homogeneous banking and systemic risk
TRANSCRIPT
Homogeneous Banking and
Systemic Risk
An accounting and market-based exploration including regulatory
policy effects
J.G.M. Koch BSc.
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Homogeneous Banking and Systemic Risk
An accounting and market-based exploration including regulatory policy effects
Master Thesis in Finance
Name: J.G.M. Koch BSc.
ANR: 416987
Supervisor: dr. O.G. De Jonghe
Date: September 2013
Tilburg School of Economics and Management
Finance Department
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Acknowledgements
This thesis is written with the aim of completing the Master Finance at Tilburg University, and therefore
terminating my student career. During the six months it took to write this report, several people provided
the necessary support and I would like to take the opportunity to thank them.
First of all, many thanks go to dr. O.G. De Jonghe for his quick responses, enthusiasm when guiding
me through STATA and valuable feedback and comments regarding the many tables I sent. Besides that, I
would like to thank him for setting up this unique research group, that gave each student participating the
opportunity to write a thesis based on a new and unique database and finally, for helping all of us
preparing the database in order to make it easy to use.
Next, I would like to thank all students participating in the research group for investigating the many
annual reports of international banks for two months and the good cooperation when things were unclear.
Nobody of us could have collected the data for this database on his or her own.
Finally, I would like to thank all my family and friends for their endless support and distraction
when necessary. Special thanks go to Bart, Remco, Sandra, Floris, Inge, Lendert, Amber and Redmar for
providing valuable comments and suggestions on the drafts they were willing to read, in-depth
discussions when I stumbled into problems or late-night coffees including physical exercises as an
attempt to stay fit.
Thank you all, I could not have finished this thesis without you.
Joni Koch BSc.
September 2013
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Abstract
This thesis investigates the effect of homogeneity in the banking sector on banks’ systemic risk levels.
Homogeneity is measured by studying the accounting-based and market-based ‘distance’ (degree of
dissimilarity) between banks. The accounting-based ‘distances’ are gauged while using manually
collected data on sectoral loan portfolio exposures of banks, which are taken from their annual reports for
the years 2007-2011. Market-based ‘distances’ are determined using banks’ sectoral betas that indicate
the exposure of their stock to these sectors. Using these proxies for homogeneity, this thesis finds that
more homogeneity in the banking sector reduces systemic risk. The relation is found to be of a mountain
parabolic shape for both accounting and market-based ´distances´, but has most observations on the
mountains´ right hand sides. Besides that, the level of systemic risk found at a bank is highly influenced
by its level a year earlier. The relation is further tested by differentiating countries based on regulatory
policy. Although there are some cross-country differences, on average, the stabilizing effect of
homogeneity remains the same. These results go against existing theoretical models and indicate that
policy makers should focus on increasing banking sector homogeneity.
Keywords: banking sector homogeneity, systemic risk, regulation, herding
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Table of contents
TABLE OF CONTENTS – TABLES ................................................................................................... - 7 -
TABLE OF CONTENTS – FIGURES ................................................................................................. - 8 -
1. INTRODUCTION .............................................................................................................................. - 9 -
2. RELATED LITERATURE ............................................................................................................. - 11 -
2.1. GOAL OF THIS THESIS ............................................................................................................... - 11 -
2.2. SYSTEMIC RISK AND ITS IMPORTANCE FOR THE FINANCIAL SYSTEM ....................................... - 12 -
2.3. DRIVERS OF SYSTEMIC RISK IN FINANCIAL SYSTEM ................................................................ - 12 -
2.4. DIVERSIFICATION ..................................................................................................................... - 13 -
2.4.1. Diversification via a bank’s loan portfolio ..................................................................... - 13 -
2.5. HERDING .................................................................................................................................. - 14 -
2.6. HOMOGENEOUS BANKING SYSTEM .......................................................................................... - 14 -
2.6.1. Homogeneous banking system and systemic risk ........................................................... - 14 -
2.6.2. Homogeneity through banks’ loan portfolio ................................................................... - 15 -
2.7. SETUP OF THE THESIS ............................................................................................................... - 15 -
3. DATA AND METHODOLOGY..................................................................................................... - 15 -
3.1. DATA ........................................................................................................................................ - 16 -
3.1.1. Database collection ........................................................................................................ - 16 -
3.1.2. Regional division ............................................................................................................ - 16 -
3.2. EMPIRICAL METHODOLOGY ..................................................................................................... - 17 -
3.2.1. The accounting distance measures ................................................................................. - 17 -
3.2.2. The market-based distance measure ............................................................................... - 18 -
3.2.3. Systemic risk ................................................................................................................... - 18 -
3.2.4. Control variables ............................................................................................................ - 19 -
3.3. SUMMARY STATISTICS AND CORRELATIONS ............................................................................ - 19 -
3.3.1. MES ................................................................................................................................ - 19 -
3.3.2. Distance measures .......................................................................................................... - 21 -
3.3.3. CONTROL VARIABLES ........................................................................................................... - 23 -
3.4. CONSTRUCTION OF THE REGRESSIONS ..................................................................................... - 23 -
4. RESULTS ......................................................................................................................................... - 23 -
4.1 BASELINE REGRESSION: DISTANCE AND SYSTEMIC RISK .......................................................... - 24 -
4.1.1. Preferred model using accounting-based data ............................................................... - 24 -
4.1.2. Robustness checks ........................................................................................................... - 24 -
4.2. EXTENSION OF THE BASELINE REGRESSION ............................................................................. - 26 -
4.2.1. Lagging the dependent variable ..................................................................................... - 26 -
4.2.2. Second order distance measure ...................................................................................... - 27 -
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4.3. MARKET-BASED DISTANCE AND SYSTEMIC RISK ..................................................................... - 28 -
4.3.1. Preferred model .............................................................................................................. - 29 -
4.3.2. Lagging the dependent variable ..................................................................................... - 29 -
4.3.3. Second order distance measure ...................................................................................... - 29 -
4.3.4. Interaction with market beta ........................................................................................... - 30 -
5. EFFECTS OF REGULATION ON DISTANCE VERSUS SYSTEMIC RISK ......................... - 32 -
5.1. EXPLANATION OF THE REGULATORY MEASURES AND PREDICTION OF THEIR EFFECTS ........... - 33 -
5.1.1. Overall Restriction on Banking Activities ...................................................................... - 33 -
5.1.2. Fraction of Entry Applicants Denied .............................................................................. - 33 -
5.1.3. Capital Regulatory Index ................................................................................................ - 33 -
5.1.4. Official Supervisory Power............................................................................................. - 34 -
5.1.5. External Governance ...................................................................................................... - 34 -
5.1.6. Summary ......................................................................................................................... - 34 -
5.2. ACCOUNTING-BASED PAIR WISE DISTANCE AND REGULATION ................................................ - 35 -
