44% 58% 67% 65% 67% 63% 45% 53% 69%

** significant at the 5% level

* significant at the 10% level

not significantly different than 0 at 10% level

Table 13: Regression coefficients and significance from equation 9 for the strong scenarios, based on the time split sample of 102 AARs. The “Before (After)

column” displays the regressions based on the AARs from the event dummy which only included the events before (after) July 2010 (source: author’s calculations

based on data from Datastream, Bankscope and EBA website)

64

REGRESSION COEFFICIENTS AND SIGNIFICANCE FROM EQUATION 10 FOR STRONG EVENTS

2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1]

Before After Before After Before After Before After Before After Before After

GEXPR for

banks with

T1CR =

mean - 2*stdev 0,074 0,326** 0,165* 0,251** 0,102 0,402** 0,070 0,324** 0,140* 0,221** 0,093 0,383**

mean - stdev 0,063 0,297 0,146 0,227** 0,081 0,364* 0,058 0,295 0,123 0,205** 0,079 0,348*

mean 0,051 0,269 0,126 0,204 0,061 0,327 0,047 0,265 0,105 0,189 0,066 0,312

mean + stdev 0,040 0,240 0,107 0,180 0,041 0,289 0,035 0,236 0,087 0,172 0,053 0,276

mean + 2* stdev 0,029 0,211 0,088 0,157 0,021 0,252 0,024 0,206 0,070 0,156 0,039 0,241

** significant at the 5% level * significant at the 10% level not significantly different than 0 at 10% level

Table 14: Regression coefficients and significance from equation 10 for the strong scenarios, based on the time split sample of 102 AARs. The “Before (After)”

column displays the regressions based on the AARs from the event dummy which only included the events before (after) July 2010 (source: author’s calculations based

on data from Datastream, Bankscope and EBA website)

Exhibit 12: strong GEXPR coefficient before vs after equation 9 (source: table 13) Exhibit 11: strong GEXPR coefficient before vs after equation 10 (source: table 14)

65

o Further robustness checks

Two more series of regressions were performed in order to check the robustness of the results.

(1) The first one was the general regression of the series with all 102 AARs collected from the time

split regressions (equation 5). Now, these were further regressed on the control variables of 2009 (for

the AARs from before July 2010) and 2010 (for those after). In contrast to the previous paragraph

where the AARs were regressed separately (once the AARs before and once the AARs after), this

regression does not split them and regresses them all at once. The regressions resulted in a set of

coefficients (see appendix 3.1.2) which are very similar to the general regression based on the not-

time split dummies (see appendix 3.1.1). Nevertheless, there were some differences. However, since

these differences are not substantial regarding the two main hypotheses, the coefficients are not shown

here but the reader can look them up in the appendix. I will scroll through the hypotheses to check

whether they differ from the finding based on the AARs coming from the not-time split.

First of all, main hypothesis 1a concerning the positive link between exposure and the AARs is still

accepted based on this sample (for the strong events and/or the non-Greek banks). The second sub-

hypothesis 1b about the results before versus after July 2010 is based on this sample and can therefore

not be checked. A more interesting finding regards hypothesis 2a concerning the risk T1CR adds or

reduces. While the effect of T1CR was found to be minor or non-existent based on the not-time split

sample, based on this sample it is more pronounced, though mainly for equation 8 and less for

equations 9 and 10. As a consequence, hypothesis 2a would have more probability of being accepted

as for all equations a big majority of coefficients are negative. Globally seen, hypothesis 2b (negative

correlation between T1CR and the size of the GEXPR coefficients) would still be accepted. However

for event window [0] the relationship was reversed (so rising GEXPR coefficient with rising capital

base). This is a weird outcome as both “before” and “after” resulted in the negative correlation and this

regression is based on the same AARs. Next, the previous conclusions about the rejection of the

hypotheses about the deposits-to-assets and loans-to-asset ratio as well as the acceptance of the

country and size hypotheses stay valid.

To conclude, based on these coefficients, almost all previous findings are confirmed. The only

substantial difference is that T1CR might matter more than in the previous results. Overall, this

robustness check was quite successful.

(2) The second robustness check was performed taking into account the significance of the AARs used

in the second step. All of the previous regressions were done based on all the AARs (so both

significant as well as insignificant ones). Now, the regressions were once again repeated for the

significant AARs only. As there were not enough significant AARs from “before” the stress test, this

robustness check was only done for the results from the general regressions based on the 51 and 102

AARs. As there are less significant AARs, these regressions are in general based on less observations

66

and as a result, have a lot less significant coefficients. The significant coefficients however, were very

similar (both in size and sign) so that these regressions verify the robustness of the previous general

results. These results can be found in appendix 3.2.

To conclude, both robustness checks were successful thereby increasing the reliability of the

outcome found.

5.1.3 Conclusion stock return

In conclusion, the two main hypotheses can be accepted based on the stock return sample. This means

that investors take the Greek exposure (significantly) into account during Greek events and this even

more after July 2010 than before. It is noteworthy that also before July 2010, this held true for non-

Greek banks. In general, two significant interaction terms were verified. The first one is the Greek

exposure with the Greece dummy; For the Greek banks, the abnormal reaction caused by one unit of

exposure is about ten times smaller than for the other banks. The second interaction term identified is

the Greek exposure with the capital ratio as it is obvious that the banks with low capital are affected

more per percent of exposure they hold. On top of that, during the strong and most stunning events,

the investors seem to attach more value to this exposure. During the smaller events, only the investors

of non-Greek banks value the banks based on the Greek exposure, although to a lesser extent than

during the stronger events only.

Of course the abnormal reactions were not only driven by the Greek exposure. This is obvious as in

general, the banks’ exposure only explains about 13% of the AARs (19% for the strong events). The

rest of this abnormal reaction is correlated with other terms like the size and the origin of the banks.

Especially the nationality of the bank seems to play a big role. The Greek banks and banks from the

PIIS experience a substantial external influence independent of their exposure size. For the Greek

(PIIS) banks this explains up to 75% (50%) of the abnormal reaction. A more in depth conclusion is

described in section 7 (infra, p.72) where table 16 (infra, p.74) gives an overview of the hypotheses as

well as the conclusions about them.

67

5.2 CDS While the previous section describes the results based on the stock returns, this part will focus on the

results from the CDS returns. Unfortunately, these regressions were not as successful as the first step

resulted in a minority of significant coefficients, making it impossible or irrelevant to pursue the

second step of the methodology.

5.2.1 STEP 1: market model and average abnormal return

For the CDSs the methodology is more or less similar.

Table 15 shows the averages of the AARs generated for each scenario as well as the standard

deviation and proportion of significant coefficients. It is clear that there is a problem with the

reliability of these results. Since none of them attains the barrier of at least 50% (and not even 30%)

significant coefficients, these AARs cannot be further regressed in a second step.

As can be seen from the table, the averages are mainly negative or close to zero. This is what was

expected as CDS prices generally move in the opposite direction of the stock prices. When there is

good (bad) news, the probability of a default should decrease (increase), resulting in negative

(positive) CDSs returns. As all the events are programmed as positive events (the negative ones got “-

1” as a value), on average, a negative value was expected for the AARs. Unfortunately, these AARs

were not big enough, or there was too much variance to generate reliable AARs.

In an attempt to create more significant results, two more event windows were added, one for [0,3] and

one for [0,5]. However, this did not result in enough reliability either.

A plausible explanation for this insignificance is that the events are not well chosen or that the

expected reaction (positive or negative) is not conform to the reality. As mentioned before,

bondholders and shareholders do not always react in a similar way. Therefore, it is possible that while

some events were valued positively by the bondholders, they were experienced negatively by the

shareholders. In order to identify this type of exceptional events, I calculated the average market-

adjusted abnormal return on each event date. That way, I have found some events where the CDS

return behaved differently than expected. Here are two clear examples of this type of reversed events:

on 27 October 2011, the rescue package with a bail-out of 50% was announced. While the

stockholders of the banks in general experienced this as a positive event, the average bank CDS

showed a strong positive market-adjusted abnormal return. The reverse reaction is measured on 31

October 2011 when the referendum was announced. A possible explanation is that the banks’

bondholders until October 27 expected a loss smaller than the 50 percent bail-out so that this was a

negative outcome which was later on reverted by the referendum. This in contrast to the banks’

stockholders which were in general less optimistic and experienced this event as a “relief”. Next to

68

these events, there are also other events where the abnormal returns were very small, if not non-

existent. Possibly setting new event dummy series could help, even though the process of choosing

these events would be even less scientifically justifiable. On top of that, using other events for the

stocks than for the CDS, would impede the main reason to conduct this research with both securities,

i.e. the ability to compare the results of both.

The AAR(s) for the CDSs from each bank and per scenario can be found in appendix 2.2.

SUMMARY STATISTICS OF THE AARS OF THE CDS RETURNS

Event group

Time frame

Event window Average

AAR Std. Dev. AARs

Number significant AARs (as a % of total)

All (29) 2008 - 2011

[-1,1] -0,2% 0,3% 3 (8%)

[0] -0,5% 0,4% 3 (8%)

[0,1] -0,5% 0,4% 8 (21%)

[0,3] -0,4% 0,2% 10 (26%)

[0,5] -0,3% 0,2% 7 (18%)

Strong (9) 2008-2011

[-1,1] 0,1% 0,7% 2 (5%)

[0] 0,0% 1,2% 4 (11%)

[0,1] -0,3% 0,8% 2 (5%)

[0,3] -0,6% 0,4% 6 (16%)

[0,5] -0,4% 0,3% 1 (3%) Table 15: Summary statistics of the AARs of the CDS returns (source: author’s calculations based on data

from Bloomberg)

5.2.2 STEP 2: regression of the AARs

As the first step of this research on the CDSs did not result in enough significant coefficients, the

second step of the methodology cannot be performed so there are no further results.

5.2.3 Conclusion

In conclusion, the first step of the methodology based on the CDS returns resulted in too few

significant AARs. A plausible explanation is the fact that CDS holders (which are often holders of the

underlying bonds) and stockholders can react to certain events in a different way as they have not

exactly the same goals (reduction of risk versus the increase of profits) (King, 2009). Due to this lack

of significant results, the second step of the methodology would not have resulted in reliable results.

Therefore, no conclusions about the hypotheses can be based on the CDS returns. The inclusion of

different events as well as different more banks could solve this problem. However, I leave this for

further research.

69

6. Limitations and directions for further research Even though I have tried to be as complete and correct as possible while performing this research and

writing this paper, there will always remain limitations in every work. Hence, I hereby list the most

important ones as I believe it will make my work more valuable once the readers know how to

interpret it correctly. The five most important restrictions I have identified concern (1) the fact that

only listed banks are used, (2) the fact that the event selection process is subjective and the results can

depend on it, (3) the small amount of (significant) AARs as well as the limited availability of the input

data for control variables (4) the geographical extendibility to other countries (in a crisis?) and (5) the

fact that this research is not able to verify that the publication of the exposure data was the underlying

cause of the identified change.

(1) Only listed banks. First off all, due to availability issues of data, only listed banks (which

participated in the European stress test of 2010) are included in the sample. In general, it is often stated

that listed companies are bigger companies (as it is too costly for smaller ones to go public) and that

they often behave differently because they are put under pressure to perform well in the short term.

Therefore, it is possible that the results of this research cannot be generalized to all European banks.

However, I strongly believe that the logic that a bank with Greek exposure, will be affected more by a

Greek event (which is verified by this research), should also apply to other banks (independent of

whether they are listed or subject to the stress test).

(2) Event selection. A second constraint concerns the choice of the events. This process was rather

subjective. If different events would have been selected, the CDS returns might have rendered more

significant coefficients. However, I have tried to keep it as scientifically valid as possible by focusing

mainly on one source (the timeline of the Financial Times). I have compared this timeline with others

and this was the most complete one. On top of it, I have done a robustness check by including

scenarios with different numbers of events. Although this robustness check showed that the strength of

events did matter, the impact was quite limited as both event choices confirmed the main hypotheses.

As it cannot be excluded that an event study with other events would give different results, it would be

interesting to analyze this further.

(3) Small number of observations. Third, only a limited number of banks is included, making it

harder to check the reliability of the results as this lowers the power of the regressions in the second

step of the methodology. Fortunately, the regressions based on 51 or 102 observations, should already

be quite reliable. However, the coefficients of the regressions based on only the significant AARs

should be interpreted with caution as they are not as reliable. As they just serve as a robustness check,

I have only taken into account the sign of the significant coefficients. Next to that, the values for the

70

control variables are a snapshot at the end of the year. These might change during the course of the

year and often these end-of-year reports are window dressed in order to show a nicer image of the

company. As a result these snapshots are not always reliable, even though it should approach the true

image as the banks (and their reports) are audited. The majority of the research (even the EBA) uses

these variables so this research should be as reliable as those concerning the input variables. One of

the implicit assumptions of this research is that the snapshot taken at the end of the year is

representative for that variable from 6 months before until 6 months after (and this implies the

assumption that those variables change at a linear rate over the whole year). Moreover, there were no

end-of-year data available for the end of 2011 which is why the values of the end of 2010 are used

until October 2011. In general, these variables are quite stable which implies that these assumptions

should not create too much bias.

(4) Extendibility to other countries (in a crisis?). This third limitation stems from the limited

literature sources on the same topic (but other countries) which my findings could comply with. Third,

there are some questions concerning the geographic extendibility. As the markets have become more

and more international (70% of the European sovereign debt is held by the non-European banks

(Bolton and Jeanne, 2011)), I do not doubt that for the bigger events, also non-European banks should

be affected by their sovereign exposure towards Greece. Only towards Greece? While this research

focuses on the Greek crisis and Greek sovereign debt, the same type of research can be done for other

countries in crisis (like the PIIS) or even for other countries in general to check whether there is a

general relationship between the reaction of investors on events concerning countries of which the

banks hold sovereign debt. That is a question which should be investigated. I believe that this link

exists for every country which receives a large amount of media attention, which holds a lot of debt

and which is in a crisis. However, how far this term ‘crisis’ reaches and if the country really needs to

be in a crisis should be further investigated. My prediction would be that the higher the sovereign

default spread (or the higher the sovereign CDS price), the stronger this link should be.

(5) Depth of research: underlying causes? A fifth restriction is that the cause of this reinforced

relationship before versus after the stress test cannot be identified. Is this change due to the better

informed investors, due to a difference in composition of the chosen events or does the exposure get

more attention as the probability of a default rises? This difference in selection of events can be

countered by the fact that there are ten (out of the 29) events before the stress test of which four strong

ones (so 40% is strong). This means that the sample after the stress test is based on 19 events, of which

5 strong ones (so a proportion of 26% strong events). Hence, the argument of proportionally more

strong events, does not hold. However, due to the smaller number of events, it is possible that the

AARs before are more biased as there are less event dates present. Even if it were proven that it is due

to an reinforced correlation, it is still not sure whether this is caused by the information the stress test

71

published, whether this relationship reinforces constantly over time as the probability of a default

increases or whether there is another cause. Therefore, this research could be extended by

incorporating factors like the sovereign default or CDS spread of Greece itself, in interaction with the

GEXPR coefficient.

On top of the recommendations for further research related to the limitations described above, there

are two other main directions which can be followed; i.e. (1) include future and other events and

checks with the CDS returns and (2) perform this research on corporate debt holders.

(1) Include future and other events. The first is to further investigate the results I found concerning

Greece. An attempt should be made to check whether samples exist for which the results are

independent of the event group included. On top of that, events after October 2011 should be included

as it would be interesting to find out whether this relationship will still hold (or maybe even further

reinforce) or whether it is time bound and only appears during crises. If in some way the CDSs can be

used to test the same hypotheses (and this new event choice), the results of that research would be very

valuable since they can verify or reject the results of this study.

(2) Corporate debt holders. In order to be able to generalize even further, research with corporations

instead of countries as debt issuers could be performed. While there has been some research about it,

this research mainly focuses on companies that have defaulted or have gone bankrupt. Therefore, it

can be interesting to see whether the securities of the main debt holding bank react to events like

company announcements, changes in profit (or loss) forecasts, annual results, etc.

72

7. Conclusion To end with, I will briefly summarize my thesis. A Dutch summary is provided in appendix 4.

This research investigates the effect of the Greek exposure of banks on their stock returns from the

start of the Greek crisis (December 2009) until October 2011. The purpose of this research is to

investigate the link between a bank’s valuation and its sovereign exposure towards a country in

trouble. The main hypothesis states that the larger the size of this (Greek) exposure, the larger the

average abnormal returns (AARs) should be (hypothesis 1a). However, this only applies if everything

else stays equal or “ceteris paribus”. This last sentence means that there might also be other factors

than the Greek exposure which may influence these abnormal returns. One of them is the nationality

of the bank. It is expected that, due to external economic as well as contagious forces, the securities of

banks from the PIIGS (in particular from Greece) will have higher abnormal returns than the others

(hypothesis 6a and 6b). Next, some control variables which determine the overall risk profile of a

bank, such as the capital ratio, size, funding ratio and asset structure ratio, can also drive the size of the

AARs (hypothesis 2a, 3, 4 and 5). On top of that, the strength of the impact of the Greek exposure can

depend on some other factors like the capital ratio (as low-capitalized banks might be punished more

per unit of Greek exposure than the high-capitalized banks) as well as whether it is a Greek bank or

not (hypothesis 2a and 6c). A last hypothesis is based on the fact that the investors could not make

fully informed decisions regarding the Greek exposure before the publication of the stress test in July

2010 as during that period, these exposures were not publicly available (Kirkegaard, 2010, July 28)

(hypothesis 1b). All these hypotheses are built based on relevant literature in section 2.4 (supra, p.20),

they are repeated shortly in section 4.1 Summary of hypotheses (supra, p.36) and summarized in

table 16 (infra, p.74).