5.3 MARKET-BASED DISTANCE AND REGULATION ......................................................................... - 38 -
6. CONCLUSIONS .............................................................................................................................. - 41 -
REFERENCES ..................................................................................................................................... - 43 -
APPENDICES ...................................................................................................................................... - 47 -
APPENDIX 1. TABLES ...................................................................................................................... - 47 -
APPENDIX 2. FIGURES ..................................................................................................................... - 53 -
APPENDIX 3. FORMATION OF THE DATABASE ................................................................................. - 55 -
A3.1 Elaborated description of the hand collected database ................................................... - 55 -
A3.2 Example data gathering ................................................................................................... - 55 -
A3.3. Assumptions..................................................................................................................... - 57 -
APPENDIX 4 BANKS IN THE SAMPLE AND THEIR REGIONAL/CONTINENTAL CLASSIFICATION ........ - 62 -
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Table of contents – Tables
TABLE I ............................................................................................................................................... - 20 -
SUMMARY STATISTICS
TABLE II .............................................................................................................................................. - 22 -
PAIR WISE CORRELATIONS
TABLE III ............................................................................................................................................ - 25 -
ACCOUNTING-BASED DISTANCE AND SYSTEMIC RISK
TABLE IV ............................................................................................................................................ - 28 -
EXTENSION OF THE REGRESSION MODEL
TABLE V .............................................................................................................................................. - 30 -
MARKET-BASED DISTANCE AND SYSTEMIC RISK
TABLE VI ............................................................................................................................................ - 36 -
THE EFFECT OF BANKING REGULATION ON THE RELATION BETWEEN ACCOUNTING-BASED
DISTANCE AND SYSTEMIC RISK
TABLE VII ........................................................................................................................................... - 39 -
THE EFFECT OF BANKING REGULATION ON THE RELATION BETWEEN MARKET-BASED DISTANCE
AND SYSTEMIC RISK
TABLE A1: ........................................................................................................................................... - 47 -
VARIATION IN DISTANCE MEASURES OVER TIME
TABLE A2: ........................................................................................................................................... - 49 -
TESTING CLUSTERED ERRORS
TABLE A3: ........................................................................................................................................... - 50 -
ROBUSTNESS CHECK: ADDED VOLATILITY MEASURES
TABLE A4 ............................................................................................................................................ - 51 -
TRIMMED, BUT NO FORWARDED MES
TABLE A5 ............................................................................................................................................ - 52 -
BANKING REGULATION, MARKET-BASED DISTANCE AND SYSTEMIC RISK – REDUCED SAMPLE
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Table of contents – Figures
FIGURE 1.............................................................................................................................................................. - 27 -
STABILITY OF LOCAL MES
FIGURE 2 ............................................................................................................................................................ - 31 -
EFFECT OF MARKET BETA ON THE RELATION MBD_-FT VERSUS SYSTEMIC RISK
FIGURE 3.............................................................................................................................................................. - 37 -
EFFECT OF OVERALL RESTRICTION ON BANKING ACTIVITIES: REGIONAL PWD VERSUS MES
FIGURE 4.............................................................................................................................................................. - 37 -
EFFECT OF FRACTION OF ENTRY APPLICANTS DENIED : REGIONAL PWD VERSUS MES
FIGURE 5.............................................................................................................................................................. - 37 -
EFFECT OF EXTERNAL GOVERNANCE: REGIONAL PWD VERSUS SYSTEMIC RISK
FIGURE 6.............................................................................................................................................................. - 37 -
EFFECT OF OVERALL RESTRICTION ON BANKING ACTIVITIES: MBD VERSUS MES
FIGURE 7.............................................................................................................................................................. - 40 -
EFFECT OF CAPITAL REGULATORY INDEX: MBD VERSUS SYSTEMIC RISK
FIGURE A1 ........................................................................................................................................................... - 53 -
EFFECT OF CAPITAL REGULATORY INDEX ON THE RELATION REGIONAL PWD VERSUS SYSTEMIC RISK
FIGURE A2 ........................................................................................................................................................... - 54 -
EFFECT OF OFFICIAL SUPERVISORY POWER: REGIONAL PWD VERSUS SYSTEMIC RISK
FIGURE A3 ........................................................................................................................................................... - 54 -
EFFECT OF FRACTION OF ENTRY APPLICANTS DENIED: MBD VERSUS SYSTEMIC RISK
FIGURE A4 ........................................................................................................................................................... - 54 -
EFFECT OF OFFICIAL SUPERVISORY POWER: MBD VERSUS SYSTEMIC RISK
FIGURE A5 ........................................................................................................................................................... - 54 -
EFFECT OF EXTERNAL GOVERNANCE ON THE RELATION MBD VERSUS SYSTEMIC RISK
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1. Introduction
“What we know about the global financial crisis is that we don’t know very much” – (Paul A. Samuelson,
2009)
The credit crisis of 2007-2009 has shed new light on contagion, systemic risk1 and the repercussions for
the real economy not just nationally, but also internationally. Up until today academics are still trying to
unravel the many mysteries concerning systemic risk; through which channels does it advance? How does
it affect multiple institutions? Can it be contained in order to avoid a full-blown global financial crisis? In
other words; it is important to economists, bank managers, but above all policy makers to understand
what constitutes systemic risk and what regulations can be implemented in order to reduce systemic risk.
After all, everybody wants a prosperous future with an expanding economy, full employment and a
growing savings account.
This thesis will try to shed light on a small part of the relation between the financial sector (in
particular the banking sector) and systemic risk by using an entirely new and manually collected database
with information on the sectoral allocation of banks’ loan portfolio. The goal of this thesis is to improve
the understanding of how banks contribute to systemic risk and how this can be translated into regulatory
policy. Specifically, the effect of the level of homogeneity in the banking sector on systemic risk will be
tested.
Literature concerning this topic emerged only recently when theoretical academics started to link a
homogeneous banking sector to systemic risk. They argued that the growing ‘closer’ of financial
institutions over the last few decades might have been one of the reasons why the global financial crisis of
2007-2009 spread quickly and over different continents. A banking sector can become more
homogeneous through two different channels. Firstly, ´conglomerate´ banks now also provide insurance,
investment, trading and many other activities and are therefore exposed to a larger variety of shocks.
Moreover, these exposures are more interconnected with not only other banks, but also e.g. insurance
firms. Secondly, banks herd each other, meaning they copy each other´s behavior2. A homogeneous
banking sector occurs when all banks resemble each other. Authors agree that increasing resemblance
gives rise to risk-sharing between banks, which decreases the probability of an individual failure. At the
same time, however, the possibility that banks fail jointly is highly augmented, imposing a large negative
externality to the real economy (Acharya and Yorulmazer, 2008, Acharya, 2009, Elsinger et al., 2006).
The effect homogeneity has on systemic risk runs through different channels. On the one hand, risk
sharing increases aggregate risk in the economy, which makes joint failures more likely (Wagner, 2008,
Wagner, 2010). On the other hand, when banks hold (a part of) the same assets, problems at one bank and
an accompanying fire-sale of its assets can decrease the asset values of other banks as well, increasing the
probability of an entire banking crisis (Barth and Schnabel, 2013).
Only few authors empirically tested this relationship as data is on banks’ exposures is not readily
available. Cai, Saunders and Steffen (2012) and Caballero (2012) investigated homogeneity in banks’
1 The risk of an event that weakens the entire financial sector (further explained in section 2.2.) 2 Reasons for bank herding are explained in section 2.5
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syndicated loan portfolio and confirmed the theoretical models. Caballero (2012), did find a stabilizing
effect when a country has easy access to international funding even though their banking sector was
homogeneous.
This thesis adds to the existing literature by investigating whether homogeneity in the banking sector
indeed increases systemic risk. As empirical investigations until today always relied on the syndicated
loan market it is unsure whether the conclusions found, are applicable to all lending activities of banks.
Besides that, banks also have exposures via stock markets, which can be highly interconnected and which
are not entirely incorporated in the information of a syndicated loan portfolio. Enhanced knowledge is
therefore desired regarding the effect of banking sector homogeneity on systemic risk when other
exposures than syndicated loan exposures are considered. As homogeneity is measured based on
(accounting) loan portfolio exposures and market-based exposures, this thesis will try to provide this
knowledge. In addition, I will test whether the relationship differs among countries depending on their
regulatory environment. Each bank is given a value for their level of homogeneity by calculating the
Euclidean distances3 (as in Cai et al. (2012)) from other banks in a country, region and continent, based
on their accounting sectoral loan portfolio weights and sectoral market betas, thus generating:
‘accounting-based distances’ and ‘market-based distances’. Besides that, the Marginal Expected Shortfall
(MES)4 is employed as proxy for systemic risk (Acharya et al., 2010).
A manually collected database including data on the sectoral division of the loan portfolios of 466
banks in 64 countries is used to conduct the investigation. To the best of my knowledge no such database
existed before as data is not readily available. Therefore, one can say that the research presented here has
never been conducted before. The fact that the database includes data on banks globally makes it possible
to draw conclusions and propose policy measures not just applicable for the banking sector in one country,
but also internationally.
The results when regressing accounting-based distances show a positive and highly significant
coefficient which is also economically relevant. This indicates that the less distant banks are (more
homogeneity), the lower systemic risk. This finding thus contradicts all theoretical papers on
homogeneity and systemic risk (e.g. Wagner (2008, 2010), Acharya (2009) and Elsinger et al. (2006)).
Only Caballero (2012) showed a possible stabilizing effect when a bank is important to the global
network, but the banks in this database cannot all be important to the global network. It is therefore
unlikely this effect would overrule the effect of homogeneity. Furthermore, the positive significant results
are robust to several additions and changes in the model.
The effect of accounting-based distance on MES was tested further in order to get a better
understanding how the relation runs. Firstly, an autoregressive model is tested as MES depends to a large
extent on its value a year earlier. When including a lagged value of MES as independent variable, the
explanatory power of the model increases by 46%, but the accounting distance measure remains positive
and significant. Secondly, when testing for a nonlinear relation, a mountain shaped parabola is found. The
3 On a J-dimension space, a Euclidean distance measure gives a value of how similar one bank is to another (further explained in
section 3.2.1.) 4 The MES measures systemic risk by looking at the change in a bank’s equity during the 5% worst trading days of a national
index (further explained in 3.2.3.)
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major part of the observations, however, lies on the left-hand side of the parabola again indicating that the
more homogeneous a bank is, the larger its systemic risk.
The results when including market-based distances again show positive and highly significant
regression coefficients. Thus, also when considering market-based exposures, more homogeneity is found
to reduce systemic risk, contradicting existing theoretical literature. This result is again robust to several
additions and changes in the model. Besides that, no significant herding with the market is found to
influence the relationship.
Additionally, when differentiating countries based on regulatory policy, some cross-country
variation in the relation tested is found although on average the relation remains positive and significant.
Systemic risk will increase more due to increased accounting-based distance in countries with stricter
activity restrictions, more open banking markets and lower levels of external governance. While an
increase in market-based distance increases systemic risk more for countries with liberty concerning
activities a bank can undertake and that have obligate banks to hold smaller amounts of regulatory capital.
In conclusion, homogeneity decreases systemic risk significantly even though it is tested based on
accounting and market-based measures, after different robustness checks and by taking cross-country
differences in regulatory stringency into account. This is an important finding for policymakers and
academics as it contradicts all existing theories concerning the relation between homogeneity and
systemic risk. Based on the empirical findings in this thesis, my advice to policymakers would be to
impose regulations with a focus on increasing homogeneity both in lending and in market-based
exposures, as this is found to decrease systemic risk. The economic rationale for this conclusion is not
investigated in this thesis and remains open for future research. Crucially, future research should focus on
among others; investigating the channels through which homogeneity influences systemic risk, extending
the database, implementing different measures for homogeneity and systemic risk and tackling the
survivor and selection bias that the sample in this database suffers from.
The rest of this thesis is organized as follows. Section 2 reviews the existing literature concerning
homogeneity and systemic risk. Section 3 will describe the dataset and the methodology used in this paper.
Section 4 outlines the empirical results of both the accounting and market-based distance measures and
their effect on systemic risk. Besides that, the extensions to the model will be discussed. Section 5
introduces the regulatory measures and will provide the results of their effect on the relationship between
distance and systemic risk. Section 6 will conclude and provide possibilities for further research.
2. Related literature
In this section I will shortly discuss the goal of this thesis, elaborate on the research conducted on this
topic until today and end with a short note on the setup of this thesis
2.1. Goal of this thesis
Since the last financial crisis, more emphasis has been put on systemic risk in the banking industry,
especially after the fall of Lehman Brothers in 2008 when the dangers of a systemic crisis became
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apparent in many western economies. In this thesis, a study on systemic risk in the global banking
industry will be conducted. Systemic risk will be related to the Euclidean distance between banks’ loan
portfolio and market exposures and therefore investigate whether the degree of homogeneity of the
banking sector (their interconnectedness) is of importance for systemic risk.
2.2. Systemic risk and its importance for the financial system
According to De Bandt and Hartmann (2000), a systemic crisis occurs when a systemic event weakens a
substantial amount of financial institutions or markets, which consequently impairs the well-functioning
of the financial system in general. Systemic risk can thus be defined as the risk of a systemic event that
triggers a systemic crisis.
There are three reasons why systemic risk is especially important in the financial system. Firstly,
banks transform liquid liabilities into illiquid assets and are therefore able to provide long-term loans to
firms even though they use short-term funding. The provision of this ‘transformation’-service makes them
vulnerable to systemic events because these can result in a drop in confidence for the depositors and an
accompanying bank run (Diamond and Dybvig, 1983). Secondly, due to the modern electronic age banks
become more and more intertwined as to their settlement systems. In these settlement systems huge sums
are exchanged over the course of one day, which are settled at the end. If one of the participants is unable
to settle, it will have an immediate effect on other participants and possibly even start a chain reaction.
The same holds for very short-term loans on the interbank market (Angelini et al., 1996). Lastly, systemic
events are especially risky for the financial system due to reliance on trust in financial contracts. When
credibility is questioned or uncertainty increases, market expectations may change quickly as well
resulting in different (dis)investment decisions (Stiglitz et al., 1993).