In order to measure the AAR(s) of each bank, an event-study was performed using two sets of events:

a set of 9 strong events and a set of 29 events which also includes smaller ones. These regressions

resulted in a series of AARs (one or two per bank) which were subsequently linked through a cross-

section with inter alia the Greek exposure, some control variables as well as country dummies of each

bank. The regressions were performed on two security types: the stock returns (for 51 banks) as well

as the CDS returns (for 38 banks). Unfortunately, the CDS returns resulted in an insufficient amount

of significant results, causing the cross-section to be unreliable and unusable. As a consequence, only

the AARs based on the stock returns were further investigated.

On average (for all scenarios), an abnormal reaction of the stock return of 1% was measured for

positive events. The size of the AARs however depends on the origin of the banks. For banks coming

from Greece, AARs four times bigger than those of non-PIIGS banks were measured. Similarly, the

73

AARs of the PIIS were double the size of the non-PIIGS banks. From this first step, it could not be

determined whether this difference is caused by domestic forces, by more Greek exposure or even a

riskier profile. A second factor influencing the size of the AARs were the number of events. Logically,

the AARs based on the dummy with the 9 strong events resulted in a bigger abnormal reaction (1.3%)

than those based on the dummy with all 29 (0.8%).

In the last column of table 16, the conclusions about the results of the regressions of the AARs (from

the stock return) are displayed. This research brings no surprising findings as the main hypotheses are

accepted. Nevertheless, it is worthwhile to see that these theoretical market forces can be empirically

identified. First of all, the main hypothesis that banks’ stock return in general are influenced by the

Greek exposure is accepted. However, there are three factors which need to be taken into account

when investigating this influence. First, as hypothesis 6c denotes, the strength of the influence is

dependent on the nationality of a bank, as it was found that the influence for one percent of Greek

exposure is ten times smaller for Greek banks than for non-Greek banks. Second, the impact of this

effect depends on the selection of events. A bit surprising is that the investors of the Greek banks only

take the Greek exposure into account during strong events. In general during those strongest events,

the Greek exposure receives more influence both in absolute terms (as the AARs of the strong events

are bigger) as in relative terms as 19% of the AARs can be explained by the size of the Greek bond

portfolio, while this proportion drops to 7% when the other 20 events are included. As a consequence,

the rest of the results are based on the set of strong events. Third, the capital ratio, as stated in

hypothesis 2b, also influences the impact of the Greek exposure. Banks with a lower capitalization

will get a bigger AAR than banks with a large capital buffer, ceteris paribus.

A second important research result concerns the time split before and after the exposure data were

released. Just like during the Mexican debt crisis (Bruner and Simms (1987)), the investors appear to

be able to incorporate the exposure size when valuing the banks without all of the data being publicly

available. This implies that investors found other ways to estimate the size of the exposure. This

applied mainly for the non-Greek banks though. For the Greek banks, the impact of the exposure

before July 2010 was, although positive, not significant. Afterwards, the influence of the Greek

exposure seems to have gained importance as its coefficients became substantially bigger (2.4 times)

and significant for all banks resulting in the acceptance of hypothesis 1b.

A last interesting take-away concerns the influence of the nationality of a bank, independent of the

size of the bond portfolio. While the Greek exposure only counts for a minor part of the total AAR

(19% for the strong events), this country factor largely drives the AARs of the banks from Greece as

well as from the PIIS. This explains largely why their AARs were respectively 4 (Greece) and 2 (PIIS)

times higher than the AARs from the non-PIIGS banks. For the Greek banks, 75% of the AAR was

74

explained by a Greece (country) dummy. This means that the Greek banks’ value were largely affected

by the content of these events, independent of the possible value decrease of their domestic sovereign

bonds. This is logical as many of the events also reported or influenced the economic condition of

Greece which in turn affected the debtors and lenders of these Greek banks. A similar logic applies to

the banks from the PIIS. On average, 50% of their AARs is driven by domestic origin. This can be

explained by (fear for) contagion as the PIIGS are perceived to be similar countries. On top of that,

there are also some events included which can impact multiple (in general peripheral) countries like

the announcement of the rescue packages. If this domestic factor would be filtered out, the size of the

AARs of the PIIGS banks becomes almost similar to those of non-PIIGS banks.

Hypothesis Factors Expected influence on size of AARs Conclusion

1a Greek exposure ratio +

1b Greek exposure ratio (before

vs after) after > before

2a Tier 1 Capital ratio -

2b Greek exposure ratio

depending on T1CR

coefficient GEXPR (small T1CR) >

coefficient (big T1CR)

3 ln(Total Assets) +

4

5

Deposits/TA

Loans/TA

-

+ or -

6a PII(G)S +

6b Greece +

6c Greek exposure ratio

depending on Greece

coefficient GEXPR (non-Greek banks)

> coefficient GEXPR (Greek banks)

Table 16: Summary of hypotheses and findings

It is quite hard to draw conclusions concerning the four control variables. Fortunately, this is not the

main focus of the paper which is why less attention is devoted to them. (1) Hypothesis 2a concerning

the impact of the capital ratio, independent of its influence on the Greek exposure link, is not accepted.

This means that the capital ratio does not seem to influence the returns directly, only through its

impact on the strength of the Greek exposure coefficient. (2) The “size” coefficient is significantly

positive if considered over the whole sample period. As a result, hypothesis 3 is accepted and Demsetz

and Strahan’s (1997) research results, concerning the asset size holding a positive relationship with

systematic risk, is confirmed. (3) Next, the deposit-to-asset ratio did not seem to influence the AARs

in general. When looking at the coefficients of this ratio of the time split, an interesting outcome

appears: they showed significant positive coefficients before July 2010 contradicting the expectations

based on the research of Ivashina and Scharfstein (2010), Demirguc-Kunt and Huizinga (2009),

Wheelock (1992). After July 2010, significant negative values showed up based on the regressions.

75

When considered over the whole period, hypothesis 4 is rejected. (5) A last control variable included

is the loans-to-asset ratio for which the literature provided both arguments for a negative as well as a

positive link. The results indicate that this asset structure ratio has no significant effect either. As a

result, hypothesis 5 stating that it would have some kind of influence is rejected based on this sample.

In conclusion, only the size ratio seemed to have a significant and immediate impact on the AAR size.

Now that the results are summarized, it is time to pay some attention to the practical and theoretical

implications of these results as well as their limitations. In general, this research includes quite some

robustness checks (like the scenarios based on two series of events, three event windows, a time frame

starting from 2008 and one from 2011, the regressions based on all AARs and only the significant

ones, etc.). Even though most of these robustness checks resulted in similar coefficients, the results

need to be interpreted with caution. In general I have identified five main restrictions which are

described more extensively in the previous section. These concern (1) the fact that only listed banks

are included, (2) the fact that the event selection process is quite subjective and that a different

selection could result in different findings (especially concerning the CDS returns), (3) the small

amount of banks included (especially for the CDSs) resulting in few (significant) AARs as well as the

limited availability of the input data for control variables (4) limited possibilities for geographical

generalizability to other countries (in a crisis?) due to a lack of similar research in a recent setting to

complement with and (5) the lack of ability to identify the underlying cause of the identified change.

As can be seen, there are quite some limitations leaving room for further research. Two other further

research topics concern the inclusion of future events (past October 2011) as well as extension towards

companies by including corporate debtors.

In regard to the theoretical relevance, it can be interesting to fit these results in the existing literature.

Not a lot of comparable research concerning sovereign exposure in a recent (or even European) setting

has been performed. If I were to summarize the previous research (which is mainly focused on the

LDC crisis), I would conclude that a link was found between the banks’ security returns and their

exposure on the long term (after the Mexican default), while there were no short term effects.

However, this paper contradicts the previous literature that there is no short term link. One might

argue that this difference should be ascribed to a different research set-up. The structure of this

research is such that only shorter term effects can be measured, and this for both negative and positive

events concerning a possible default, but not the default itself. However, logically seen, a default event

itself (like for Mexico) should have more impact both on the short and long term, leading to the

reasoning that if these smaller events had an immediate effect, the default would a fortiori. As a result,

this outcome should not be ascribed to the fact that the research design is different. The different

outcome could be due to an increase in efficiency and transparency of the markets over time. Hence,

76

more research concerning this short term impact of the sovereign exposure on banks should be

investigated in a recent setting to see whether this change is persistent.

To end with, this paper could have some practical relevance as the outcome might help banks and

investors to better understand or predict the movements of their stocks on the day of an event. They

should also be able to better understand these movements in comparison with other banks, by taking

into account their risk profile (mainly size), possible contagion effects, the content of the event (for the

Greek banks) as well as portfolios of Greek sovereign bonds.

IX

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Appendix 1

VI List of Appendices Appendix 1 Input control variables and Greek exposure

Appendix 2 AARs from stock and CDS return resulting from step 1

Appendix 2.1 AARs for stock return

o AARs for the not-time split dummies for stocks

o AARs for the time split dummies for stocks

Appendix 2.2 AARs for CDS return

Appendix 3 Results regressions second step

Appendix 3.1 Regressions based on all AARs (significant and insignificant)

Appendix 3.1.1 General regressions for (51) AARs from not-time split dummies

Appendix 3.1.3 Regressions for AARs from events BEFORE July 2010

Appendix 3.1.4 Regressions for AARs from events AFTER July 2010

Appendix 3.2 Regressions based on significant AARs only

Appendix 3.2.1 General regressions for (51) AARs from not-time split dummies (only

sign AARs)

Appendix 3.2.2 General regressions for (102) AARs from time split dummies (only

sign AARs)

Appendix 4 Dutch Summary/Nederlandse samenvatting

Appendix 1

Appendix 1: Input control variables and Greek exposure stock return sample

Name of institution T1CR 2010

T1CR 2009

T1CR av

ln(TA) 2010

ln(TA) 2009

ln(TA) Av

dep/ TA

2010

dep/ TA

2009

dep/ TA Av

loans /TA

2010

loans /TA

2009

loans /TA Av

GEXPR 2010

GEXPR 2009

GEXPR ratio

PIIGS Greece PIIS

AGRICULTURAL BANK OF GREECE S.A.

6,3% 8,4% 7,4% 3,73 3,86 3,80 69% 61% 65% 68% 67% 67% 18,82% 21,53% 20,18% 1 1 0

ALLIED IRISH BANKS 3,7% 7,0% 5,4% 5,27 5,53 5,41 53% 47% 50% 59% 59% 59% 0,02% 0,02% 0,02% 1 0 1

ALPHA BANK 10,8% 11,6% 11,2% 4,49 4,61 4,55 61% 56% 59% 74% 74% 74% 6,13% 5,06% 5,60% 1 1 0

BANCO BPI 8,2% 8,5% 8,4% 4,11 4,22 4,17 47% 44% 45% 66% 63% 64% 0,53% 0,73% 0,63% 1 0 1

BANCO COMERCIAL PORTUGUÊS

5,9% 9,3% 7,6% 4,90 4,92 4,91 50% 44% 47% 74% 79% 76% 0,54% 0,52% 0,53% 1 0 1

BANCO DE SABADELL 6,2% 9,0% 7,6% 4,87 4,78 4,82 49% 42% 46% 73% 76% 75% 0,00% 0,00% 0,00% 1 0 1

BANCO PASTOR, S.A. 7,6% 10,5% 9,1% 3,73 3,84 3,79 50% 47% 48% 69% 63% 66% 0,00% 0,09% 0,04% 1 0 1

BANCO POPOLARE 5,8% 7,7% 6,8% 5,20 5,28 5,24 35% 32% 33% 70% 70% 70% 0,05% 0,05% 0,05% 1 0 1

BANCO POPULAR ESPAÑOL, S.A.

7,1% 9,1% 8,1% 5,16 5,23 5,19 51% 48% 50% 74% 73% 74% 0,00% 0,00% 0,00% 1 0 1

BANCO SANTANDER 7,1% 10,0% 8,6% 7,39 7,38 7,39 48% 42% 45% 59% 60% 60% 0,01% 0,03% 0,02% 1 0 1

BANK OF CYPRUS 8,1% 10,5% 9,3% 4,04 4,04 4,04 66% 61% 63% 65% 65% 65% 4,22% 3,34% 3,78% 0 0 0

BANK OF IRELAND 8,4% 9,2% 8,8% 5,41 5,56 5,49 48% 39% 44% 68% 66% 67% 0,00% 0,00% 0,00% 1 0 1

BANK OF VALLETTA 10,5% 10,5% 10,5% 2,16 2,21 2,18 64% 60% 62% 55% 52% 54% 0,12% 0,10% 0,11% 0 0 0

BANKINTER, S.A. 6,2% 7,5% 6,9% 4,28 4,36 4,32 43% 42% 42% 79% 73% 76% 0,00% 0,00% 0,00% 1 0 1

BARCLAYS 10,0% 13,0% 11,5% 7,75 7,71 7,73 33% 30% 31% 29% 30% 30% 0,01% 0,02% 0,01% 0 0 0

BNP PARIBAS 9,2% 10,1% 9,7% 7,89 7,99 7,94 34% 34% 34% 34% 33% 34% 0,20% 0,17% 0,18% 0 0 0

COMMERZBANK AG 10,0% 10,5% 10,3% 6,92 7,10 7,01 44% 38% 41% 39% 39% 39% 0,30% 0,24% 0,27% 0 0 0

CREDIT AGRICOLE 8,2% 9,7% 9,0% 7,66 7,72 7,69 31% 27% 29% 24% 23% 24% 0,03% 0,04% 0,04% 0 0 0

DANSKE BANK 10,0% 11,7% 10,9% 6,35 6,39 6,37 27% 26% 27% 58% 59% 58% 0,00% 0,00% 0,00% 0 0 0

DEUTSCHE BANK AG 8,8% 12,6% 10,7% 7,84 7,68 7,76 29% 20% 25% 21% 17% 19% 0,07% 0,08% 0,07% 0 0 0

DEUTSCHE POSTB AG 7,1% 7,1% 7,1% 5,66 5,79 5,73 57% 55% 56% 51% 48% 50% 0,00% 0,41% 0,20% 0 0 0

DEXIA 10,5% 12,3% 11,4% 6,63 6,72 6,68 36% 36% 36% 62% 61% 62% 0,06% 0,45% 0,25% 0 0 0

EFG EUROBANK ERGASIAS S.A.

9,0% 11,2% 10,1% 4,76 4,80 4,78 61% 55% 58% 65% 66% 65% 7,55% 6,14% 6,84% 1 1 0

ERSTE GR BANK AG 8,7% 9,2% 9,0% 5,62 5,67 5,65 50% 49% 49% 61% 62% 62% 0,13% 0,26% 0,19% 0 0 0

ESPÍRITO SANTO FIN 6,4% 7,7% 7,1% 4,76 4,81 4,78 47% 30% 38% 61% 59% 60% 0,27% 0,38% 0,32% 1 0 1

Appendix 1

GR S.A. (ESFG) HSBC HOLDINGS PLC 10,5% 10,8% 10,7% 7,81 7,77 7,79 44% 41% 43% 39% 38% 38% 0,05% 0,06% 0,06% 0 0 0

ING Bank 9,6% 10,2% 9,9% 7,13 7,15 7,14 54% 50% 52% 63% 63% 63% 0,06% 0,19% 0,13% 0 0 0

INTESA SANPAOLO 7,9% 8,3% 8,1% 6,78 6,80 6,79 31% 28% 30% 58% 60% 59% 0,07% 0,09% 0,08% 1 0 1

JYSKE BANK A/S 12,1% 13,5% 12,8% 3,77 3,77 3,77 45% 40% 43% 47% 49% 48% 0,15% 0,21% 0,18% 0 0 0

KBC GROUP 12,1% 10,9% 11,5% 5,91 6,01 5,96 55% 50% 52% 52% 52% 52% 0,94% 0,22% 0,58% 0 0 0

LB BERLIN AG 14,6% 13,3% 14,0% 5,16 5,32 5,24 40% 37% 39% 35% 33% 34% 0,26% 0,21% 0,24% 0 0 0

LLOYDS BANKING GR 10,2% 9,6% 9,9% 7,35 7,42 7,38 34% 33% 33% 60% 61% 60% 0,00% 0,00% 0,00% 0 0 0

MARFIN POPULAR B. 7,3% 9,4% 8,4% 4,04 4,10 4,07 64% 57% 60% 62% 60% 61% 5,99% 4,88% 5,44% 0 0 0

MONTE DEI PASCHI DI SIENA

5,8% 7,5% 6,7% 5,79 5,78 5,78 39% 35% 37% 64% 68% 66% 0,00% 0,01% 0,01% 1 0 1

NAT BANK of GREECE 11,9% 11,3% 11,6% 5,08 5,10 5,09 61% 57% 59% 59% 61% 60% 11,65% 12,09% 11,87% 1 1 0

NORDEA BANK 8,9% 10,2% 9,6% 6,65 6,59 6,62 37% 38% 37% 54% 56% 55% 0,00% 0,03% 0,02% 0 0 0

OP-POHJOLA GROUP 12,2% 12,6% 12,4% 4,72 4,75 4,74 45% 43% 44% 68% 66% 67% 0,00% 0,02% 0,01% 0 0 0

OTP BANK NYRT. 12,3% 13,8% 13,1% 3,85 3,95 3,90 55% 52% 54% 69% 66% 67% 0,00% 0,00% 0,00% 0 0 0

PIRAEUS BANK GR 8,0% 9,1% 8,6% 4,34 4,36 4,35 64% 57% 60% 65% 69% 67% 10,67% 10,62% 10,64% 1 1 0

PKO BANK POLSKI 11,8% 13,3% 12,6% 4,05 4,01 4,03 61% 58% 59% 77% 75% 76% 0,00% 0,00% 0,00% 0 0 0

RAIFFEISEN 8,1% 9,3% 8,7% 5,21 5,36 5,29 52% 50% 51% 54% 48% 51% 0,00% 0,01% 0,01% 0 0 0

ROYAL BANK OF SCOTLAND (RBS)