It is thus of utmost importance to investigate what drives systemic risk in the financial system in
order to shed light on some of the key weaknesses of the financial system.
2.3. Drivers of systemic risk in financial system
Due to the unique activities that banks provide, they have a larger exposure and contribution to systemic
risk. Many researchers have investigated this issue and indicated aspects that enforce this relation. Firstly,
the banking sector is highly regulated, which is intended to decrease systemic risk, but sometimes has
opposite effect. A government’s explicit deposit insurance, for example, is found to increase systemic risk
(Demirgüç-Kunt and Detragiache, 2002). The same holds for implicit insurances like ‘too-many-to-fail’
(Acharya and Yorulmazer, 2007) or ‘too-big-to-fail’ (Stern and Feldman, 2004)). Secondly,
macroeconomic aspects like a weak macroeconomic environment, weak law enforcement, vulnerability to
a balance-of-payment crisis and high real interest rates make the banking sector more fragile (Demirgüc-
Kunt and Detragiache, 1998)). Lastly, also aspects specific to banks make them vulnerable, e.g. asset
growth, share of non-interest income (Vallascas and Keasey, 2012) and absolute size (Demsetz and
Strahan, 1997, Anderson and Fraser, 2000, Barrel et al., 2011). Other authors argue that relative or
systemic size is even more important (Barth and Schnabel, 2013, Demirgüç-Kunt and Huizinga, 2010).
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On the other hand, a restricted leverage ratio coupled with liquidity requirements have been shown to
increase a bank’s flexibility to systemic shocks (Vallascas and Keasey, 2012).
Also the way in which banks are interconnected in order to manage liquidity needs, increases
systemic risk directly (Allen and Gale, 2000, Allen et al., 2010, Tsmomocos et al., 2007, Wagner, 2007,
Kahn and Santos, 2010, Freixas et al., 2000, Brunnermeier et al., 2012), but also indirectly as the risk of
bank runs increases after failure of one bank (Chen, 1999, Aghion et al., 1999, Rochet and Tirole, 1996,
De Bandt, 1995). The level of interconnectedness is amplified when the banking sector is more
homogeneous. The effect of homogeneity on systemic risk is the main topic of this thesis and will
therefore be elaborated upon more extensively in section 2.6. First I will discuss two causes of a
homogeneous banking sector and their effect on systemic risk in isolation: diversification and herding.
2.4. Diversification
Portfolio Theory as proposed by Markowitz (1952) states that combining assets in one portfolio improves
the risk-return ratio since risks are more spread. Many researchers have investigated whether this holds by
looking at functional diversification (diversification of a bank’s activities) and found contradicting
evidence. On the one hand, functional diversification can result in economies of scale (Saunders and
Walter, 1994), economies of scope (Kim, 1986) and revenue generation (Saunders, 1994). However, more
recently evidence has emerged that points at an increase in risk without a sufficient increase in returns
(Demirgüc-Kunt and Huizinga, 2010, De Nicolo et al., 2004, DeYoung and Roland, 2001, Stiroh, 2004,
Stiroh, 2006).
Empirically, the link between (functional) diversification and systemic risk has not been investigated
extensively. Baele, De Jonghe and Vander Vennet (2007), Brunnermeier, Dong and Palia (2012), De
Jonghe (2010) and Moore and Zhou (2013) show that systemic risk increases when banks have more
alternative (noninterest) revenue streams, while Moshirian, Saghal and Zhang (2011) argue that this effect
depends on the level of concentration in the financial sector.
2.4.1. Diversification via a bank’s loan portfolio
Banks are in a unique position in that they are able to diversify their exposures while not actually
diversifying their activities; namely via their loan portfolio. There are different views of what
organization of loan portfolio is optimal. On the one hand, lending to only few sectors can be risky as a
downturn of one of these sectors might trigger default of a large proportion of loans outstanding and
consequentially maybe even the bank itself (Kalotychou and Staikouras, 2006). On the other hand,
focusing on few sectors, increases the ability to monitor and screen the loans, decreasing default rates
(Hayden and Westernhagen, 2007). Winton (1999) argues that loan diversification benefits depend on the
level of risk of the loans already outstanding. Acharya, Hasan and Saunders (2006) tested this prediction
and indeed found that the benefit of diversification reduces when banks are riskier.
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2.5. Herding
Herding occurs when firms (here: banks) make similar or even the same asset holding and risk-taking
decisions (Liu, 2011). Theory gives some reasons why banks in particular would herd: performance-based
reward structures for managers (Scharfstein and Stein, 1990, Rajan, 1994), protection against liquidity
shocks (Kahn and Santos, 2010), lack of information (Liu, 2011), decreasing deposit rates (Penati and
Protopapadakis, 1988, Acharya and Yorulmazer, 2005) and ‘too-many-to-fail’ regulation (Acharya and
Yorulmazer, 2007, Mitchell, 1997).
Few researchers have empirically investigated the existence of herding. Jain and Gupta (1987) found
ambiguous evidence of herding among large banks, but was able to show that small (regional) banks do
follow the decisions made by large banks. Barron and Valev (2000) support this last conclusion.
2.6. Homogeneous banking system
If all banks would perfectly diversify their activities and portfolios, all banks would look the same and
hold the market portfolio implying perfectly correlation of their risks. This extreme example signifies an
entirely homogeneous banking system. The same happens if all banks perfectly herd each other. The only
difference between homogeneity through diversification and through herding is that the former
unintentionally increases homogeneity while more homogeneity is the intention for the latter. When there
is a homogeneous banking sector, all banks have exactly the same exposures to the same shocks, which is
important for the level of systemic risk in the economy. As this is the main topic of this thesis, it is
important to elaborate further.
2.6.1. Homogeneous banking system and systemic risk
The effect of correlated exposures has mainly been investigated theoretically and until today, all authors
conclude that a homogeneous banking system is unfavorable for stability. Shaffer (1994) is one of the
first making a contribution in this research area. He finds that joint failures increase after a merger due to
the fact that both parts of the merger suffer from shocks or shortfalls to the other as risks are now shared.
This is the same intuition as when two banks invest in the same asset. Acharya (2009), Acharya and
Yorulmazer (2008) and Moore and Zhou (2013) generalize this view and conclude that joint failures are
more probable when banks choose to correlate their portfolios of assets. Elsinger, Lehar and Summer
(2006) even show that the importance of correlated exposure as a source of systemic risk is larger than
contagion. Wagner (2010, 2008) expands on our knowledge by discussing through which channel the
relation between homogeneity and systemic risk runs. He argues that homogenization equalizes risks
distribution among institutions, decreasing the probability of individual failure, but making joint failure
more likely. As systemic failure has greater negative externalities, it is more unfavorable. Wagner
therefore proposes to discourage diversification by charging higher capital requirements for more
diversified banks, a finding that is corroborated by Allen et al. (2010), Ibragimov, Jaffe and Walden (2011)
and Boot and Thakor (2010). Barth and Schnabel (2013) investigate another channel through which
homogenization affects systemic risk and argue that when one bank suffers from a shortage in liquidity, it
has to sell its assets at fire-sale prices, deteriorating prices of assets of other banks as well and as such set
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off a domino effect. Kahn and Santos (2010) extend this reasoning and find that when banks are
stimulated to provide liquidity themselves, they fail to find a proper degree of interdependence and
collectively become too risky, increasing systemic risk.
Billio, Getmansky, Lo and Pelizzon (2012) empirically investigated the level of interconnectedness
in the financial system and found banking and insurance sector to be important contributors. As these
institutions also hold illiquid assets and are unable to withstand instant large losses, they are especially
vulnerable to systemic shocks, increasing the general level of systemic risk found in the financial system.
2.6.2. Homogeneity through banks’ loan portfolio
Only few researches have looked at homogenization of the banking system from a loan portfolio point of
view due to the fact that data on bank loan portfolio composition is not readily available. Cai, et al. (2012)
investigated homogeneity in banks’ syndicated loan portfolio. Their findings confirm the theories
mentioned above; more homogeneous banks contribute more to systemic risk. Besides that, Caballero
(2012) found that more interconnectedness based on network statistics in syndicated loan exposure
increases incidences of a systemic banking crisis. However, he also found that the importance of a bank to
the global network, which is a proxy for easier and quicker access to international funding, has a negative
effect of the on banking crisis incidences in a country.
2.7. Setup of the thesis
This thesis adds to the already existing literature by empirically investigating whether systemic risk is
positively influenced by homogeneity in the banking sector, where the latter is measured via accounting
and market-based exposures. As a manually collected database including data on banks’ loan portfolio
exposures will be used, the thesis will give a clearer picture of the relation and complement the research
of Cai, et al. (2012) and Caballero (2012). The setup used, borrows some aspects of Cai et al. (2012)
concerning how to measure homogeneity via banks’ distance to other banks. An important difference is
the risk measure used and the data that will be investigated. Taking into account current literature, I
expect to find the level of homogeneity to significantly positively influence systemic risk.
3. Data and Methodology
In this section I will describe the data and the methodology used. First, I will discuss where the data was
retrieved. Secondly, the construction of the most important variables (distance measures and MES) and
the control variables are covered. Third, the summary statistics and some initial correlations (found in
Table I and II) will be discussed and the section will conclude with a discussion of how the regressions
are constructed.
- 16 -
3.1. Data
3.1.1. Database collection
A database with manually collected data was merged with data gathered from Bankscope, Datastream,
Barth Caprio and Levine database and some other databases. The manually collected database contains
annual data on the 466 largest listed banks in the world for the years 2007 to 2011. For each bank that
reports a sectoral allocation of their loan portfolio in the annual reports, this data was collected and
categorized into ten economic sectors based on the Standard Industrial Classification (SIC)5. As this
thesis focuses on the exposure to corporate loans, personal/consumer loans, loans to central governments
and interbank loans were excluded in the data collection process. In several cases assumptions were
needed for the other sectors, due to the fact that some items could be allocated to more than one category
or other items were not a clear fit with one of the ten categories. These assumptions were put in a separate
file, which can be found in Appendix 3 together with a more elaborated description of the data collection
and an example. Sometimes, there was a large change in the way the sectors were reported in the annual
reports of one bank throughout the years, which resulted in an unrealistic change in sectoral allocation for
that particular bank. It was then decided to keep the most recent years for accuracy. Occasionally,
however, certain banks did not report a sectoral breakdown of their loan portfolio at all, these banks were
therefore excluded. In the end, sufficient data for at least one of the years 2007-2011 could be found for
335 banks in 62 countries.
3.1.2. Regional division
In order to investigate homogeneity in the banking sector, it is necessary to firstly decide what comprises
‘a banking sector’. In this thesis, three different ‘banking sectors’ will be used; national, international
(region) and continental.
Data on the nationality of banks was retrieved from Bankscope. A decision on how to allocate these
countries to a region and a continent was made by using data from the United Nations Statistics Division
(UNSD) (2012)6. The UNSD allocates all countries into five continents (Africa, Americas, Asia, Europe
and Oceania), which are subdivided into 22 regions. A list of all banks included in the database, their
residence country and regional/continental allocation can be found in Appendix 4. In order to make the
research more accurate, I decided to modify the allocation of UNSD slightly. In this thesis, the continent
‘Americas’ is subdivided into ‘Northern and Central America’ (encompassing the regions: Northern
America, Central America and Caribbean) and ‘Latin America’, which is decided because the banks in
Central America for which data is present all have the Mexican nationality and Mexico is a member of the
NAFTA (international trade agreement with U.S. and Canada). So viewing Mexico, U.S. and Canada as
one continent and Latin America as another, will represent the reality in a better way. Besides that, in
Bankscope, Taiwan is mentioned as the residence country of some banks. I therefore regarded Taiwan as
a sovereign entity in the country-level comparison and added it to the region ‘Eastern Asia’ and the
continent ‘Asia’. In total, 64 countries, 17 regions and 6 continents were added to the database.
5 Retrieved May 3, 2013 from http://www.sec.gov/info/edgar/siccodes.htm 6 Retrieved June 2, 2013 from http://unstats.un.org/unsd/methods/m49/m49regin.htm
- 17 -
3.2. Empirical methodology
In this thesis, the level of homogeneity in a banking sector will be proxied by looking at how ‘distant’ a
bank’s loan and market-based exposures are to the exposures of other banks in the same country, region
or continent.
3.2.1. The accounting distance measures
Pair wise distance
The manually collected database includes portfolio weights of loan portfolio exposures per sector. In
order to find the pair wise distance (PWD) between banks, the method in Cai et al. (2012) is used, who
measure distance as the Euclidean distance between banks on a J-dimension space7. PWD thus measures
the distance (versus similarity) between the loan portfolios of one bank to another. In order to find the
PWD of one bank in a country, region or continent, all Euclidean distances of that one bank to other
banks in the same area are averaged. A higher value for PWD indicates that the bank in question is not
homogeneous in its lending activities to other banks in the same area. PWD will be explained further
using formulas. Here, indicates the portfolio weight where is a specific bank, represents a loan
category and denotes the year of interest. It is important to note that for all pairs of and the following
holds: ∑ . The distance between banks and ( ) in year is then computed by using
the following formula (Cai et al., (2012); p. 8):
√∑
cannot be smaller than zero or larger than √ by construction and the larger the outcome, the more
distant (or: the less homogeneous) two banks are on a Euclidian J-dimension space.
In order to obtain the average pair wise distance of one bank to all other banks in a region, the
following formula is applied, as adapted from Cai et al. (2012, p. 9):
(∑
) ( )
Where is the distance calculated in equation (1) of banks and , both present in region .
is the total number of banks present in region in year . The higher , the more distant a
bank is from the other banks in the region and thus the less homogeneous the bank is to the other banks.
The PWD indicators on country and continental level are constructed the same way.