9,7% 14,4% 12,1% 7,62 7,68 7,65 32% 30% 31% 40% 40% 40% 0,06% 0,10% 0,08% 0 0 0

SKANDIN. ENSKILDA BANKEN (SEB)

11,1% 12,4% 11,8% 5,78 5,78 5,78 42% 36% 39% 49% 51% 50% 0,04% 0,05% 0,04% 0 0 0

SNS BANK 8,4% 10,7% 9,6% 4,66 4,75 4,71 39% 36% 37% 82% 84% 83% 0,04% 0,03% 0,04% 0 0 0

SOCIETE GENERALE 8,1% 10,7% 9,4% 7,32 7,30 7,31 38% 36% 37% 34% 36% 35% 0,19% 0,29% 0,24% 0 0 0

SVENSKA HANDELSB. 7,7% 9,1% 8,4% 5,77 5,70 5,74 40% 38% 39% 69% 70% 69% 0,00% 0,00% 0,00% 0 0 0

SWEDBANK 8,7% 10,4% 9,6% 5,54 5,53 5,54 40% 28% 34% 69% 72% 71% 0,00% 0,00% 0,00% 0 0 0

SYDBANK A/S 12,4% 13,1% 12,8% 3,29 3,41 3,35 58% 55% 56% 56% 55% 55% 0,00% 0,00% 0,00% 0 0 0

TT HELLENIC POSTB 18,5% 17,1% 17,8% 3,10 3,25 3,18 69% 62% 65% 48% 44% 46% 24,00% 20,76% 22,38% 1 1 0

UNICREDIT 7,8% 8,6% 8,2% 7,12 7,20 7,16 41% 37% 39% 60% 61% 60% 0,05% 0,06% 0,06% 1 0 1

UNIONE DI BANCHE ITALIANE (UBI)

7,0% 8,0% 7,5% 5,16 5,17 5,17 37% 34% 36% 78% 80% 79% 0,01% 0,01% 0,01% 1 0 1

Appendix 2.1

Appendix 2: AARs from stock and CDS return resulting from step 1

Appendix 2.1 AARs for stock return

AARs for the not-time split dummies for stocks

Table: AARs (=γi) resulting from step 1 for the not-time split dummies applied on the stocks for the group of all 29 events. (source: author’s

calculations based on data from Datastream)

NOT-TIME SPLIT STOCK All (29) events

Ri,t = αi+ βi*Rm,t + γi*Dt + εi,t 2006-2011 2008-2011

[-1,1] [-3,3] [0] [0,1] [-1,1] [-3,3] [0] [0,1]

AGRICULTURAL BANK OF GREECE S.A. 0,0202** 0,0075** 0,0247** 0,0224** 0,0193** 0,0069* 0,0232** 0,0212**

ALLIED IRISH BANKS 0,0043 0,0060 0,0043 0,0046 0,0028 0,0053 0,0016 0,0026

ALPHA BANK 0,0204** 0,0074** 0,0233** 0,0225** 0,0197** 0,0069** 0,0222** 0,0217**

BANCO BPI 0,0123** 0,0049** 0,0188** 0,0130** 0,0120** 0,0047** 0,0181** 0,0124**

BANCO COMERCIAL PORTUGUÊS 0,0116** 0,0059** 0,0200** 0,0119** 0,0113** 0,0057** 0,0195** 0,0115**

BANCO DE SABADELL 0,0056** 0,0027** 0,0123** 0,0075** 0,0052** 0,0024** 0,0118** 0,0070**

BANCO PASTOR, S.A. 0,0007 0,0007 0,0030 -0,0003 0,0012 0,0008 0,0039 0,0003

BANCO POPOLARE 0,0076** 0,0024 0,0140** 0,0070** 0,0067** 0,0020 0,0126** 0,0059*

BANCO POPULAR ESPAÑOL, S.A. 0,0081** 0,0040** 0,0167** 0,0106** 0,0077** 0,0038** 0,0160** 0,0101**

BANCO SANTANDER 0,0068** 0,0034** 0,0171** 0,0085** 0,0062** 0,0032** 0,0159** 0,0077**

BANK OF CYPRUS PUBLIC CO LTD 0,0116** 0,0048** 0,0202** 0,0154** 0,0115** 0,0046** 0,0203** 0,0154**

BANK OF IRELAND 0,0063 0,0030 0,0101 0,0080 0,0059 0,0029 0,0093 0,0075

BANK OF VALLETTA 0,0001 0,0001 -0,0032 0,0001 0,0003 0,0003 -0,0029 0,0004

BANKINTER, S.A. 0,0064** 0,0027* 0,0139** 0,0086** 0,0062** 0,0024 0,0136** 0,0084**

BARCLAYS 0,0023 0,0002 0,0039 0,0023 0,0014 0,0000 0,0020 0,0010

BNP PARIBAS 0,0072** 0,0019 0,0213** 0,0109** 0,0058** 0,0014 0,0187** 0,0091**

COMMERZBANK AG 0,0075** 0,0043** 0,0151** 0,0090** 0,0071** 0,0041* 0,0145** 0,0085**

CREDIT AGRICOLE 0,0086** 0,0036** 0,0216** 0,0130** 0,0078** 0,0033* 0,0202** 0,0120**

** significant at the 5% level * significant at the 10% level not significantly different than 0 at 10% level

Appendix 2.1

DANSKE BANK 0,0042** 0,0028** 0,0073** 0,0056** 0,0040* 0,0028* 0,0069* 0,0054*

DEUTSCHE BANK AG 0,0023 0,0014 0,0046 0,0011 0,0017 0,0013 0,0033 0,0002

DEUTSCHE POSTB AG 0,0004 0,0009 0,0020 0,0018 0,0010 0,0011 0,0028 0,0025

DEXIA 0,0106** 0,0060** 0,0167** 0,0139** 0,0098** 0,0055** 0,0154** 0,0129**

EFG EUROBANK ERGASIAS S.A. 0,0206** 0,0084** 0,0287** 0,0224** 0,0199** 0,0079** 0,0274** 0,0215**

ERSTE GR BANK AG 0,0038 0,0038** 0,0132** 0,0035 0,0034 0,0037* 0,0124** 0,0028

ESPÍRITO SANTO FIN GR S.A. (ESFG) -0,0011 0,0004 -0,0051* -0,0027 -0,0003 0,0006 -0,0036 -0,0016

HSBC HOLDINGS PLC 0,0001 -0,0007 -0,0009 -0,0010 -0,0003 -0,0009 -0,0016 -0,0015

ING Bank 0,0062** 0,0027 0,0106** 0,0080** 0,0049* 0,0023 0,0079* 0,0062

INTESA SANPAOLO 0,0084** 0,0030* 0,0222** 0,0108** 0,0072** 0,0026 0,0200** 0,0093**

JYSKE BANK A/S 0,0018 0,0005 0,0041 0,0017 0,0018 0,0006 0,0041 0,0017

KBC GROUP 0,0057 0,0012 0,0167 0,0099** 0,0045 0,0008 0,0145* 0,0083

LB BERLIN AG 0,0012 0,0005 0,0066 0,0028 0,0020 0,0007 0,0081* 0,0038

LLOYDS BANKING GR 0,0018 -0,0005 0,0026 0,0009 0,0002 -0,0011 -0,0004 -0,0012

MARFIN POPULAR B. 0,0118** 0,0042** 0,0174** 0,0158** 0,0118** 0,0040* 0,0175** 0,0157**

MONTE DEI PASCHI DI SIENA 0,0045** 0,0018 0,0117** 0,0056** 0,0039* 0,0014 0,0106** 0,0048*

NAT BANK OF GREECE 0,0205** 0,0093** 0,0286** 0,0219** 0,0203** 0,0091** 0,0283** 0,0216**

NORDEA BANK 0,0030 0,0022 0,0057* 0,0027 0,0019 0,0018 0,0036 0,0013

OP-POHJOLA GROUP 0,0038* 0,0018 0,0148** 0,0059** 0,0032 0,0016 0,0138** 0,0052*

OTP BANK NYRT. 0,0034 0,0026 0,0105** 0,0023 0,0029 0,0025 0,0097* 0,0017

PIRAEUS BANK GR 0,0214** 0,0100** 0,0274** 0,0224** 0,0210** 0,0095** 0,0270** 0,0219**

PKO BANK POLSKI 0,0024 0,0013 0,0036 0,0014 0,0020 0,0011 0,0028 0,0009

RAIFFEISEN ZB OESTERRREICH 0,0036 0,0035* 0,0074 0,0042 0,0039 0,0035* 0,0081 0,0047

ROYAL BANK OF SCOTLAND (RBS) 0,0009 -0,0014 0,0034 0,0007 0,0004 -0,0015 0,0025 0,0001

SKANDIN. ENSKILDA BANKEN (SEB) 0,0001 0,0011 0,0029 -0,0005 -0,0004 0,0009 0,0020 -0,0011

SNS BANK 0,0041 0,0017 0,0098** 0,0029 0,0030 0,0013 0,0078* 0,0015

SOCIETE GENERALE 0,0071** 0,0024 0,0207** 0,0109** 0,0058** 0,0019 0,0183** 0,0092**

SVENSKA HANDELSB. 0,0014 0,0016 0,0042 0,0017 0,0008 0,0015 0,0030 0,0009

SWEDBANK 0,0025 0,0027 0,0066 0,0024 0,0019 0,0026 0,0053 0,0015

SYDBANK A/S 0,0046** 0,0028** 0,0068** 0,0046** 0,0050** 0,0029** 0,0074* 0,0050**

TT HELLENIC POSTB 0,0179** 0,0070** 0,0246** 0,0193** 0,0176** 0,0068* 0,0242** 0,0190**

UNICREDIT 0,0056** 0,0017 0,0190** 0,0060** 0,0047 0,0014 0,0172** 0,0047

UNIONE DI BANCHE ITALIANE (UBI) 0,0058** 0,0025* 0,0154** 0,0077** 0,0047** 0,0020 0,0137** 0,0064**

Appendix 2.1

Table: AARs (=γi) resulting from step 1 for the not-time split dummies applied for the stocks for the group of 9 events. (* denotes significance at the

10 percent level, ** denotes significance at the 5 percent level) (source: author’s calculations based on data from Datastream)

NOT-TIME SPLIT STOCK 9 events

Ri,t = αi+ βi*Rm,t + γi*Dt + εi,t 2006-2011 2008-2011

[-1,1] [-3,3] [0] [0,1] [-1,1] [-3,3] [0] [0,1]

AGRICULTURAL BANK OF GREECE S.A. 0,0389** 0,0072 0,0433** 0,0505** 0,0379** 0,0067 0,0408** 0,0488**

ALLIED IRISH BANKS 0,0114 0,0083 0,0096 0,0154 0,0095 0,0076 0,0047 0,0120

ALPHA BANK 0,0294** 0,0034 0,0359** 0,0349** 0,0287** 0,0030 0,0343** 0,0338**

BANCO BPI 0,0195** 0,0047* 0,0335** 0,0252** 0,0190** 0,0045 0,0322** 0,0244**

BANCO COMERCIAL PORTUGUÊS 0,0161** 0,0076** 0,0346** 0,0189** 0,0158** 0,0074** 0,0340** 0,0183**

BANCO DE SABADELL 0,0056** 0,0038** 0,0182** 0,0086** 0,0052* 0,0036* 0,0174** 0,0079**

BANCO PASTOR, S.A. -0,0034 0,0021 0,0012 -0,0043 -0,0026 0,0023 0,0032 -0,0030

BANCO POPOLARE 0,0069 0,0041 0,0227** 0,0079 0,0058 0,0037 0,0200** 0,0059

BANCO POPULAR ESPAÑOL, S.A. 0,0117** 0,0032 0,0316** 0,0175** 0,0111** 0,0029 0,0302** 0,0165**

BANCO SANTANDER 0,0082** 0,0038* 0,0283** 0,0135** 0,0072** 0,0035 0,0260** 0,0118**

BANK OF CYPRUS PUBLIC CO LTD 0,0171** 0,0077** 0,0253** 0,0292** 0,0173** 0,0076* 0,0259** 0,0296**

BANK OF IRELAND -0,0004 0,0054 0,0025 0,0004 -0,0011 0,0052 0,0008 -0,0008

BANK OF VALLETTA -0,0052 -0,0022 -0,0101 -0,0054 -0,0051 -0,0021 -0,0098 -0,0052

BANKINTER, S.A. 0,0078** 0,0025 0,0216** 0,0126** 0,0077* 0,0024 0,0214** 0,0124**

BARCLAYS 0,0059 0,0027 0,0162* 0,0070 0,0042 0,0023 0,0120 0,0041

BNP PARIBAS 0,0118** 0,0038 0,0364** 0,0204** 0,0096** 0,0032 0,0312** 0,0166**

COMMERZBANK AG 0,0127** 0,0083** 0,0229** 0,0118** 0,0123** 0,0081** 0,0219** 0,0111*

CREDIT AGRICOLE 0,0175** 0,0085** 0,0381** 0,0255** 0,0163** 0,0081** 0,0351** 0,0234**

DANSKE BANK 0,0030 0,0019 0,0124** 0,0058 0,0026 0,0018 0,0114* 0,0051*

DEUTSCHE BANK AG 0,0075* 0,0051* 0,0191** 0,0090** 0,0063 0,0048 0,0162** 0,0069

DEUTSCHE POSTB AG -0,0046 -0,0028 -0,0005 -0,0025 -0,0039 -0,0025 0,0013 -0,0012

DEXIA 0,0139** 0,0074* 0,0271** 0,0196** 0,0130* 0,0070 0,0248** 0,0180**

EFG EUROBANK ERGASIAS S.A. 0,0258** 0,0031 0,0351* 0,0288** 0,0250** 0,0027 0,0330** 0,0273**

ERSTE GR BANK AG 0,0017 0,0039 0,0146* -0,0013 0,0009 0,0037 0,0126 -0,0028

ESPÍRITO SANTO FIN GR S.A. (ESFG) 0,0000 0,0040* -0,0045 -0,0034 0,0013 0,0043** -0,0013 -0,0012

Appendix 2.1

HSBC HOLDINGS PLC 0,0015 0,0005 0,0009 -0,0010 0,0010 0,0003 -0,0004 -0,0020

ING Bank 0,0111** 0,0056 0,0248** 0,0152** 0,0089* 0,0050 0,0192** 0,0113*

INTESA SANPAOLO 0,0158** 0,0061** 0,0399** 0,0202** 0,0140** 0,0056* 0,0356** 0,0171**

JYSKE BANK A/S 0,0011 -0,0033 0,0077 0,0026 0,0011 -0,0033 0,0077 0,0025

KBC GROUP 0,0112* 0,0049 0,0299* 0,0141* 0,0094 0,0044 0,0253* 0,0109

LB BERLIN AG 0,0062 0,0017 0,0090 0,0062 0,0074 0,0021 0,0122 0,0084

LLOYDS BANKING GR 0,0023 0,0016 0,0127 0,0001 -0,0002 0,0009 0,0065 -0,0044

MARFIN POPULAR B. 0,0205** 0,0061* 0,0168* 0,0296** 0,0208** 0,0060* 0,0175* 0,0302**

MONTE DEI PASCHI DI SIENA 0,0073** 0,0016 0,0203** 0,0086** 0,0065* 0,0013 0,0183** 0,0071*

NAT BANK OF GREECE 0,0289** 0,0075* 0,0445** 0,0340** 0,0288** 0,0073 0,0445** 0,0339**

NORDEA BANK 0,0023 0,0003 0,0087 -0,0014 0,0005 -0,0001 0,0043 -0,0045

OP-POHJOLA GROUP 0,0056 0,0034 0,0267** 0,0087* 0,0048 0,0032 0,0248** 0,0072

OTP BANK NYRT. -0,0046 -0,0007 0,0073 -0,0033 -0,0053 -0,0009 0,0055 -0,0046

PIRAEUS BANK GR 0,0260** 0,0049 0,0392** 0,0293** 0,0259** 0,0046 0,0390** 0,0291**

PKO BANK POLSKI 0,0009 0,0006 0,0025 -0,0004 0,0003 0,0004 0,0011 -0,0015

RAIFFEISEN ZB OESTERRREICH 0,0038 0,0037 0,0124 0,0036 0,0044 0,0038 0,0140 0,0047

ROYAL BANK OF SCOTLAND (RBS) 0,0034 -0,0005 0,0136 0,0023 0,0026 -0,0007 0,0115 0,0008

SKANDIN. ENSKILDA BANKEN (SEB) -0,0029 -0,0022 0,0020 -0,0054 -0,0037 -0,0024 0,0001 -0,0068

SNS BANK 0,0088* 0,0027 0,0139 0,0098* 0,0072 0,0023 0,0097 0,0069

SOCIETE GENERALE 0,0162** 0,0065** 0,0407** 0,0242** 0,0143** 0,0060* 0,0359** 0,0208**

SVENSKA HANDELSB. -0,0006 -0,0010 0,0010 -0,0027 -0,0016 -0,0013 -0,0015 -0,0045

SWEDBANK -0,0024 -0,0018 0,0036 -0,0040 -0,0036 -0,0021 0,0005 -0,0062

SYDBANK A/S 0,0062* 0,0002 0,0141** 0,0068* 0,0067* 0,0003 0,0155** 0,0078*

TT HELLENIC POSTB 0,0319** 0,0054 0,0454** 0,0336** 0,0317** 0,0052 0,0451** 0,0334**

UNICREDIT 0,0092** 0,0036 0,0292** 0,0096* 0,0077 0,0032 0,0256** 0,0070

UNIONE DI BANCHE ITALIANE (UBI) 0,0049 0,0016 0,0230** 0,0091** 0,0035 0,0011 0,0194** 0,0067

Appendix 2.1

AARs for the time split dummies for stocks

Tzble: AARs (=γi) resulting from step 1 for the time split dummies applied for the stocks for the group of all 29 events. (* denotes significance at the