Banks’ distance from an average
A second distance measure will be used in the regressions as a robustness check. Here the Euclidean
distance will be calculated by looking at a bank’s average distance from the ‘average bank’. The portfolio
weights of the ‘average bank’ are calculated via the following formula:
7 “The Euclidean distance is the square root of the sum of the squared differences in portfolio weights across all dimensions of
lending specializations” (Cai et al., CAI, J., SAUNDERS, A. & STEFFEN, S. 2012. Syndication, Interconnectedness, and
Systemic Risk. NYU Working Paper. New York.; p. 8)
- 18 -
(∑
)
When the portfolio composition of the ‘average bank’ in a region is established, the distance from the
average bank will be calculated using formula (1) but including the average portfolio weights:
√∑
For completeness, the ´average bank´ was also calculated excluding the bank in question, on national,
regional and continental level. Due to little divergence, this thesis only reports the measures excluding the
bank in question.
3.2.2. The market-based distance measure
Market-based exposures are calculated using daily data from Datastream. Regressions were run with the
return of a bank as dependent variable and indices of different sectors as independent variables. The betas
given by these regressions then indicate the exposures of a bank to the sectors.
The market-based distance measure (MBD) is a measures for the interconnectedness of one bank to
other banks in a country via their market-based exposures to sectoral shocks. A smaller value for MBD
indicates banks’ market exposures are more correlated. MBD is constructed using the formulas (3) and (4)
where is replaced by the sectoral betas of the bank and with four different restrictions. Distance all
sector betas (MBD_A) includes the betas for all sectors found for all banks in one country, Distance all
sector betas if t-stat>1 (MBD_AT) only included the betas for which the t-statistics were larger than 1.000,
other betas were set to zero (which indicates ‘no exposure’ to the specific sector), Distance excl financial
beta (MBD_-F) excludes the financial sector beta as this beta might be noisier and Distance excl financial
beta and if t-stat>1 (MBD_-FT) both excludes the financial sector and only takes the beta’s into account
of which the t-statistic is larger than 1.000. Moreover, for completeness, MBD is measured including and
excluding the bank in question. As results were equivalent, only MBD excluding the bank in question will
be reported.
3.2.3. Systemic risk
Systematic risk is measured by using the Marginal Expected Shortfall (MES) as introduced in Acharya,
Pedersen, Philippon & Richardson (2010). They state that a bank is more systemically risky if it is prone
to endure capital shortage while the financial sector is fragile itself as this damages the real economy as
well. MES therefore assesses a “bank’s contribution to this [systemic] risk” (Acharya et al., 2010; p.8)
and measures the average returns of a specific bank conditional on the banking market having its α%
worst trading days in a year. In other words: how does a downturn in the market affect the bank in
question? In this research a standard risk level of 5% (α=5%), a national and (constructed) global index
and daily returns from Datastream will be used. MES will then be measured by:
[
]
- 19 -
Where, refers to the 5% worst trading days of the market index,
is the equity of bank the day
before and
is the equity of the bank on one these worse trading days and (Acharya et al., 2010). A
higher value of MES can be interpreted as the bank being more systemically risky.
3.2.4. Control variables
In the regressions that will be displayed in the next section, control variables are included in order to be
more certain that the variability in MES is correctly explained by the several distance measures instead of
another omitted variable. The added control variables will capture strategic decisions that are made by
managers in a bank and which affect a bank’s systemic risk profile. Firstly, banks that are better
capitalized have higher liquidity and more well-performing loans are less vulnerable to market-wide
shocks in general. Therefore, Equity to Total Assets is added as proxy for capitalization (as in De Jonghe
(2010), Lehar (2005) and Martikainen (1991)), Liquid Assets to Total Assets (as in De Jonghe (2010)) and
Loans to Total Assets as a proxy for liquidity, while Non-Performing Loans ratio proxies for vulnerability
of the bank’s asset portfolio. Besides that, Herfindahl-Hirschman Index concerning Total Assets is
included to proxy for competition, as this is found to deteriorate profits as well. Risk is shown to increase
with bank size, bank asset growth, functional diversification and non-deposit funding. In order to control
for these factors, (Logarithm) of Total Assets (Brunnermeier et al., 2012, Lehar, 2005, Gauthier et al.,
2012), Growth in Total Assets (Barrel et al., 2011), Non-Interest Income Share (De Jonghe, 2010,
Brunnermeier et al., 2012) and Total Deposits in Total Funding (Demirgüc-Kunt and Huizinga, 2010) are
included. Besides that, Return on Assets is included to control for a risk-return tradeoff (as in Martikainen
(1991). Stiroh (2004) and Demirgüç-Kunt & Huizinga (2010)).
3.3. Summary statistics and correlations
The descriptive statistics of the variables used in this thesis can be found in Table I. The pair wise
correlations of the most important variables can be found in Table II. No conclusions on the causal
linkage of the variables can be drawn from Table II, but information on the relationship between the
different variables is provided.
3.3.1. MES
Buch (2005) shows that banks still prefer to base their activities nearby. This would result in a higher
exposure to shocks in the local banking market index than the global index, which is exactly what the data
in panel A of Table I shows (global MES is smaller on average than local MES).
Panel A of Table II shows how the two measures for MES are interrelated and confirms the finding
in Table I. Local and global MES show a pair wise correlation of 0.4317, which is statistically significant,
but quite low considering that the only difference is the index used of which the 5% worst trading days
are taken. This indicates that on average local shocks do not coincide with global shocks.
In writing this thesis all regressions were run with both measures as dependent variables, but results
were often equivalent. Therefore, only the regressions with local MES will be shown because this
measure is more accurate, consistent with Buch (2005) and the outcome of table I and II.
- 20 -
Table I
Summary Statistics This table gives the descriptive statistics of the dependent, independent and control variables used in the different regressions
Besides the accounting distance measures, all variables are truncated at the 1% level to mitigate the impact of outliers. Panel A
shows the statistics for the Marginal Expected Shortfall (MES). Local MES is measured by looking at the return of a bank during
the 5% worst trading days of a national banking index in a year, global MES is measured the same but the 5% worst trading days
of a (self-constructed) global banking index were used. Panel B shows the independent variables of interest; the distance
measures. Distance between two banks is measured as their Euclidean distance (their positions in the Euclidean space based on
sectoral loan portfolio allocation). The accounting-based distance measures are constructed using data retrieved from annual
reports of the 466 largest listed banks worldwide. Pair wise distance (PWD) is measured as the average of all Euclidean distances
of one bank to the other banks in a country, region or continent. To construct Distance from average loan portfolio (DAV), first
the average loan portfolio per area is calculated of which the Euclidean distance from one bank’s loan portfolio is computed. The
market-based distances are calculated by using sectoral betas retrieved when running regressions based on daily stock return of
the banks from Datastream as dependent variable and indices of the different sectors as independent variable. Distance all sector
betas (MBD_A) includes the betas for all sectors when calculating distance, Distance all sector betas if t-stat>1 (MBD_AT) only
included the betas for which the t-statistics were larger than 1.000, other betas were set to zero, Distance excl financial beta
(MBD_-F) excludes the financial sector beta from the distance measure and Distance excl financial beta and if t-stat>1 (MBD_-
FT) both excludes the financial sector and only takes the beta’s into account of which the t-statistic is larger than 1.000. Panel C
shows descriptive statistics of the control variables used in the regressions.