10 percent level, ** denotes significance at the 5 percent level) (source: author’s calculations based on data from Datastream)

TIME SPLIT

STOCK All (29) events

Rit = αi+ βi*Rmt +

γbefore,i*Dbefore,t +

γafter,i*Dafter,t + εit

2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1]

Before After Before After Before After Before After Before After Before After

AGRIC BANK OF GREECE 0,0196** 0,0208** 0,0306** 0,0218** 0,0035 0,0214** 0,0181* 0,0202** 0,0282* 0,0208* 0,0053 0,0293**

ALLIED IRISH BANKS -0,0007 0,0034 0,0067 0,0030 -0,0029 0,0084 -0,0032 0,0023 0,0025 0,0012 -0,0058 0,0069

ALPHA BANK 0,0173** 0,0224** 0,0241** 0,0228** 0,0068 0,0305** 0,0161** 0,0220** 0,0224* 0,0221** 0,0055 0,0299**

BANCO BPI 0,0160** 0,0112** 0,0140** 0,0212** 0,0118** 0,0135** 0,0153** 0,0109** 0,0128** 0,0207** 0,0110** 0,0132**

BANCO COM PORTUG. 0,0148** 0,0108** 0,0220** 0,0190** 0,0118** 0,0120** 0,0143** 0,0106** 0,0212** 0,0187** 0,0112** 0,0117**

BANCO DE SABADELL 0,0038* 0,0065** 0,0130** 0,0120** 0,0061** 0,0082** 0,0032 0,0063** 0,0122** 0,0116** 0,0054* 0,0079**

BANCO PASTOR -0,0004 0,0013 -0,0024 0,0057 -0,0022 0,0007 0,0003 0,0016 -0,0010 0,0064* -0,0014 0,0012

BANCO POPOLARE 0,0064 0,0095** 0,0072 0,0174** 0,0043 0,0084** 0,0050 0,0090** 0,0049 0,0164** 0,0026 0,0076*

BANCO POPULAR ESP 0,0117** 0,0060** 0,0250** 0,0126** 0,0156** 0,0081** 0,0110** 0,0057** 0,0238** 0,0121** 0,0148** 0,0077**

BANCO SANTANDER 0,0099** 0,0052* 0,0219** 0,0147** 0,0112** 0,0071** 0,0089** 0,0047** 0,0201** 0,0139** 0,0100** 0,0065**

BANK OF CYPRUS 0,0132** 0,0108** 0,0199** 0,0203** 0,0089 0,0188** 0,0130** 0,0108** 0,0199** 0,0204** 0,0087 0,0188**

BANK OF IRELAND 0,0011 0,0094 0,0031 0,0135 0,0003 0,0119 0,0005 0,0092 0,0020 0,0130 -0,0004 0,0115

BANK OF VALLETTA 0,0040 -0,0018 0,0020 -0,0058 0,0062 -0,0029 0,0044 -0,0017 0,0025 -0,0056 0,0067 -0,0027

BANKINTER, S.A. 0,0050 0,0071** 0,0173** 0,0122** 0,0074* 0,0093** 0,0047 0,0070** 0,0169** 0,0120** 0,0070 0,0091**

BARCLAYS -0,0011 0,0049 -0,0047 0,0082 -0,0005 0,0037 -0,0027 0,0042 -0,0077 0,0068 -0,0024 0,0028

BNP PARIBAS 0,0015 0,0097** 0,0165** 0,0236** 0,0048 0,0141** -0,0008 0,0087** 0,0125* 0,0217** 0,0020 0,0127**

COMMERZBANK AG 0,0002 0,0124** -0,0025 0,0238** -0,0015 0,0144** -0,0005 0,0122** -0,0035 0,0234** -0,0023 0,0140**

CREDIT AGRICOLE 0,0035 0,0108** 0,0119** 0,0265* 0,0064 0,0164** 0,0021 0,0102** 0,0096 0,0254** 0,0048 0,0156**

DANSKE BANK 0,0013 0,0065** 0,0049 0,0085** 0,0048 0,0060** 0,0010 0,0064** 0,0042 0,0082* 0,0044 0,0058*

DEUTSCHE BANK 0,0002 0,0037** -0,0081 0,0110** -0,0016 0,0024 -0,0008 0,0003 -0,0102 0,0099** -0,0029 0,0017

DEUTSCHE POSTB -0,0008 0,0011 0,0000 0,0028 0,0016 0,0020 0,0000 0,0014 0,0015 0,0035 0,0026 0,0025

DEXIA 0,0014 0,0162** 0,0123 0,0189** 0,0072 0,0173** 0,0001 0,0156** 0,0101 0,0180** 0,0057 0,0166**

Appendix 2.1

EFG EUROBANK ERGASIAS S.A.

0,0189** 0,0219** 0,0263** 0,0298** 0,0068 0,0304** 0,0176** 0,0214** 0,0243* 0,0290** 0,0053 0,0297**

ERSTE GR BANK AG 0,0052 0,0042 0,0147* 0,0125** 0,0059 0,0022 0,0045 0,0038 0,0133 0,0119* 0,0050 0,0017

ESPÍRITO santo FIN GR 0,0000 -0,0018 -0,0074 -0,0040 -0,0031 -0,0024 0,0013 -0,0013 -0,0051 -0,0029 -0,0015 -0,0017

HSBC HOLDINGS -0,0002 -0,0002 -0,0009 -0,0009 -0,0021 -0,0005 -0,0008 -0,0005 -0,0020 -0,0014 -0,0029 -0,0009

ING Bank 0,0047 0,0080** 0,0042 0,0137** 0,0073 0,0084** 0,0025 0,0070** 0,0001 0,0118* 0,0047 0,0070*

INTESA SANPAOLO 0,0041 0,0109** 0,0136** 0,0264** 0,0051 0,0138** 0,0022 0,0101** 0,0102 0,0249** 0,0028 0,0126**

JYSKE BANK A/S 0,0021 0,0011 0,0073 0,0024 0,0015 0,0017 0,0021 0,0012 0,0074 0,0024 0,0016 0,0017

KBC GROUP 0,0017 0,0081* 0,0010 0,0203** 0,0060 0,0119** -0,0004 0,0072 0,0061 0,0186** 0,0036 0,0107

LB BERLIN AG -0,0033 0,0033 0,0023 0,0088 -0,0028 0,0056 -0,0021 0,0039 0,0046 0,0099* -0,0013 0,0063

LLOYDS BANKING -0,0010 0,0042 -0,0050 0,0064 -0,0027 0,0028 -0,0037 0,0030 -0,0098 0,0042 -0,0059 0,0012

MARFIN POPULAR 0,0132** 0,0113** 0,0245** 0,0138** 0,0110* 0,0182** 0,0130** 0,0113** 0,0245** 0,0140** 0,0109* 0,0182**

MONTE DEI PASCHI DI SIENA

0,0037 0,0059** 0,0095* 0,0128** 0,0064* 0,0052* 0,0026** 0,0054** 0,0078 0,0120** 0,0051 0,0046

NAT BANK OF GREECE 0,0217** 0,0202** 0,0313** 0,0272** 0,0097 0,0281** 0,0213** 0,0200** 0,0309** 0,0271** 0,0092 0,0278**

NORDEA BANK 0,0012 0,0042* 0,0077 0,0046 -0,0006 0,0044 -0,0006 0,0035 0,0045 0,0031 -0,0028 0,0033

OP-POHJOLA GROUP 0,0025 0,0047* 0,0170** 0,0137** 0,0065 0,0056* 0,0015 0,0043 0,0154** 0,0130** 0,0054 0,0051

OTP BANK NYRT. 0,0047 0,0034 0,0125 0,0095* 0,0053 0,0007 0,0040 0,0031 0,0113 0,0090 0,0045 0,0003

PIRAEUS BANK GR 0,0231** 0,0208** 0,0333** 0,0246** 0,0133* 0,0270** 0,0223** 0,0205** 0,0324** 0,0243** 0,0125 0,0267**

PKO BANK POLSKI 0,0049 0,0015 0,0064 0,0022 0,0039 0,0002 0,0042 0,0012 0,0052 0,0016 0,0030 -0,0002

RAIFFEISEN 0,0010 0,0055 -0,0012 0,0117** 0,0038 0,0044 0,0015 0,0058 -0,0002 0,0123** 0,0045 0,0047

ROYAL BANK of SCOTL -0,0034 0,0037 -0,0032 0,0067 -0,0048 0,0036 -0,0041 0,0034 -0,0047 0,0060 -0,0058 0,0031

SKANDIN. ENSKILDA BANKEN (SEB)

-0,0027 0,0019 0,0009 0,0039 -0,0026 0,0006 -0,0035 0,0015 -0,0005 0,0033 -0,0035 0,0001

SNS BANK 0,0041 0,0040 0,0060 0,0118** 0,0031 0,0028 0,0024 0,0032 0,0028 0,0103* 0,0009 0,0017

SOCIETE GENERALE 0,0009 0,0102** 0,0118 0,0251** 0,0044 0,0142** -0,0012 0,0093** 0,0081 0,0234** 0,0019 0,0129**

SVENSKA HANDELSB. -0,0020 0,0033 0,0049 0,0038 -0,0010 0,0031 -0,0030 0,0028 0,0031 0,0029 -0,0022 0,0025

SWEDBANK -0,0003 0,0046 0,0077 0,0061 0,0020 0,0026 -0,0013 0,0042 0,0057 0,0051 0,0008 0,0019

SYDBANK A/S 0,0019 0,0053** 0,0078 0,0063 0,0016 0,0061** 0,0024 0,0056** 0,0088 0,0067* 0,0022 0,0064**

TT HELLENIC POSTB 0,0141** 0,0202** 0,0262** 0,0238** 0,0067 0,0257** 0,0136 0,0200** 0,0256* 0,0236** 0,0061 0,0255**

UNICREDIT 0,0029 0,0080** 0,0077** 0,0246** 0,0026 0,0077** 0,0013 0,0073** 0,0049 0,0233** 0,0007 0,0067*

UNIONE DI BANCHE ITALIANE (UBI)

0,0028 0,0081** 0,0035** 0,0214** 0,0040 0,0096** 0,0011 0,0074** 0,0007 0,0202** 0,0021 0,0086**

Appendix 2.1

Table: AARs (=γi) resulting from step 1 for the time split dummies applied for the stocks for the group of 9 strong events. (* denotes significance at

the 10 percent level, ** denotes significance at the 5 percent level) (source: author’s calculations based on data from Datastream)

TIME SPLIT

STOCK Strong (9) events

Rit = αi+ βi*Rmt +

γbefore,i*Dbefore,t +

γafter,i*Dafter,t + εit

2006-2011 2008-2011

[-1,1] [0] [0,1] [-1,1] [0] [0,1]

Before After Before After Before After Before After Before After Before After

AGRICULTURAL BANK OF GREECE 0,0196* 0,0548** 0,0477** 0,0404** 0,0216 0,0715** 0,0179 0,0541** 0,0430* 0,0393* 0,0186 0,0706**

ALLIED IRISH BANKS 0,0007 -0,0080 0,0329 -0,0059 0,0055 0,0226 -0,0018 -0,0099 0,0246 -0,0084 0,0003 0,0204

ALPHA BANK 0,0211** 0,0359** 0,0458** 0,0294** 0,0255** 0,0417** 0,0197 0,0356** 0,0426** 0,0288* 0,0233 0,0413**

BANCO BPI 0,0198** 0,0235** 0,0171* 0,0443** 0,0194** 0,0295** 0,0191** 0,0235** 0,0148 0,0437 0,0180** 0,0289**

BANCO COMERCIAL PORTUGUÊS 0,0211** 0,0174** 0,0427** 0,0293** 0,0205** 0,0177** 0,0205** 0,0172** 0,0413** 0,0292** 0,0194** 0,0175**

BANCO DE SABADELL 0,0005** 0,0090* 0,0167** 0,0191** 0,0052 0,0110** -0,0002** 0,0087** 0,0151** 0,0188** 0,0040 0,0107**

BANCO PASTOR -0,0058 -0,0020 -0,0086 0,0084 -0,0097 -0,0005 -0,0053 -0,0009 -0,0069 0,0099 -0,0081 0,0007

BANCO POPOLARE 0,0014 0,0207** 0,0047 0,0345** -0,0034 0,0161** 0,0000 0,0197** 0,0000 0,0332** -0,0065 0,0149*

BANCO POPULAR ESPAÑOL, S.A. 0,0088* 0,0112** 0,0401** 0,0259** 0,0181** 0,0171** 0,0080 0,0107** 0,0379** 0,0252** 0,0165** 0,0165**

BANCO SANTANDER 0,0081* 0,0082** 0,0349** 0,0239** 0,0171** 0,0108** 0,0071 0,0071 0,0312** 0,0225** 0,0148** 0,0096**

BANK OF CYPRUS PUBLIC CO LTD 0,0095 0,0233** 0,0197 0,0290** 0,0176* 0,0376** 0,0091 0,0239** 0,0200 0,0298** 0,0176* 0,0383**

BANK OF IRELAND 0,0074 -0,0054 0,0086 -0,0015 0,0090 -0,0059 0,0069 -0,0062 0,0060 -0,0026 0,0075 -0,0068

BANK OF VALLETTA -0,0016 -0,0072 -0,0018 -0,0156* 0,0000 -0,0094 -0,0011 -0,0073 -0,0009 -0,0156* 0,0006 -0,0093

BANKINTER, S.A. 0,0016 0,0119** 0,0255** 0,0189** 0,0091 0,0151** 0,0011 0,0120** 0,0249** 0,0191** 0,0085 0,0152**

BARCLAYS 0,0010 0,0151** 0,0020 0,0256** 0,0052 0,0116 -0,0003 0,0131 -0,0045 0,0228 -0,0032 0,0093

BNP PARIBAS -0,0046 0,0205** 0,0247** 0,0441** 0,0014 0,0341** -0,0068 0,0181** 0,0163 0,0409** -0,0038 0,0313**

COMMERZBANK AG -0,0004 0,0304** -0,0103 0,0449** -0,0076 0,0259** -0,0011 0,0301** -0,0124 0,0444** -0,0090 0,0255**

CREDIT AGRICOLE 0,0022 0,0249** 0,0181* 0,0513** 0,0073 0,0387** 0,0009 0,0236** 0,0132 0,0495** 0,0043 0,0372**

DANSKE BANK 0,0009 0,0102** 0,0044 0,0177** 0,0026 0,0081 0,0006 0,0097* 0,0029 0,0170* 0,0018 0,0076

DEUTSCHE BANK 0,0005 0,0149** 0,0013 0,0308** -0,0014 0,0165** -0,0004 0,0134** -0,0032 0,0289** -0,0040 0,0148**

DEUTSCHE POSTB -0,0024 -0,0071 0,0051 -0,0041 0,0024 -0,0060 -0,0016 -0,0064 0,0081 -0,0031 0,0042 -0,0052

DEXIA -0,0003 0,0295** 0,0190 0,0325** 0,0091 0,0273** -0,0018 0,0288** 0,0148 0,0314** 0,0064 0,0264**

Appendix 2.1

EFG EUROBANK ERGASIAS S.A. 0,0115 0,0375** 0,0219 0,0438** 0,0070 0,0446** 0,0100 0,0369** 0,0179 0,0430** 0,0043 0,0439**

ERSTE GR BANK AG 0,0022 0,0090 -0,0043 0,0270** -0,0041 0,0007 0,0016 0,0080 -0,0074 0,0257** -0,0060 -0,0005

ESPÍRITO SANTO FIN GR S.A. (ESFG) 0,0019 -0,0018 -0,0102 0,0007 -0,0064 -0,0013 0,0030 -0,0004 -0,0052 0,0013 -0,0035 0,0004

HSBC HOLDINGS 0,0032 -0,0032 0,0041 -0,0012 -0,0013 -0,0008 0,0026 -0,0038 0,0019 -0,0019 -0,0027 -0,0014

ING Bank 0,0105 0,0182** 0,0203 0,0278** 0,0132 0,0167** 0,0086 0,0156** 0,0116 0,0242** 0,0081 0,0137*