Variables Obs. Mean Std. Dev. Min Max
A: Dependent variables
Local MES 5730 2.6270 2.3691 -1.1259 11.3249
Global MES 5776 0.9701 1.3766 -1.5040 5.9120
B: Independent variables
Distance measures based on accounting data
Pair wise distance (PWD) in a country 2847 0.3072 0.1349 0.0000 0.9716
Pair wise distance (PWD) in a region 2915 0.3548 0.0992 0.0000 0.9070
Pair wise distance (PWD) in a continent 2925 0.3800 0.0910 0.1865 0.9098
Distance from average loan portfolio (DAV) in a country 2837 0.2633 0.1425 0.0514 1.0066
Distance from average loan portfolio (DAV) in a region 2915 0.2836 0.1236 0.0574 0.9503
Distance from average loan portfolio (DAV) in a continent 2925 0.2975 0.1233 0.0574 0.9433
Distance measures based on market exposure
Distance all sector betas (MBD_A) 5828 1.7136 1.9492 0.1856 12.8645
Distance all sector betas if t-stat>1 (MBD_AT) 5828 1.4169 1.6906 0.1480 10.8673
Distance excl. financial beta (MBD_-F) 5828 1.6851 1.9480 0.1720 12.8636
Distance excl financial beta and if t-stat>1 (MBD_-FT) 5828 1.3807 1.6889 0.1339 10.8673
C: Control variables
Ln(Total Assets) 4239 12.3825 2.5473 7.9010 19.0229
Equity to Total Assets 4239 0.0808 0.0392 0.0164 0.2400
Liquid Assets to Total Assets 4239 22.0852 20.1622 1.8700 112.0300
Return on Assets 4044 0.9666 1.0226 -2.6200 4.3400
HHI – Asset Market Share 3895 0.1543 0.1714 0.0332 1.0000
Non-Interest Income Share 4224 0.3374 0.1756 0.0288 0.9031
Growth in Total Assets 4011 12.4310 17.5569 -14.9600 93.3700
Non-Performing Loans ratio 3815 0.0392 0.0402 0.0009 0.2217
Loans to Total Assets 4236 0.5812 0.1521 0.0860 0.8619
Total Deposits in Total Funding 4207 0.8231 0.1799 0.2063 1.0000
- 21 -
3.3.2. Distance measures
Panel B of Table I first gives descriptive statistics of the distance measures based on accounting data and
market-based exposures. Panel B and C of Table II show the correlations between the MES and the
distance measures. I will discuss both distance measures below.
Distance measures based on accounting data
In Table I a different amount of observations is found per level for both PWD and DAV. This occurs
because for some countries and regions in the sample only one bank could be investigated, making it
impossible to construct PWD or DAV. The continental PWD and DAV could be constructed for each
bank because there were always other banks in the same continent.
As can be seen from Table I, the PWD reports a higher average distance with a lower standard
deviation than the DAV. This is intuitive because the former is more accurate as it measures the distance
between two banks directly while the latter only looks at the distance from an average. Besides that,
distance increases if we move from country to continent, meaning that geographic distance still matters
and banks have a loan portfolio that is on average more alike with banks that are geographically closer.
In panel B of Table II the difference between DAV and PWD is apparent again as the former has
lower correlation coefficients and of smaller significance than the latter. This means that PWD moves
more together with MES than DAV. Local MES shows the highest correlation with both regional PWD
and DAV while global MES is mostly correlated with continental PWD and national DAV. Most striking
result of panel B of Table II is that all correlation coefficients are positive. This is not in line with the
theories discussed in section 2, which all implied a negative relation.
The correlations between the different accounting-based distance measures can be found in panel D
of Table II. As expected, all distance measures are highly correlated (and statistically significant)
especially when measured at the same level. It is therefore likely that regressions using either one of the
PWD measures or one of the DAV measures as independent variables will give similar outcomes in
equivalent regression models.
Distance measures based on market-based exposures
As the betas used in calculating MBD can take on more values than accounting-based weights, MBD
shows a larger range, which can be seen in panel B of Table I. MBD_AT and MBD_-FT show a lower
average distance. This is intuitive because these distances were constructed while including some betas
that were put to zero.
Panel C of Table II shows the correlations between the market-based distance measures and MES.
Almost all distance measures are statistically significant although global MES shows higher coefficients.
An interesting result is that correlation coefficients of local MES and MBD_A and MBD_-F, are negative
while the others are positive. Besides that, Panel D of Table II (right bottom) shows that all MBD are
highly correlated with each other, which is as expected as all measures rely on the same information.
Lastly, panel D of Table II also shows the correlations between the accounting and market-based
distance measures (left bottom). The small coefficients and their insignificance indicate that the measures
- 22 -
Table II
Pair wise Correlations This table shows pair wise correlations between the two MES (Marginal Expected Shortfall) measures and the different distance
measures. MES the average return of a bank during the 5% worst trading days in a national (Local MES) or global banking index
(Global MES). Distance is measured by a bank’s Euclidean distance (their position in the Euclidean space based on sectoral loan
portfolio weights or sectoral betas). Pair wise distance (PWD) is measured as the average of all Euclidean distances of one bank to
the other banks in a country, region or continent. To construct Distance from average loan portfolio (DAV), first the average loan
portfolio per area is calculated of which the Euclidean distance from one bank’s loan portfolio is computed. Market-based
distances (MBD) are calculated the same as accounting distances, but instead of loan portfolio weights, regression sectoral betas
are used. Distance all sector betas (MBD_A) includes the betas for all sectors, Distance all sector betas if t-stat>1 (MBD_AT) only
included the betas for which the t-statistics were larger than 1, other betas were set to zero, Distance excl financial beta (MBD_-F)
excludes the financial sector beta and Distance excl financial beta and if t-stat>1 (MBD_-FT) both excludes the financial sector
and only takes the beta’s into account of which the t-statistic is larger than 1. Panel A shows the pair wise correlation between the
two measures for MES. Panel B gives the pair wise correlations for the accounting-based distance measures and MES. Panel C
provides pair wise correlations between market-based distance measures and MES and panel D shows pair wise correlations
between the accounting and market-based distance measures. P-values are presented in italics below the correlation coefficients
A: MES
Local MES
Global MES 0.4317
0.0000
B: Distance measures based on accounting data
DAV
country
PWD
country
DAV
region
PWD
region
DAV
continent
PWD
continent
Local MES 0.0521 0.1132 0.0844 0.1251 0.0700 0.1222
0.0087 0.0000 0.0000 0.0000 0.0004 0.0000
Global MES 0.0943 0.1581 0.0669 0.1427 0.0752 0.1665
0.0000 0.0000 0.0006 0.0000 0.0001 0.0000
C: Distance measures based on market exposures
MBD_A MBD_AT MBD_-F MBD_-FT
Local MES -0.0315 0.0412 -0.0527 0.0145
0.0172 0.0018 0.0001 0.2731
Global MES 0.0637 0.0913 0.0556 0.0801