INTESA SANPAOLO 0,0027 0,0278** 0,0191* 0,0536** 0,0021 0,0333** 0,0009 0,0259** 0,0120 0,0510** -0,0022 0,0310**

JYSKE BANK A/S -0,0054 0,0029 0,0038 0,0104 -0,0052 0,0082 -0,0054 0,0029 0,0037 0,0103 -0,0053 0,0081

KBC GROUP -0,0006 0,0216** 0,0165 0,0387** 0,0037 0,0217** -0,0026 0,0196* 0,0090 0,0360** -0,0009 0,0193

LB BERLIN AG -0,0004 0,0091 -0,0102 0,0217** -0,0054 0,0147* 0,0007 0,0105 -0,0054 0,0237** -0,0026 0,0164**

LLOYDS BANKING 0,0008 0,0102 -0,0014 0,0221 -0,0050 0,0045 -0,0017 0,0074 -0,0115 0,0183 -0,0121 0,0012

MARFIN POPULAR 0,0164** 0,0236** 0,0209 0,0140 0,0178* 0,0382** 0,0159* 0,0244** 0,0214 0,0150 0,0179* 0,0391**

MONTE DEI PASCHI DI SIENA 0,0007 0,0195** 0,0086 0,0280** 0,0010 0,0141** -0,0004** 0,0188** 0,0051 0,0270** -0,0013 0,0132**

NAT BANK OF GREECE 0,0199** 0,0359** 0,0439** 0,0449** 0,0207* 0,0436** 0,0193* 0,0362** 0,0433** 0,0453** 0,0200 0,0439**

NORDEA BANK -0,0025 0,0073 0,0030 0,0125* -0,0127 0,0068 -0,0041 0,0054 -0,0039 0,0097 -0,0169** 0,0045

OP-POHJOLA GROUP 0,0038 0,0084 0,0323** 0,0230** 0,0069 0,0100* 0,0029 0,0075 0,0291** 0,0219** 0,0048 0,0089

OTP BANK NYRT. 0,0020 -0,0050 0,0182 0,0000 0,0062** -0,0102 0,0014 -0,0059 0,0155 -0,0010 0,0046 -0,0111

PIRAEUS BANK GR 0,0242** 0,0269** 0,0442** 0,0358** 0,0223** 0,0343** 0,0231** 0,0274** 0,0430** 0,0364** 0,0212 0,0348**

PKO BANK POLSKI 0,0069 -0,0017 0,0111 -0,0032 0,0084 -0,0068 0,0061 -0,0023 0,0087 -0,0039 0,0068 -0,0075

RAIFFEISEN ZB OESTERRREICH 0,0053 0,0063 -0,0041 0,0233** 0,0040 0,0034 0,0057 0,0071 -0,0018 0,0244** 0,0053 0,0043

ROYAL BANK OF SCOTLAND (RBS) 0,0032 0,0076 0,0128 0,0141 -0,0009 0,0046 0,0027 0,0065 0,0096 0,0127 -0,0028 0,0033

SKANDIN. ENSKILDA BANKEN (SEB) -0,0097 0,0045 -0,0130 0,0119

-

0,0168** 0,0028 -0,0104 0,0036 -0,0161 0,0107 -0,0187* 0,0017 SNS BANK 0,0043 0,0118* 0,0062 0,0189** 0,0024 0,0152** 0,0026 0,0100 -0,0005 0,0164 -0,0016 0,0131*

SOCIETE GENERALE -0,0029 0,0296** 0,0211* 0,0536** 0,0010 0,0410** -0,0049 0,0275** 0,0133 0,0507** -0,0037 0,0385**

SVENSKA HANDELSB. -0,0031 0,0012 0,0005 0,0014 -0,0070 0,0004 -0,0039 0,0000 -0,0035 -0,0002 -0,0093 -0,0010

SWEDBANK -0,0111 0,0092 -0,0063 0,0101 -0,0125 0,0021 -0,0119 0,0077 -0,0109 0,0080 -0,0152 0,0003

SYDBANK A/S -0,0035 0,0075 0,0042 0,0207** -0,0067 0,0166** -0,0031 0,0081* 0,0063 0,0216** -0,0056 0,0174**

TT HELLENIC POSTB 0,0163 0,0446** 0,0426** 0,0473** 0,0131 0,0485** 0,0156** 0,0448** 0,0416* 0,0475** 0,0122 0,0486**

UNICREDIT 0,0044 0,0191** 0,0131 0,0399** 0,0030 0,0144** 0,0029 0,0175** 0,0072 0,0377** -0,0006 0,0125

UNIONE DI BANCHE ITALIANE (UBI) 0,0005 0,0131** 0,0055 0,0346** -0,0015 0,0168** -0,0011 0,0118** -0,0002 0,0328** -0,0051 0,0152**

Appendix 2.2

Appendix 2.2 AARs for CDS return AARs (=γi) resulting from step 1 for the CDSs for the group of 29 events. (* denotes significance

at the 10 percent level, ** denotes significance at the 5 percent level) (source: author’s calculations

based on data from Bloomberg)

CDS All (29) events

Ri,t = αi+ βi*Rm,t + γi*Dt + εi,t [-1,1] [0] [0,1] [0,3] [0,5]

ABN/ FORTIS BANK NL (HOLDING) N.V 0,0000 -0,0012 -0,0009 -0,0075** -0,0061**

ALLIED IRISH BANKS -0,0051 -0,0094 -0,0092 -0,0094 -0,0081*

ALPHA BANK 0,0003 -0,0066 -0,0029 -0,0025 -0,0018

BANCO COMERCIAL PORTUGUÊS -0,0045 -0,0073 -0,0065 -0,0078** -0,0062**

BANCO POPOLARE -0,0044 -0,0090 -0,0054 -0,0019 -0,0007

BANK OF IRELAND -0,0021 -0,0043 -0,0057 -0,0053 -0,0048

BARCLAYS -0,0025 -0,0070 -0,0060 -0,0034 -0,0017

BAYERISCHE LANDESBANK -0,0048 -0,0071 -0,0088** -0,0051* -0,0027

BNP PARIBAS -0,0059 -0,0080** -0,0083* -0,0042 -0,0028

CAIXA -0,0044 0,0042 -0,0076* -0,0066** -0,0045*

COMMERZBANK AG 0,0017 -0,0063 -0,0071 -0,0048 -0,0022

CREDIT AGRICOLE -0,0100** -0,0148** -0,0155** -0,0092** -0,0067**

DANSKE BANK -0,0003 -0,0016 -0,0020 -0,0024 -0,0024

DEUTSCHE BANK AG 0,0003 -0,0019 -0,0028 -0,0028 -0,0001

ERSTE GR BANK AG -0,0017 -0,0039 -0,0065 -0,0040 -0,0042

ESPÍRITO SANTO FIN GR S.A. (ESFG) -0,0089** -0,0071 -0,0114** -0,0093** -0,0074**

HSBC HOLDINGS PLC -0,0003 0,0008 -0,0041 -0,0047 -0,0027

HSH NORDBANK AG 0,0022 0,0021 0,0003 -0,0004 -0,0006

ING Bank -0,0011 -0,0040 -0,0049 -0,0038 -0,0029

INTESA SANPAOLO -0,0021 -0,0093 -0,0095* -0,0058 -0,0041

KBC GROUP -0,0004 -0,0014 -0,0051 -0,0029 -0,0021

LANDESBANK BADEN-WÜRTTEMBERG 0,0027 -0,0008 -0,0022 -0,0027 -0,0009

LANDESBANK HESSEN-THÜRINGEN GZ 0,0006 0,0003 -0,0021 -0,0021 -0,0018

LLOYDS BANKING GR 0,0015 -0,0021 -0,0025 -0,0037 -0,0027

MONTE DEI PASCHI DI SIENA -0,0023 -0,0083 -0,0070 -0,0037 -0,0023

NORDDEUTSCHE LANDESBANK -GZ- -0,0001 -0,0011 -0,0008 -0,0005 -0,0003

NORDEA BANK 0,0027 -0,0006 0,0027 0,0000 -0,0003

RABOBANK GROUP -0,0014 -0,0069 -0,0060 -0,0040 -0,0035

RAIFFEISEN ZB OESTERRREICH -0,0011 -0,0069 -0,0054 -0,0058** -0,0038

ROYAL BANK OF SCOTLAND (RBS) 0,0028 -0,0004 -0,0024 -0,0049 -0,0034

SKANDIN. ENSKILDA BANKEN (SEB) -0,0055 -0,0054 -0,0054 -0,0032 -0,0024

SNS BANK -0,0006 -0,0052 -0,0007 -0,0015 -0,0011

SOCIETE GENERALE -0,0118** -0,0153** -0,0139** -0,0078** -0,0063**

SVENSKA HANDELSB. -0,0018 -0,0073 -0,0025 -0,0034 -0,0021

SWEDBANK -0,0018 -0,0050 -0,0031 -0,0035* -0,0026

UNIONE DI BANCHE ITALIANE (UBI) -0,0008 0,0026 -0,0015 -0,0049 -0,0030

UNICREDIT -0,0015 -0,0099 -0,0082 -0,0055 -0,0030

WESTLB AG -0,0052 -0,0098 -0,0105** -0,0084** -0,0047

Appendix 2.2

AARs (=γi) resulting from step 1 for the CDSs for the group of 9 events. (* denotes significance at

the 10 percent level, ** denotes significance at the 5 percent level) (source: author’s calculations

based on data from Bloomberg)

CDS All (9) events

Ri,t = αi+ βi*Rm,t + γi*Dt + εi,t [-1,1] [0] [0,1] [0,3] [0,5]

ABN/ FORTIS BANK NL (HOLDING) N.V 0,0083 0,0011 0,0042 -0,0080 -0,0034

ALLIED IRISH BANKS -0,0193** -0,0448** -0,0260 -0,0223* -0,0119

ALPHA BANK -0,0060 -0,0166 -0,0049 -0,0046 -0,0035

BANCO COMERCIAL PORTUGUÊS 0,0024 -0,0031 0,0028 -0,0065 -0,0016

BANCO POPOLARE 0,0025 0,0050 0,0040 0,0000 0,0014

BANK OF IRELAND -0,0065 0,0035 -0,0180 -0,0091 -0,0041

BARCLAYS -0,0020 -0,0014 -0,0050 -0,0046 -0,0019

BAYERISCHE LANDESBANK -0,0064 -0,0065 -0,0112* -0,0095* -0,0035

BNP PARIBAS -0,0061 -0,0018 -0,0134 -0,0121* -0,0101*

CAIXA 0,0072 0,0279** 0,0084 0,0001 0,0034

COMMERZBANK AG 0,0055 0,0078 0,0026 -0,0074 -0,0031

CREDIT AGRICOLE -0,0118 -0,0174 -0,0177** -0,0016 -0,0093

DANSKE BANK 0,0048 0,0054 0,0031 -0,0026 -0,0015

DEUTSCHE BANK AG 0,0021 0,0106 0,0044 -0,0045 -0,0009

ERSTE GR BANK AG -0,0037 0,0120 -0,0116 -0,0026 -0,0057

ESPÍRITO SANTO FIN GR S.A. (ESFG) -0,0045 0,0008 -0,0048 -0,0098 -0,0041

HSBC HOLDINGS PLC 0,0049 0,0148 0,0050 -0,0027 0,0013

HSH NORDBANK AG 0,0022 -0,0076 -0,0026 -0,0036 -0,0055

ING Bank 0,0061 0,0213* 0,0086 -0,0057 -0,0027

INTESA SANPAOLO 0,0044 -0,0014 -0,0059 -0,0076 -0,0109

KBC GROUP 0,0013 -0,0059 -0,0034 -0,0046 -0,0025

LANDESBANK BADEN-WÜRTTEMBERG -0,0001 -0,0031 -0,0069 -0,0073* -0,0022

LANDESBANK HESSEN-THÜRINGEN GZ 0,0013 0,0065 -0,0016 -0,0030 -0,0018

LLOYDS BANKING GR 0,0067 0,0055** 0,0016 -0,0077 -0,0028

MONTE DEI PASCHI DI SIENA 0,0070 -0,0075 -0,0048 -0,0080 -0,0041

NORDDEUTSCHE LANDESBANK -GZ- -0,0038 -0,0085 -0,0054 -0,0048* -0,0022

NORDEA BANK 0,0099* 0,0050 0,0098 0,0013 -0,0001

RABOBANK GROUP 0,0009 -0,0025 -0,0039 -0,0055 -0,0049

RAIFFEISEN ZB OESTERRREICH 0,0005 -0,0092 -0,0021 -0,0079 -0,0040

ROYAL BANK OF SCOTLAND (RBS) 0,0109 0,0030 -0,0004 -0,0065 -0,0013

SKANDIN. ENSKILDA BANKEN (SEB) -0,0023 -0,0046 -0,0014 -0,0056 -0,0016

SNS BANK 0,0018 -0,0107 0,0033 -0,0041 -0,0027

SOCIETE GENERALE -0,0108 -0,0035 -0,0127 -0,0103 -0,0082

SVENSKA HANDELSB. 0,0046 -0,0028 0,0068 -0,0023 -0,0022

SWEDBANK 0,0021 0,0020 0,0004 -0,0048 -0,0007

UNIONE DI BANCHE ITALIANE (UBI) 0,0127 0,0173 0,0057 -0,0030 -0,0006

UNICREDIT 0,0045 -0,0037 -0,0088 -0,0128* -0,0069

WESTLB AG 0,0004 0,0032 -0,0059 -0,0113 -0,0073

Appendix 3.1

Appendix 3: Results regressions second step stock return

Non-time split Time split

General General Before After

All AARs app 3.1.1 app 3.1.2 app 3.1.3 app 3.1.4

Only significant

AARs app 3.2.1 app 3.2.2 / /

** significant at the 5% level

* significant at the 10% level

not significantly different than 0 at 10% level

Appendix 3.1 Regressions based on all AARs (significant and insignificant)

Appendix 3.1.1 General regressions for (51) AARs from not-time split dummies

Event group Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/ TA loans /TA R²

All (29) 2006-

2011 [-1,1] -0,010 0,096** -0,025 0,001* 0,010 0,010** 65%

[0] -0,017 0,117** -0,030 0,002** 0,012 0,019** 43%

[0,1] -0,011 0,102** -0,037 0,002** 0,015 0,009 58%

2008-

2011 [-1,1] -0,007 0,093** -0,026 0,001 0,010 0,008 65%

[0] -0,011 0,110** -0,029 0,002* 0,010 0,013 40%

[0,1] -0,008 0,098** -0,039 0,001 0,015 0,007 58%

Strong (9) 2006-

2011 [-1,1] -0,005 0,181** -0,069 0,002* 0,010 0,003 65%

[0] -0,018 0,212** -0,033 0,005** 0,001 0,423 42%

[0,1] -0,001 0,209** -0,128* 0,002 0,018 0,000 57%

2008-

2011 [-1,1] -0,001 0,178** -0,073 0,001 0,011 0,001 65%

[0] -0,001 0,205** -0,043 0,004* 0,002 0,011 41%

[0,1] 0,006 0,204** -0,135** 0,001 0,019 -0,005 58%

Appendix 3.1

Events

type

time

frame

event

window Constant GEXPR T1CR ln(TA) dep/TA loans /TA PIIGS R²

All (29) 2006-

2011 [-1,1] -0,014 0,079** 0,003 0,001** 0,014* 0,007 0,003* 68%

[0] -0,025 0,084** 0,024 0,003** 0,019 0,014 0,006* 48%

[0,1] -0,015 0,085** -0,010 0,002** 0,019* 0,006 0,003 60%

2008-

2011 [-1,1] -0,012 0,075** 0,004 0,001* 0,014* 0,005 0,003** 68%

[0] -0,018 0,080** 0,021 0,002* 0,016 0,008 0,005* 45%

[0,1] -0,012 0,080** -0,010 0,001* 0,019* 0,003 0,003 60%

Strong

(9)

2006-

2011 [-1,1] -0,010 0,158** -0,033 0,002** 0,015 -0,001 0,004 67%

[0] -0,032 0,154** 0,063 0,005** 0,012 0,008 0,010** 48%

[0,1] -0,006 0,189** -0,094 0,002* 0,022 -0,003 0,003 58%

2008-

2011 [-1,1] -0,006 0,154** -0,034 0,002 0,015 -0,004 0,004 68%

[0] -0,023 0,142** 0,060 0,004** 0,015 0,000 0,011** 48%

[0,1] 0,001 0,181** -0,097 0,002 0,024 -0,009 0,004 59%

Event

group

Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,004 0,025 -0,022 0,001 0,008 0,005 0,012** 0,001 75%

[0] -0,019 0,050 0,008 0,002* 0,015 0,012 0,012* 0,005 49%

[0,1] -0,007 0,035 -0,033 0,001* 0,013 0,004 0,012** 0,001 64%

2008-

2011 [-1,1] -0,002 0,021 -0,021 0,000 0,009 0,003 0,012** 0,001 76%

[0] -0,012 0,043 0,004 0,002 0,013 0,007 0,011* 0,004 47%

[0,1] -0,004 0,030 -0,032 0,001 0,014 0,001 0,012** 0,001 65%

Strong

(9)

2006-

2011 [-1,1] -0,019 0,111** -0,054 0,002* 0,010 -0,003 0,012** 0,002 69%

[0] -0,027 0,127* 0,050 0,005** 0,009 0,006 0,015 0,009* 48%

[0,1] 0,001 0,152** -0,111 0,002** 0,018 -0,005 0,010 0,002* 59%

2008-

2011 [-1,1] 0,002 0,106** -0,056 0,001 0,011 -0,006 0,012** 0,003 70%

[0] -0,019 0,114 0,048 0,004** 0,012 -0,001 0,016 0,010** 48%

[0,1] 0,007 0,144** -0,114 0,001 0,020 -0,010 0,010 0,003 59%

Appendix 3.1

GEXPR for

Event group Time

frame

Event

window Constant

non-Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,006 0,210** -0,002 0,005 0,001* 0,001 0,005 0,018** 0,002* 82%

[0] -0,021 0,338** 0,009 0,049 0,002** 0,005 0,013 0,020** 0,006** 57%

[0,1] -0,008 0,287** 0,000 0,003 0,001** 0,004 0,004 0,019** 0,003* 74%

2008-

2011 [-1,1] -0,003 0,211** -0,006 0,006 0,001 0,002 0,003 0,018** 0,003** 83%

[0] -0,013 0,333** 0,003 0,046 0,002* 0,002 0,007 0,020** 0,006** 55%

[0,1] -0,005 0,289** -0,006 0,005 0,001 0,005 0,002 0,019** 0,003* 75%

Strong (9) 2006-

2011 [-1,1] -0,004 0,425** 0,067** -0,009 0,002* -0,002 -0,002 0,021** 0,004* 75%

[0] -0,029 0,412** 0,087 0,091 0,005** -0,001 0,007 0,023** 0,011** 51%

[0,1] -0,002 0,622** 0,086** -0,044 0,002* 0,001 -0,004 0,023** 0,005 68%

2008-

2011 [-1,1] 0,000 0,434** 0,060* -0,009 0,001 -0,001 -0,005 0,022** 0,005** 77%

[0] -0,020 0,436** 0,070 0,094 0,004** 0,000 -0,001 0,025** 0,012** 52%

[0,1] 0,004 0,640** 0,074* -0,043 0,001 0,002 -0,010 0,024** 0,006* 71%

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant T1CR ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-

2011 [-1,1] -0,006 -0,008 0,001 0,008 0,005 0,012* 0,002 0,037 0,033 0,030 0,026 0,023 75%

[0] -0,022 0,027 0,002* 0,015 0,012 0,011* 0,005 0,068 0,063 0,058 0,053 0,048 49%

[0,1] -0,008 -0,018 0,001* 0,013 0,004 0,011** 0,002 0,049 0,045 0,041 0,037 0,033 65%

2008-

2011 [-1,1] -0,003 -0,014 0,001 0,009 0,003 0,014** 0,002 0,022 0,021 0,021 0,021 0,020 76%

[0] -0,014 0,023 0,002 0,013 0,006 0,011* 0,005 0,062 0,057 0,051 0,046 0,041 47%

[0,1] -0,006 -0,016 0,001 0,014 0,001 0,011** 0,002 0,046 0,041 0,037 0,033 0,029 65%

Strong

(9)

2006-

2011 [-1,1] -0,007 -0,014 0,002* 0,010 -0,003 0,011* 0,004 0,149** 0,139** 0,128** 0,117** 0,107** 69%

[0] -0,032 0,087 0,005** 0,010 0,006 0,014 0,010* 0,162* 0,152** 0,142** 0,133** 0,123** 49%

[0,1] -0,009 -0,030 0,002 0,019 -0,005 0,008 0,005 0,229** 0,207** 0,186** 0,165** 0,144** 60%

2008-

2011 [-1,1] -0,001 -0,034 0,001 0,011 -0,006 0,017 0,003 0,110** 0,109** 0,108** 0,106** 0,105** 70%

[0] -0,024 0,089 0,004** 0,012 -0,001 0,014 0,011** 0,154* 0,143* 0,132** 0,121** 0,110* 49%

[0,1] 0,001 -0,063 0,001 0,021 -0,010 0,022 0,004 0,153** 0,150** 0,148** 0,145** 0,142** 61%

Appendix 3.1

Appendix 3.1.2 General regressions for (102) AARs from time split dummies

Event group Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/TA loans /TA R²

All (29) 2006-

2011 [-1,1] -0,010 0,094** -0,034 0,001** 0,013** 0,011** 57%

[0] -0,015 0,117** -0,041 0,002** 0,015 0,017** 37%

[0,1] -0,132 0,065** -0,044 0,001 0,024** 0,007** 35%

2008-

2011 [-1,1] -0,007 0,090** -0,037* 0,001 0,014** 0,009** 56%

[0] -0,011 0,108** -0,048 0,002* 0,018 0,013* 36%

[0,1] -0,008 0,072** -0,061** 0,001 0,024** 0,004 40%

Strong (9) 2006-

2011 [-1,1] -0,013 0,168** -0,086* 0,003** 0,020 0,007 42%

[0] -0,025 0,200** -0,072 0,005** 0,021 0,017 30%

[0,1] -0,006 0,180** -0,172** 0,003* 0,039** -0,004 42%

2008-

2011 [-1,1] -0,009 0,164** -0,090* 0,002* 0,020 0,004 42%

[0] 0,001 0,207** -0,119* 0,003* 0,014 0,000 31%

[0,1] 0,003 0,176** -0,188** 0,002 0,038** -0,008 42%

Event group Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/TA loans /TA PIIGS R²

All (29) 2006-

2011 [-1,1] -0,013* 0,079** -0,013 0,001** 0,016** 0,008* 0,003** 59%

[0] -0,020 0,094** -0,008 0,002** 0,020* 0,013* 0,004* 39%

[0,1] -0,017* 0,050** -0,023 0,002** 0,027** 0,004 0,003 36%

2008-

2011 [-1,1] -0,011* 0,074** -0,015 0,001 0,017** 0,006 0,003** 58%

[0] -0,016 0,084** -0,013 0,002* 0,023** 0,008 0,004* 38%

[0,1] -0,010 0,060** -0,044 0,001 0,027** 0,001 0,002 41%

Strong (9) 2006-

2011 [-1,1] -0,015 0,158** -0,071 0,003** 0,022 0,005 0,002 43%

[0] -0,037 0,145** 0,006 0,005** 0,033 0,006 0,010** 34%

[0,1] -0,009 0,163** -0,148** 0,003** 0,042** -0,007 0,003 42%

2008-

2011 [-1,1] -0,012 0,152** -0,072 0,002** 0,023* 0,002 0,002 42%

[0] -0,008 0,165** -0,059 0,003** 0,023 -0,008 0,007* 33%

[0,1] 0,000 0,164** -0,171** 0,002 0,040** -0,010 0,002 43%

Appendix 3.1

Event group Time

frame

Event

window Constant

Greek exp

ratio Tier 1 rat ln(TA)

deposits

/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,005 0,025 -0,033 0,001 0,011* 0,006 0,012** 0,001 65%

[0] -0,014 0,054 -0,024 0,002** 0,016 0,012 0,011** 0,003 40%

[0,1] -0,011 0,014 -0,037 0,001* 0,023** 0,003 0,009** 0,002 39%

2008-

2011 [-1,1] -0,003 0,021 -0,035 0,000 0,012* 0,004 0,012** 0,001 64%

[0] -0,010 0,045 -0,029 0,001 0,019 0,007 0,011** 0,003 40%

[0,1] -0,005 0,026 -0,057* 0,001 0,023** 0,000 0,008** 0,001 42%

Strong (9) 2006-

2011 [-1,1] -0,009 0,115** -0,087* 0,002** 0,018 0,003 0,009 0,001 44%

[0] -0,033 0,118* -0,004 0,005** 0,031 0,005 0,014 0,009** 34%

[0,1] -0,004 0,132** -0,160** 0,003* 0,039** -0,008 0,008 0,002 43%

2008-

2011 [-1,1] -0,005 0,111** -0,088* 0,002 0,019 0,000 0,009 0,001 43%

[0] 0,001 0,107 -0,081 0,003* 0,017 -0,010 0,017* 0,006 34%

[0,1] 0,005 0,134** -0,183** 0,002 0,037** -0,011 0,007 0,001 43%

GEXPR for

Event group Time

frame

Event

window Constant

non-Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,005 0,190** 0,002 -0,017 0,001* 0,006 0,006 0,016** 0,002* 70%

[0] -0,015 0,288** 0,021 0,000 0,002** 0,009 0,011 0,017** 0,004* 45%

[0,1] -0,011 0,214** -0,014 -0,017 0,001** 0,017* 0,003 0,014** 0,003 44%

2008-

2011 [-1,1] -0,003 0,187** -0,002 -0,019 0,000 0,007 0,004 0,016** 0,002* 69%

[0] -0,010 0,283** 0,012 -0,005 0,001 0,012 0,007 0,018** 0,004* 44%

[0,1] -0,005 0,204** 0,001 -0,039 0,001 0,018** 0,000 0,013** 0,002 46%

Strong (9) 2006-

2011 [-1,1] -0,009 0,358** 0,081** -0,063 0,002** 0,011 0,003 0,016** 0,002 47%

[0] -0,033 0,299 0,092* 0,014 0,005** 0,025 0,005 0,019* 0,010** 35%

[0,1] -0,005 0,491** 0,081* -0,124* 0,003* 0,028 -0,008 0,018** 0,004 47%

2008-

2011 [-1,1] -0,006 0,365** 0,074** -0,063 0,002* 0,011 0,000 0,016** 0,002 46%

[0] 0,001 0,285 0,081 -0,064 0,003* 0,012 -0,011 0,022** 0,007 35%

[0,1] 0,004 0,493** 0,083* -0,147** 0,002 0,026 -0,011 0,017** 0,003 47%

Appendix 3.1

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant T1CR ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-

2011 [-1,1] -0,005 -0,029 0,001 0,011* 0,006 0,012** 0,001 0,031 0,029 0,028* 0,026* 0,025* 65%

[0] -0,014 -0,022 0,002** 0,016 0,012 0,011** 0,003 0,056 0,055 0,055* 0,054* 0,054* 40%

[0,1] -0,009 -0,055 0,001* 0,023** 0,004 0,010** 0,001 -0,009 -0,002 0,004 0,011 0,017 39%

2008-

2011 [-1,1] -0,003 -0,032 0,000 0,012** 0,004 0,012** 0,001 0,025 0,024 0,023 0,022 0,020 64%

[0] -0,010 -0,029 0,001 0,019 0,007 0,011** 0,003 0,045 0,045 0,045 0,045 0,045 40%

[0,1] -0,005 -0,058 0,001 0,023** 0,000 0,008** 0,001 0,025 0,025 0,025 0,026 0,026 42%

Strong (9) 2006-

2011 [-1,1] -0,009 -0,083 0,002** 0,018 0,003 0,009 0,001 0,121** 0,119** 0,118** 0,116** 0,115** 44%

[0] -0,032 -0,009 0,005** 0,030 0,005 0,013 0,009* 0,116 0,117* 0,117* 0,118** 0,118** 34%

[0,1] -0,008 -0,131 0,003* 0,040** -0,009 0,007 0,003 0,169** 0,158** 0,148** 0,138** 0,127** 43%

2008-

2011 [-1,1] -0,006 -0,083 0,002 0,019 0,000 0,009** 0,001 0,117** 0,115** 0,114** 0,112** 0,110** 43%

[0] 0,004 -0,109 0,003 0,017 -0,010 0,018* 0,005 0,072 0,082 0,091 0,101* 0,111** 35%

[0,1] 0,003 -0,164** 0,002 0,038** -0,011 0,007 0,002 0,158** 0,151** 0,144** 0,138** 0,131** 43%

Appendix 3.1

Appendix 3.1.3 Regressions for AARs from events BEFORE July 2010

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/TA loans/TA R²

All (29) 2006-

2011 [-1,1] -0,012 0,084** -0,032 0,001 0,018** 0,013** 64%

[0] -0,027 0,121** -0,008 0,002 0,028* 0,026** 55%

[0,1] -0,011 0,010 -0,019 0,001 0,018** 0,011* 30%

2008-

2011 [-1,1] -0,007 0,079** -0,034 0,000 0,018** 0,010* 64%

[0] -0,021 0,109** -0,013 0,001 0,031** 0,020** 56%

[0,1] -0,005 0,012 -0,029 0,000 0,017** 0,007 34%

Strong (9) 2006-

2011 [-1,1] -0,025* 0,093** -0,021 0,002* 0,027** 0,016** 53%

[0] -0,070** 0,184** 0,016 0,006** 0,059** 0,041** 49%

[0,1] -0,040** 0,076** -0,030 0,003** 0,048** 0,019* 44%

2008-

2011 [-1,1] -0,021 0,088** -0,023 0,001 0,027** 0,128* 51%

[0] -0,043* 0,188** 0,016 0,003* 0,048** 0,018 55%

[0,1] -0,028 0,073** -0,042 0,002 0,038** 0,015 42%

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/TA loans/TA PIIGS R²

All (29) 2006-

2011 [-1,1] -0,017* 0,063** 0,003 0,001 0,023** 0,009 0,004** 68%

[0] -0,033* 0,099** 0,029 0,002 0,033** 0,022** 0,004 57%

[0,1] -0,014 0,000 -0,002 0,001 0,020** 0,009 0,002 31%

2008-

2011 [-1,1] -0,013 0,058** 0,002 0,000 0,023** 0,006 0,004** 68%

[0] -0,027 0,086** 0,027 0,001 0,036** 0,016 0,004 58%

[0,1] -0,007 0,003 -0,013 0,000 0,019 0,001** 0,002 36%

Strong (9) 2006-

2011 [-1,1] -0,033** 0,064** 0,028 0,002** 0,034** 0,010 0,005** 58%

[0] -0,091** 0,107** 0,143 0,006** 0,077** 0,026* 0,014** 58%

[0,1] -0,051** 0,038 0,034 0,003** 0,057** 0,011 0,007** 50%

2008-

2011 [-1,1] -0,029** 0,057** 0,028 0,001 0,034** 0,007 0,006** 57%

[0] -0,058** 0,133** 0,108 0,003** 0,061** 0,007 0,010** 60%

[0,1] -0,034* 0,050 -0,003 0,002 0,044** 0,011 0,004 45%

Appendix 3.1

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/ass loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,090 0,018 -0,022 0,000 0,018** 0,007 0,012** 0,002 72%

[0] -0,027 0,066 0,011 0,001 0,029* 0,020** 0,010 0,003 58%

[0,1] -0,014 0,003 -0,001 0,001 0,021** 0,009 0,001 0,002 31%

2008-

2011 [-1,1] -0,005 0,012 -0,022 0,000 0,019** 0,004 0,012** 0,003 72%

[0] -0,022 0,054 0,010 0,001 0,033** 0,015 0,010 0,003 59%

[0,1] -0,008 0,009 -0,010 0,000 0,020** 0,005 0,001 0,002 36%

Strong (9) 2006-

2011 [-1,1] -0,030** 0,042 0,017 0,002* 0,032** 0,009 0,009* 0,005* 59%

[0] -0,092** 0,109 0,145 0,006** 0,077** 0,026* 0,014 0,014** 58%

[0,1] -0,052** 0,044 0,037 0,003** 0,058** 0,012 0,006 0,007** 50%

2008-

2011 [-1,1] -0,026* 0,037 0,017 0,001 0,032** 0,006 0,009* 0,005* 57%

[0] -0,050** 0,090 0,085 0,003* 0,057** 0,006 0,018* 0,009* 61%

[0,1] -0,035* 0,054 -0,001 0,002 0,044** 0,011 0,004 0,004 45%

GEXPR for

Event group Time

frame

Event

window Constant

non-Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] -0,010 0,258** -0,007 0,000 0,000 0,012 0,007 0,017** 0,003** 79%

[0] -0,029* 0,378** 0,034 0,039 0,002 0,021 0,021** 0,016** 0,004 63%

[0,1] -0,015 0,153* -0,013 0,013 0,001 0,017* 0,009 0,005 0,003 37%

2008-

2011 [-1,1] -0,006 0,262** -0,013 0,001 0,000 0,012 0,004 0,017** 0,004** 80%

[0] -0,023 0,378** 0,021 0,039 0,001 0,024 0,015 0,017** 0,005 65%

[0,1] -0,009 0,156* -0,006 0,004 0,000 0,016* 0,005 0,004 0,003 41%

Strong (9) 2006-

2011 [-1,1] -0,031** 0,343** 0,011 0,044 0,002** 0,024** 0,009 0,016** 0,006** 67%

[0] -0,093** 0,319 0,088 0,163 0,006** 0,072** 0,026* 0,018 0,015** 59%

[0,1] -0,054** 0,400** 0,008 0,069 0,003** 0,048** 0,012 0,014* 0,009** 58%

2008-

2011 [-1,1] -0,027** 0,340** 0,006 0,044 0,001 0,023** 0,006 0,016** 0,006** 65%

[0] -0,051** 0,365* 0,062 0,110 0,003* 0,049** 0,006 0,023** 0,010** 63%

[0,1] -0,037** 0,414** 0,018 0,032 0,002 0,035** 0,011 0,011 0,006* 53%

Appendix 3.1

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant Tier 1 rat ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-

2011 [-1,1] -0,012 0,007 0,000 0,019** 0,007 0,011** 0,003* 0,045 0,036 0,026 0,016 0,007 73%

[0] -0,030 0,034 0,001 0,030* 0,020* 0,009 0,004 0,088 0,080 0,072 0,065 0,057 58%

[0,1] -0,013 -0,010 0,001 0,020** 0,009 0,002 0,002 -0,006 -0,003 0,000 0,003 0,006 32%

2008-

2011 [-1,1] -0,008 0,008 0,000 0,019** 0,003 0,011** 0,004* 0,041 0,031 0,021 0,011 0,001 73%

[0] -0,025 0,035 0,001 0,034** 0,014 0,009 0,004 0,079 0,071 0,062 0,053 0,045 60%

[0,1] -0,009 -0,006 0,000 0,020** 0,005 0,001** 0,002 0,012 0,011 0,010 0,008 0,007 36%

Strong (9) 2006-

2011 [-1,1] -0,033** 0,049 0,002* 0,033** 0,008 0,009* 0,006** 0,074 0,063 0,051 0,040 0,029 60%

[0] -0,098** 0,201 0,006** 0,079** 0,025 0,013 0,016** 0,165* 0,146 0,126 0,107 0,088 59%

[0,1] -0,059** 0,096 0,003** 0,060** 0,010 0,005 0,009** 0,102 0,081 0,061 0,041 0,021 52%

2008-

2011 [-1,1] -0,030** 0,051 0,001 0,033** 0,005 0,009 0,006** 0,070 0,058 0,047 0,035 0,024 58%

[0] -0,056** 0,137 0,003* 0,058** 0,004 0,017* 0,010** 0,140* 0,123 0,105 0,087 0,070 62%

[0,1] -0,039** 0,039 0,002 0,046** 0,010 0,003 0,006 0,093 0,079 0,066 0,053 0,039 46%

Appendix 3.1

Appendix 3.1.4 Regressions for AARs from events AFTER July 2010

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/TA loans/TA R²

All (29) 2006-2011 [-1,1] -0,009 0,107** -0,010 0,002** 0,001 0,011* 59%

[0] -0,007 0,118** -0,022 0,003** -0,005** 0,013 33%

[0,1] -0,012 0,131** -0,026 0,002* 0,012 0,008 53%

2008-2011 [-1,1] -0,009 0,104** -0,008 0,001* 0,002 0,011* 59%

[0] -0,004 0,114** -0,024 0,002* -0,004 0,011 31%

[0,1] -0,007 0,144** -0,045 0,002 0,011 0,006 60%

Strong (9) 2006-2011 [-1,1] 0,003 0,267** -0,034 0,003* -0,024 0,009 58%

[0] 0,021 0,240** -0,024 0,004 -0,047 0,003 36%

[0,1] 0,036 0,315** -0,001** 0,001 -0,013 -0,016 57%

2008-2011 [-1,1] 0,007 0,266** -0,039 0,002* -0,023 0,007 58%

[0] 0,042 0,259** -0,071 0,002 -0,061** -0,002 37%

[0,1] 0,039 0,306** -0,199** 0,001 -0,005 -0,021 59%

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/TA loans/TA PIIGS R²

All (29) 2006-2011 [-1,1] -0,011 0,094** 0,010 0,002** 0,003 0,008 0,002 61%

[0] -0,013 0,086** 0,025 0,003** 0,001 0,007 0,005* 38%

[0,1] -0,016 0,107** 0,009 0,002** 0,016 0,004 0,004 56%

2008-2011 [-1,1] -0,011 0,090** 0,013 0,001** 0,005 0,008 0,002 61%

[0] -0,011 0,080** 0,026 0,003** 0,002 0,004 0,006* 37%

[0,1] -0,011 0,124** -0,016 0,002* 0,148 0,002 0,003 62%

Strong (9) 2006-2011 [-1,1] 0,002 0,263** -0,028 0,003* -0,023 0,009 0,001 58%

[0] 0,011 0,185** 0,056 0,004* -0,038 -0,007 0,009 39%

[0,1] 0,035 0,307** -0,173* 0,001 -0,012 -0,017 0,001 57%

2008-2011 [-1,1] 0,006 0,259** -0,029 0,002 -0,022 0,005 0,001 58%

[0] 0,032 0,203** 0,010 0,002 -0,052* -0,012 0,009* 40%

[0,1] 0,037 0,295** -0,182** 0,001 -0,003 -0,023 0,002 59%

Appendix 3.1

Event group Time

frame

Event

window constant GEXPR T1CR ln(TA) dep/ass loans/TA Greece PIIS R²

All (29) 2006-2011 [-1,1] -0,003 0,036 -0,008 0,001* -0,002 0,007 0,012** 0,001 69%

[0] -0,008 0,050 0,014 0,003** -0,002 0,006 0,012* 0,004 39%

[0,1] -0,005 0,028 -0,016 0,001 0,008 0,001 0,018** 0,002 64%

2008-2011 [-1,1] -0,003 0,034 -0,005 0,001 -0,001 0,006 0,012** 0,001 69%

[0] -0,006 0,045 0,015 0,002 -0,001 0,003 0,012* 0,005 38%

[0,1] 0,000 0,047 -0,040 0,001 0,008 0,000 0,017** 0,001 69%

Strong (9) 2006-2011 [-1,1] 0,011 0,204** -0,046 0,002 -0,029 0,007 0,011 -0,001 60%

[0] 0,016 0,151 0,045 0,004 -0,041 -0,008 0,015 0,008 40%

[0,1] 0,044 0,243** -0,192** 0,001 -0,018 -0,020 0,013 0,000 59%

2008-2011 [-1,1] 0,015 0,199** -0,048 0,002 -0,027 0,003 0,012 0,000 60%

[0] 0,039 0,152 -0,006 0,002 -0,056* -0,014 0,018 0,008 41%

[0,1] 0,046 0,232** -0,201** 0,000 -0,009 -0,025 0,013 0,000 60%

GEXPR for

Event group Time

frame

Event

window Constant

non-

Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-2011 [-1,1] -0,003 0,203** 0,006 0,019 0,001* -0,010 0,007 0,018** 0,002 76%

[0] -0,008 0,324** -0,001 0,059 0,003** -0,015 0,006 0,022** 0,007** 48%

[0,1] -0,005 0,359** -0,033 0,038 0,001* -0,006 0,001 0,030** 0,005** 77%

2008-2011 [-1,1] -0,003 0,202** 0,002 0,023 0,001 -0,008 0,006 0,018** 0,002 75%

[0] -0,005 0,330** -0,004 0,060 0,002* -0,014 0,003 0,022** 0,007** 48%

[0,1] 0,000 0,342** -0,007 0,008 0,001 -0,006 -0,004 0,027** 0,004* 79%

Strong (9) 2006-2011 [-1,1] 0,011 0,572** 0,136** 0,013 0,002 -0,045** 0,007 0,024** 0,002 66%

[0] 0,016 0,528** 0,081 0,106 0,004 -0,058* -0,008 0,029* 0,011* 44%

[0,1] 0,044 0,773** 0,144** -0,107 0,001 -0,042* -0,020 0,032** 0,004 67%

2008-2011 [-1,1] 0,015 0,588** 0,127** 0,015 0,002 -0,045** 0,003 0,026** 0,003 67%

[0] 0,039 0,523** 0,084 0,054 0,002 -0,073* -0,014 0,032** 0,011* 45%

[0,1] 0,046* 0,763** 0,134** -0,116 0,000 -0,033 -0,026* 0,032** 0,005 69%

Appendix 3.1

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant Tier 1 rat ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-2011 [-1,1] -0,005 0,009 0,001* -0,003 0,007 0,012** 0,001 0,058 0,053 0,048 0,043 0,038 69%

[0] -0,012 0,048 0,003** -0,003 0,006 0,010 0,006 0,091** 0,081 0,072 0,062 0,052 40%

[0,1] -0,005 -0,011 0,001 0,008 0,001 0,018** 0,002 0,034* 0,032 0,031 0,030 0,029 64%

2008-2011 [-1,1] -0,005 0,013 0,001 -0,001 0,006 0,012** 0,001 0,056 0,050 0,045 0,040 0,035 69%

[0] -0,009 0,049 0,002* -0,002 0,003 0,010 0,006 0,087** 0,077 0,067 0,057 0,048 39%

[0,1] -0,004 -0,003 0,001 0,007 0,000 0,015** 0,003 0,093* 0,082 0,071 0,061 0,050 70%

Strong (9) 2006-2011 [-1,1] 0,000 0,053 0,003 -0,031 0,006 0,007 0,003 0,326** 0,297 0,269 0,240 0,211 62%

[0] 0,007 0,126 0,004 -0,043 -0,009 0,012 0,011 0,251** 0,227** 0,204 0,180 0,157 41%

[0,1] 0,030 -0,064 0,001 -0,020 -0,020 0,007 0,004 0,402** 0,364* 0,327 0,289 0,252 61%

2008-2011 [-1,1] 0,004 0,053 0,002 -0,029 0,003 0,007 0,003 0,324** 0,295 0,265 0,236 0,206 62%

[0] 0,003 0,050 0,002 -0,057* -0,014 0,016 0,010 0,221** 0,205** 0,189 0,172 0,156 42%

[0,1] 0,033 -0,079 0,001 -0,012 -0,026 0,008 0,005 0,383** 0,348* 0,312 0,276 0,241 63%

Appendix 3.2

Appendix 3.2 Regressions based on significant AARs only

Appendix 3.2.1 General regressions for (51) AARs from not-time split dummies (only sign AARs)

Event

group

Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] 0,012 -0,003 -0,050* 0,000 0,010 -0,004 0,012** 0,000 87%

[0] -0,011 0,020 -0,022 0,002 0,027 -0,001 0,011* 0,004 50%

[0,1] 0,015 0,004 -0,069** 0,000 0,011 -0,005 0,010** -0,002 82%

2008-

2011 [-1,1] 0,011 -0,006 -0,035 0,000 0,010 -0,003 0,011** -0,001 87%

[0] 0,018 0,004 -0,066* 0,001 0,015 -0,015* 0,012** 0,003 72%

[0,1] 0,002 0,006 -0,051 0,001 0,021** -0,001 0,009** -0,002 84%

Strong (9) 2006-

2011 [-1,1] 0,022 0,072** -0,085* 0,000 0,005 -0,005 0,009** -0,002 84%

[0] 0,004 0,050 0,005 0,003 0,023 -0,014 0,013* 0,009* 54%

[0,1] 0,009 0,057 0,018 0,003 0,008 -0,011 0,013* 0,007 60%

2008-

2011 [-1,1] 0,031* 0,073** -0,076 -0,001 -0,009 -0,003 0,009* -0,004 85%

[0] 0,014 0,051 0,021 0,002 0,007 -0,011 0,012* 0,005 59%

[0,1] 0,037 0,091* -0,184** -0,001 0,020 -0,016 0,006 -0,004 77%

Appendix 3.2

GEXPR for

Event

group

Time

frame

Event

window Constant

non-

Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] 0,010 0,123** -0,012 -0,021 0,000 0,002 -0,004 0,016** 0,001 91%

[0] -0,016 0,243** 0,002 0,034 0,003** 0,013 0,002 0,017** 0,006** 55%

[0,1] 0,012 0,171** -0,009 -0,029 0,000 0,000 -0,005 0,016** 0,000 88%

2008-

2011 [-1,1] 0,008 0,123** -0,015 -0,008 0,000 0,002 -0,003 0,015** 0,001 91%

[0] 0,013 0,181** -0,010 -0,023 0,001 0,004 -0,012* 0,017** 0,005** 78%

[0,1] 0,004 0,170** -0,008 -0,016 0,001 0,007 -0,003 0,015** 0,001 90%

Strong (9) 2006-

2011 [-1,1] 0,019 0,217** 0,064** -0,051 0,000 -0,006 -0,002 0,013** -0,001 86%

[0] 0,005 0,017 0,052 -0,003 0,003 0,024 -0,015 0,012 0,009* 55%

[0,1] 0,010 0,016 0,059 0,007 0,003 0,011 -0,012 0,011 0,006 60%

2008-

2011 [-1,1] 0,028* 0,253** 0,062** -0,037 -0,001 -0,021 -0,002 0,014** -0,001 89%

[0] 0,015 0,038 0,052 0,017 0,002 0,008 -0,011 0,012 0,005 59%

[0,1] 0,033 0,352** 0,073* -0,130* 0,000 0,004 -0,016 0,014* 0,000 82%

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant T1CR ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-

2011 [-1,1] 0,013 -0,072 0,000 0,010 -0,003 0,012** -0,001 -0,014 -0,010 -0,007 -0,003 0,000 87%

[0] -0,011 -0,026 0,002 0,028 -0,001 0,011* 0,004 0,018 0,018 0,019 0,020 0,021 50%

[0,1] 0,019* -0,116* 0,000 0,011 -0,003 0,011** -0,004 -0,020 -0,012 -0,005 0,003 0,011 83%

2008-

2011 [-1,1] 0,010 -0,033 0,000 0,010 -0,003 0,011** -0,001 -0,005 -0,005 -0,005 -0,006 -0,006 87%

[0] 0,023* -0,106* 0,001 0,015 -0,015* 0,012** 0,002 -0,023 -0,016 -0,008 -0,001 0,006 72%

[0,1] 0,005 -0,117* 0,001 0,022** 0,003 0,009** -0,004 -0,026 -0,016 -0,005 0,005 0,016 86%

Strong (9) 2006-

2011 [-1,1] 0,021 -0,073 0,000 0,004 -0,005 0,009** -0,002 0,078* 0,076** 0,074** 0,072** 0,071** 84%

[0] 0,003 0,014 0,003 0,023 -0,015 0,013* 0,009 0,055 0,053 0,051 0,050 0,048 54%

[0,1] 0,006 0,051 0,003 0,007 -0,012 0,012* 0,007 0,074 0,069 0,064 0,059 0,053 60%

2008-

2011 [-1,1] 0,028 -0,036 -0,001 -0,009 -0,005 0,009* -0,002 0,091* 0,085** 0,080** 0,074** 0,068** 86%

[0] 0,013 0,037 0,002 0,007 -0,113 0,012* 0,006 0,059 0,057 0,054 0,052 0,049 59%

[0,1] 0,033 -0,140 0,000 0,021 -0,018 0,005 -0,002 0,111* 0,104* 0,098** 0,091** 0,085* 77%

Appendix 3.2

Appendix 3.2.2 General regressions for (102) AARs from time split dummies (only sign AARs)

Event

group

Time

frame

Event

window Constant GEXPR T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] 0,008 0,003 -0,020 0,000 0,004 0,003 0,010** -0,001 72%

[0] 0,012 -0,004 -0,009 0,001 0,017 -0,011 0,010** 0,002 45%

[0,1] 0,012 -0,022 -0,007 0,000 0,017 -0,013 0,014** 0,000 69%

2008-

2011 [-1,1] 0,012 0,003 0,009 0,000 -0,001 0,000 0,009** -0,002 76%

[0] 0,001 0,030 -0,007 0,002* 0,007 0,001 0,008** 0,002 48%

[0,1] 0,019* 0,003 -0,046 0,000 0,005 -0,006 0,015** -0,003 84%

Strong

(9)

2006-

2011 [-1,1] 0,039 0,020 -0,033 -0,001 0,015 -0,028 0,008 -0,002 49%

[0] -0,015 0,086 0,033 0,005** 0,008 0,007 0,011 0,006 40%

[0,1] 0,044 0,148** -0,230** 0,001 0,026 -0,030 0,004 -0,002 58%

2008-

2011 [-1,1] 0,002 0,093 -0,119 0,003 0,030 -0,006 0,004 -0,002 51%

[0] 0,050* 0,083 -0,090 0,003 -0,042 -0,023 0,019** 0,002 54%

[0,1] 0,056 0,132 -0,259** -0,001 0,030 -0,042* 0,013 0,001 62%

Appendix 3.2

GEXPR for

Event

group

Time

frame

Event

window Constant

non-Greek

bank

Greek

banks T1CR ln(TA) dep/TA loans/TA Greece PIIS R²

All (29) 2006-

2011 [-1,1] 0,006 0,122** -0,002 -0,002 0,000 -0,004 0,003 0,013** 0,001 75%

[0] 0,009 0,110 -0,009 0,006 0,001 0,010 -0,009 0,013** 0,003 48%

[0,1] 0,009 0,151 -0,047 0,032 0,000 0,005 -0,014 0,022** 0,002 75%

2008-

2011 [-1,1] 0,012 0,092 -0,001 0,000 0,000 -0,006 -0,001 0,012** 0,000 78%

[0] 0,001 0,240** 0,018 0,021 0,002** -0,008 0,002 0,014** 0,004* 57%

[0,1] 0,018 0,130** -0,015 -0,021 0,000 -0,004 -0,007 0,021** 0,000 87%

Strong (9) 2006-

2011 [-1,1] 0,043* -0,108 0,017 -0,044 -0,002 0,025 -0,031* 0,005 -0,003 50%

[0] -0,001 0,466 0,076 0,048 0,005** -0,005 0,007 0,015 0,001 42%

[0,1] 0,044 0,122 0,151** -0,236** 0,000 0,028 -0,031 0,003 -0,003 58%

2008-

2011 [-1,1] 0,003 0,054 0,098* -0,128 0,003 0,032 -0,006 0,003 -0,003 52%

[0] 0,055** 0,507* 0,069 -0,072 0,002 -0,057** -0,024 0,024** 0,004 56%

[0,1] 0,055 0,225 0,122* -0,242** -0,001 0,022 -0,041* 0,017 0,002 62%

GEXPR for banks with T1CR =

Event

group

Time

frame

Event

window Constant T1CR ln(TA) dep/TA loans/TA Greece PIIS

mean -

2*stdev

mean -

stdev mean

mean +

stdev

mean +

2* stdev R²

All (29) 2006-

2011 [-1,1] 0,006 0,007 0,000 0,004 0,001 0,010** 0,000 0,018 0,013 0,007 0,002 -0,003 72%

[0] 0,010 0,045 0,000 0,017 -0,015 0,010** 0,004 0,026 0,015 0,004 -0,007 -0,017 47%

[0,1] 0,013 -0,041 0,000 0,018 -0,011 0,015** -0,001 -0,048 -0,041 -0,034 -0,027 -0,019 70%

2008-

2011 [-1,1] 0,010 0,046 -0,001 -0,001 -0,002 0,008** 0,000 0,022 0,015 0,008 0,002 -0,005 77%

[0] 0,000 0,009 0,002* 0,007 0,000 0,008** 0,002 0,042 0,038 0,035 0,031 0,028 49%

[0,1] 0,018 -0,030 0,000 0,005 -0,007 0,015** -0,002 0,013 0,010 0,007 0,004 0,001 84%

Strong (9) 2006-

2011 [-1,1] 0,040 -0,076 -0,001 0,017 -0,026 0,009 -0,003 0,000 0,008 0,016 0,023 0,031 50%

[0] -0,015 0,038 0,005** 0,008 0,007 0,011 0,006 0,090 0,089 0,088* 0,087* 0,086* 40%

[0,1] 0,044 -0,230** 0,001 0,026 -0,030 0,004 -0,002 0,149* 0,149* 0,149** 0,148** 0,148** 58%

2008-

2011 [-1,1] -0,002 -0,064 0,003 0,029 -0,008 0,003 0,000 0,131* 0,120* 0,110* 0,100* 0,089* 52%

[0] 0,057** -0,182 0,003 -0,043 -0,017 0,020** -0,002 0,021 0,041 0,060 0,080* 0,100* 55%

[0,1] 0,053 -0,234 -0,001 0,029 -0,042* 0,012 0,002 0,156 0,150* 0,145* 0,139* 0,133* 62%

Appendix 4

Appendix 4: Dutch summary/ Nederlandse samenvatting Het doel van deze thesis is het onderzoeken van de relatie tussen de waardering van een bank en haar

blootstelling ten opzichte van een land in crisis (Griekenland). Een samenvatting van de resultaten kan

gevonden worden in de tabel op het einde van deze samenvatting.

De Europese schuldencrisis is zonder twijfel een van meest besproken mediaonderwerpen van de

voorbije 2 jaar. Griekenland speelt hier duidelijk een hoofdrol als slachtoffer (of misschien eerder als

dader), maar ook andere perifere landen, verder naar verwezen als ‘de PIIS’ (Portugal, Ierland, Italië,

en Spanje), kwamen herhaaldelijk aan bod. Deze crisis kent vele oorzaken, maar ook vele gevolgen

voor verschillende instellingen en individuen. Een van die instellingen is het hoofdonderwerp van deze

scriptie, zijnde de (Europese) banken. Vele banken houden namelijk Griekse staatsobligaties aan.

Doordat Griekenland echter steeds dieper in de problemen komt, stijgt de gepercipieerde kans op een

default. Dit heeft dan weer een weerslag op de banken die in de problemen zouden kunnen komen

indien hun Griekse obligaties niet worden terugbetaald. Zij zouden hierdoor een verlies kunnen lijden

resulterend in een waardevermindering van de bank of zelfs een liquiditeitstekort. Als gevolg, zouden

de aandelen evenals credit default swaps (CDS’en) van de bank (abnormaal hoog) moeten reageren op

Griekse events. Men zou echter verwachten dat de grootte van deze abnormale reactie afhankelijk is

van de grootte van de portefeuille met Griekse aandelen. Het lijkt namelijk logisch dat de prijs van

aandelen en CDS’en van een bank die (relatief gezien) meer Griekse obligaties aanhoudt, sterker zal

reageren (op de dag van een Grieks event) dan die van de banken die weinig tot geen Griekse

obligaties aanhouden. Dit is de eerste hypothese (1a) die onderzocht en tevens ook geverifieerd werd.

Bovenstaand verband tussen de blootstelling (verder “exposure” genoemd) ten opzicht van

Griekenland en de reactie van de aandelen en CDS’en, kan enkel bestaan indien beleggers

geïnformeerd zijn over de grootte van de exposure van de banken. Vóór de publicatie van de resultaten

van de stresstesten in juli 2010 was deze informatie echter nog niet publiek beschikbaar (Kirkegaard,

2010, July 28). Daarom stelt een volgende hypothese (1b) dat de relatie tussen Griekse exposure en de

reactie van de aandelen en CDS’en na de publicatie in juli 2010 sterker zou zijn geworden. Het

onderzoek wees echter aan dat, net als in de paper van Bruner en Simms (1987), de investeerders (vzn

niet-Griekse banken) reeds voor juli 2010 bleken in staat te zijn om de Griekse exposure deels te

incorporeren bij het waarderen van de niet-Griekse banken. Na de publicatie, was de invloed van de

Griekse exposure echter 2.4 maal groter dan ervoor, resulterend in de aanvaarding van hypothese 1b.

Naast eventuele tijdsgebonden factoren, wordt dit verband ook door andere elementen beïnvloed. Een

eerste verwachting is dat banken met een kleine kapitaalsbasis sterker zullen reageren per procent

Griekse exposure (hypothese 2b). Dit kan gestaafd worden met de redenering dat banken met een

kleinere buffer, een hoger risico aanhouden en dus sterker zullen beïnvloed worden. Als gevolg, werd

Appendix 4

in de regressievergelijking een kapitaalsratio in interactie met de Griekse exposure ratio toegevoegd.

Hoewel de coëfficiënten niet significant van elkaar verschilden, was het wel duidelijk dat de weinig

gekapitaliseerde banken sterker gestraft werden per procent exposure. Dit leidde tot de aanvaarding

van hypothese 2b. Daarnaast wordt voor de kapitaalsratio ook gekeken of deze een effect heeft, los

van die exposuregrootte (hypothese 2a). Deze factor was echter niet significant verschillend van nul

resulterend in de verwerping van hypothese 2a.

Een andere factor die de link tussen exposure en de abnormale reactie mogelijks beïnvloedt, is het land

van herkomst van de bank. Eerst en vooral wordt verwacht dat de banken van de PIIS in het algemeen

een grotere abnormale reactie zullen tonen door besmettingsvrees (hypothese 6a). Dit geldt ook voor

Griekenland waar de banken, los van hun grote exposure, ook door een externe, economische impact

van de Griekse events zullen beïnvloed worden (hypothese 6b). Beide landdummy’s bleken

coëfficiënten significant verschillend van nul te geven. Terwijl de Griekse exposure slechts gemiddeld

een relatief klein deel van de gemiddelde abnormale returns (GARs) verklaart (19% voor de sterke

events), blijken deze GARs grotendeels beïnvloed te worden door de landdummy van Griekenland

(75% van de gemiddelde GAR van de Griekse banken) en van de PIIS (50%). Bijgevolg, worden ook

hypothese 6a en 6b op basis van deze steekproef aanvaard. Indien deze landelijke factor er zou

uitgefilterd worden, zouden de GARs van de PIIGS banken zeer vergelijkbaar worden met die van de

niet-PIIGS. Daarenboven is het mogelijk dat de Griekse banken, vermits ze gemiddeld veertig maal

meer exposure hebben en reeds die externe invloed ondervinden, minder sterk zullen reageren per

procent exposure (hypothese 6c). Het onderzoek wees uit dat ook deze hypothese aanvaard kon

worden vermits, voor de sterke events, de marktreactie per procent exposure voor niet-Griekse banken

tien maal groter was.

Bovenop bovenstaande factoren, kunnen er ook nog andere (voornamelijk risico-) factoren de grootte

van de abnormale reactie beïnvloeden. Daarom worden deze ook mee in de regressies opgenomen.

Ten eerste werd verwacht dat de grootte van de bank meer risico zou toevoegen, resulterend in

positieve correlatie met de GARs (hypothese 3). Deze hypothese werd eveneens bevestigd door de

analyse. Vervolgens werd ook een structuurvariabele voor de passiva toegevoegd (deposito’s/activa).

Het voorspelde negatieve effect hiervan op de GARs bleek echter niet door dit onderzoek bevestigd te

worden. Als laatste werd ook een ratio m.b.t. de structuur van de activa (leningen/activa) in

beschouwing genomen. Hiervoor werden vooraf tegenstrijdige argumenten gevonden. Daardoor werd

een hypothese gekozen zonder de richting van de invloed voorop te stellen (hypothese 5). Uit het

onderzoek bleek echter dat deze controlevariabele geen significant effect had op de GARs.

Om al deze stellingen te testen, werd gekozen voor de event-studie methodologie. In een eerste fase

werd voor iedere bank de gemiddelde abnormale reactie (GAR) op de dag van een event berekend

voor verscheidene scenario’s (zie verder). Dit werd gedaan aan de hand van een marktmodel met een

Appendix 4

additionele dummy die ‘1’ was op de dag van een positief event en ‘-1’ op de dag van een negatief

event. Daardoor werd verwacht dat de GAR positief zou zijn. Vervolgens werd deze GAR verder

geregresseerd op verschillende combinaties van variabelen zoals hierboven beschreven: de Griekse

exposure, de controlevariabelen, een dummy voor de banken uit de PII(G)S, één voor de Griekse

banken en de interactietermen van de Griekse exposure met de Griekse dummy of kapitaalsratio.

Om de afhankelijkheid van de resultaten t.o.v. bepaalde assumpties te checken, werden meerdere

scenario’s vooropgesteld. Ten eerste m.b.t. de gekozen events. Hiervoor werd zowel een groep van 9

sterke events als een groep met nog 20 extra kleinere events gekozen. Beide reeksen starten in

december 2009 en eindigen eind oktober 2011. Vermits bleek dat de Griekse exposure tijdens de

sterke events een groter effect vertoonde, zowel in absolute grootte als relatief gezien, t.o.v. de totale

abnormale reactie (19% voor de sterke t.o.v. 7%), werden de resultaten vooral hierop gebaseerd. Een

tweede assumptie had betrekking op het tijdskader. De gegevens werden zowel geregresseerd op een

marktmodel dat startte in 2006 als één dat startte in november 2008 (dit om te testen of de veranderde

correlatie door de crisis enig effect had). Deze robuustheidtest was succesvol aangezien beide soorten

scenario’s zeer vergelijkbare resultaten gaven. Een volgende assumptie betrof het ‘event window’.

Hierbij werd gekeken naar de GAR op de dag zelf [0], op 2 dagen [0,1], op 3 dagen [-1,1] en op 7

dagen [-3,3]. Enkel met deze laatste werd niet verder gewerkt aangezien deze resulteerde in te weinig

significante GARs. Een laatste controle, die niet opgenomen werd in een scenario, betreft het tweede

deel. Hierbij werden, naast de regressie van álle GARs, ook énkel de significante coëfficiënten (op het

10%-level) verder geregresseerd. De algemene resultaten bleken dezelfde te zijn.

Deze methodologie (en controles) werd toegepast zowel op de aandelen als op de CDS’en van

verschillende banken. Voor de selectie van de banken werd gekeken naar Europese banken, die

deelnamen aan de EU stresstesten (waardoor hun exposure bekend is) en waarvan de gegevens publiek

beschikbaar waren. Zo werden voor de aandelen 51 banken gevonden en voor de CDS’en 38. Jammer

genoeg resulteerden de CDS returns in te weinig significante GARs waardoor de tweede stap van de

regressies niet uitgevoerd kan worden. Gelukkige gaf de eerste stap voor de aandeel returns wel

voldoende significante GARs.

Gemiddeld, werd een abnormale reactie van 1% gemeten (onafhankelijk van de onderliggende

assumpties zoals event window of event keuze). De grootte van de GARs was echter zeer afhankelijk

van de herkomst van de banken. Zo was de abnormale reactie van de banken van de PIIGS in het

algemeen dubbel zo groot als die van de niet-PIIGS banken. In het tweede deel van de analyse werd

onderzocht of dit verschil te wijten was aan een verschil in Griekse exposure, een hoger risicoprofiel

of door factoren te wijten aan de herkomst zelf (bv. besmetting). Daarnaast was de grootte van de

GARs ook afhankelijk van het aantal gekozen events. Logischerwijze waren de GARs gebaseerd op de

9 sterke events (1.3%) groter dan die gebaseerd op de dummy met de 29 events (0.8%).

Appendix 4

Ondanks de robuustheidtesten, zal dit onderzoek nog wat beperkingen hebben die in rekening moeten

gebracht worden bij het interpreteren en generaliseren van de resultaten. Ik identificeerde vijf

beperkingen. (1) Ten eerste zijn enkele beursgenoteerde banken uit de EU, die tevens deelnamen aan

de Europese stresstesten, opgenomen in de steekpoef. Dit kan een vertekend beeld geven hoewel ik

ervan overtuigd ben dat ook private en zelfs niet-Europese banken die Griekse obligaties aanhouden

de gevolgen van de crisis voelen. (2) Ten tweede is het selectieproces van de events vrij subjectief.

Zoals beschreven, zijn de resultaten licht afhankelijk van de gekozen eventgroep. Daarbovenop is het

mogelijk dat andere eventgroepen nog andere resultaten zouden kunnen geven (mogelijks zelf

significante voor CDS’en). (3) Een derde restrictie is dat slechts een beperkt aantal banken is

opgenomen. Hierdoor waren er weinig significant GAR’s als input voor de tweede stap van de

methodologie. (4) Vermits dit onderzoek enkel focust op Griekse events en Griekse exposure en er

slechts weinig aanvullende literatuur beschikbaar is, moet men voorzichtig zijn bij het veralgemenen

van de resultaten naar andere landen als schuldenaars. Ik ben ervan overtuigd dat soortgelijke

resultaten kunnen gevonden worden bij onderzoek op andere landen in crisis. Hoe ver het begrip

‘crisis’ in deze context echter reikt, moet onderzocht worden. (5) Een laatste beperking beperkte de

diepgang van dit onderzoek. Hoewel een versterking in de invloed van de Griekse exposure werd

vastgesteld, is dit onderzoek niet in staat te verklaren wat deze veroorzaakt is. Een eerste verklaring

zou zijn dat het door meer geïnformeerde investeerders komt (door de publicatie van de stresstesten).

Een tweede mogelijke verklaring is dat naarmate een bank een hogere kans op default heeft, meer

aandacht wordt besteed aan de blootstelling van de banken hieraan.

Al bovenstaande beperkingen kunnen deels gecounterd worden door verder onderzoek. Naast deze,

zijn er echter nog enkele andere gerelateerde onderwerpen voor verder onderzoek. Een eerste betreft

het incorporeren van events na oktober 2011. Een tweede betreft het toepassen van een soortgelijk

onderzoek met bedrijven in plaats van landen als schuldenaar.

Recentelijk is slechts weinig vergelijkbaar onderzoek uitgevoerd naar de relatie tussen de exposure

ten opzichte van de overheid en de reactie van bankaandelen. Een samenvatting van de bestaande

literatuur, die voornamelijk gebaseerd is op de default van Mexico in de LDC crisis van de jaren ‘80,

zou stellen dat deze relatie geverifieerd kon worden op de lange termijn, terwijl deze op korte termijn

niet significant aanwezig was. Mijn onderzoek spreekt deze bevinding echter tegen aangezien hier wel

degelijk sprake is van een korte termijn effect. Men zou kunnen argumenteren dat dit te wijten is aan

een andere onderzoeksopstelling vermits enkel korte termijneffecten konden gemeten worden en dit

voor andere events dan de default zelf. Men kan echter verwachten dat een default event nog meer

impact zou hebben dan de kleinere events die nu geïncorporeerd zijn. Bijgevolg, zou dit effect des te

meer aanwezig moeten zijn voor het default event waardoor dit argument niet opgaat. Het verschil met

de huidige literatuur kan echter verklaard worden door een groeiende efficiëntie en transparantie van

Appendix 4

de markt ten opzichte van 30 jaar geleden. Verder onderzoek m.b.t. het korte termijn effect zou

kunnen uitklaren of deze verandering blijvend is.

Om te eindigen zou ik graag ingaan op de relevantie van dit onderzoek. Dit onderzoek zou banken en

investeerders kunnen helpen om de bewegingen van hun aandelen beter de begrijpen (of zelfs te

voorspellen). Daarbovenop zou het onderzoek ze in staat kunnen stellen deze bewegingen ten opzichte

van andere banken beter te verstaan door het risicoprofiel (hoofdzakelijk de grootte van de bank dan),

de herkomst evenals de obligatieportefeuille in rekening te brengen. Daarnaast, zou de kwantificatie

van de invloed van de Griekse exposure de bank kunnen helpen om te begrijpen in hoeverre hun

portfolio van Griekse staatsobligaties hun risico omhoog drijft (of hun waarde naar beneden).

Hypothese Factoren Verwachte invloed op de grootte van

de GARs Conclusie

1a Griekse exposure ratio

(GEXPR) +

1b GEXPR (voor vs na) na > voor

2a Tier 1 Capital ratio -

2b GEXPR afhankelijk van

T1CR

coëfficiënt GEXPR (kleine T1CR) >

coëfficiënt GEXPR (grote T1CR)

3 ln(Totale Activa) +

4

5

Deposito’s/TA

Leningen/TA

-

+ of -

6a PII(G)S (landdummy) +

6b Griekenland (landdummy) +

6c GEXPR afhankelijk van

Griekenland

coëfficiënt GEXPR (niet-Griekse

banken) > coëfficiënt GEXPR (Griekse

banken)

Samenvatting van hypothesen en resultaten (bron: auteurs eigen werk)