0.0000 0.0000 0.0000 0.0000
D: Pair wise correlations of all distance measures
Accounting-based distance measures
Market-based distance measures
DAV
country
PWD
country
DAV
region
PWD
region
DAV
continent
PWD
continent MBD_A MBD_AT MBD_-F
Acc
oun
ting
-bas
ed d
ista
nce
mea
sure
s
PWD country 0.8529 1.0000
0.0000
DAV region 0.7788 0.6569 1.0000
0.0000 0.0000
PWD region 0.7274 0.7441 0.8630 1.0000
0.0000 0.0000 0.0000 DAV continent 0.7380 0.6112 0.9216 0.8015 1.0000
0.0000 0.0000 0.0000 0.0000
PWD continent 0.7248 0.6842 0.8623 0.8794 0.9218 1.0000
0.0000 0.0000 0.0000 0.0000 0.0000
Mar
ket
-bas
ed
dis
tan
ce m
easu
res
MBD_A 0.0065 0.0053 0.0154 0.0169 0.0458 0.0517 1.0000
0.7414 0.7900 0.4322 0.3889 0.0192 0.0083
MBD_AT -0.0012 0.0085 -0.0080 0.0113 0.0191 0.0428 0.8892 1.0000
0.9515 0.6686 0.6829 0.5650 0.3300 0.0287 0.0000 MBD_-F 0.0019 -0.0032 0.0135 0.0107 0.0444 0.0465 0.9989 0.8866 1.0000
0.9238 0.8728 0.4914 0.5860 0.0234 0.0176 0.0000 0.0000
MBD_-FT -0.0074 -0.0028 -0.0105 0.0030 0.0173 0.0361 0.8845 0.9975 0.8849
0.7087 0.8861 0.5926 0.8776 0.3760 0.0650 0.0000 0.0000 0.0000
- 23 -
are hardly interrelated and thus provide different information. Therefore, both are relevant when
investigating the effect of distance on systemic risk.
3.3.3. Control variables
Panel C of Table I shows descriptive statistics of the control variables. There is quite some variation in
size, which ranges from $2.7 billion to $178.4 trillion. Banks on average hold equity equal to 8% of asset
value next to their funding by deposits, which on average equals 82% of total funding. Besides that, the
average bank in this sample holds 22% of its assets in liquid investments and 58% in loans of which 4%
is non-performing. It obtains 34% of income from other sources than loans, shows a return of almost 1%
and grows 12% a year in total assets.
Return on Assets and Growth in Total Assets show negative values as well. This can be explained by
the fact that the data in this sample includes the credit crisis of 2007-2009 and its repercussions.
Additionally, market concentration in the countries where the banks in this sample reside is on average
15%, which makes the average banking market ‘moderately concentrated’ (Antitrust Division of United
States Department of Justice and Federal Trade Commission, 2010).
3.4. Construction of the regressions
In order to construct the model for the baseline regression certain assumptions had to be made. Firstly,
year fixed effects are added to eliminate the possibility of an omitted variable. Besides that, as the
accounting-based distances only vary less than the standard deviation over the years 2007-2011 (appendix
Table A1 panel A) and to increase the sample, the value for 2007 is used as proxy for 2002 to 2006. The
MBD also shows little variation over time (appendix Table A1 panel B), making bank-fixed effects otiose
for both measures.
Both local and global MES, all market-based distance measures and control variables are trimmed at
one percent level to get rid of outliers that could distort the regression. Accounting-based distance
measures are not trimmed because they can, by construction, only range from zero to √ . Additionally,
the MES is forwarded one period (year) in order to control for reverse causality.
The multiple dimensions of a panel data set result in correlated residuals that might cause a bias in
the OLS standard errors. Petersen (2009), who investigated this issue, proposed a technique which I
followed in order to make sure no coefficient would unrightfully be called ´significant´. While
experimenting with different cluster options (appendix Table A2 shows an example), the most restrictive
one (regional clustering) was chosen.
4. Results
In order to gain more insight in the causal relationship between distance and systemic risk, other
regressions are performed. This section will present the empirical results of these tests using first
- 24 -
accounting data and later market-based data. Afterwards some extensions of the baseline model will be
discussed.
4.1 Baseline regression: distance and systemic risk
4.1.1. Preferred model using accounting-based data
The preferred model involves regional PWD and local MES as both are the most accurate. National PWD
is too narrow and continental PWD and global MES too broad as they include regions/countries with
different economic development.
The results of the baseline regression can be found in Table III. In column (1) a regression without
any control variables is shown. Column (2) adds the control variables, which increases the
to almost 39%. Moreover, regional PWD is significant at the 1% level with a positive coefficient of 2.337,
indicating that systemic risk rises when banks are more distant (less homogeneous). This result is again
not in line with current literature on the effect of homogeneity between banks on systemic risk (e.g.
Acharya (2009), Acharya and Yorulmazer (2008), Cai et al. (2012) and Wagner (2010)). The economic
impact of this coefficient can be gauged using the following formula:
which gives a value of 7.12% indicating that an increase in distance of one standard deviation will result
in a 7.12% increase in forwarded local MES. Regional PWD is thus both statistically and economically
relevant.
As can be seen in Table III, size seems to be unrelated to systemic risk (in line with Lehar (2005)
and Gauthier et al. (2012)), while growth is related (in line with Barrel et al. (2011)). Equity to Total
Assets shows the expected sign and significance level (in line with De Jonghe (2010)). The proxy for a
banks funding mix, for bank market competition and the two proxies for liquidity are all insignificant. No
conclusions can be drawn concerning their relation to systemic risk. Furthermore, profitability seems to
be unrelated to MES as well, which is not in line with Lehar (2005) and Martikainen (1991). Non-
Performing Loans ratio does show the sign and significance level as predicted. Lastly, the level of
functional diversification also matters for systemic risk (in line with De Jonghe (2010) and Brunnermeier
et al. (2012)).
Overall, it is possible to conclude that the first findings of the effect of a homogeneous banking
sector (small distance) on systemic risk contradicts the current theories and earlier empirical results based
on the syndicated loan market. The control variables in contrast, are on average in line with literature.
4.1.2. Robustness checks
In order to check the robustness of the results, regressions using the other PWD and DAV were run as
well of which some of the results can be found in columns (3) to (5) of Table III. All distance measures
are again positive and significantly related to the dependent variable. The coefficient and economic
impact (7.04%) for country PWD are smaller than for regional PWD. This indicates that a bank’s
homogeneity level compared to others in a region is a more important determinant for systemic risk than
- 25 -
Table III
Accounting-based Distance and Systemic Risk This table captures the effect of accounting-based bank homogeneity on systemic risk. Marginal Expected Shortfall (MES), the average
return of a bank during the 5% worst trading days of an index, is the dependent variable and regressed on a proxy for homogeneity and a
group of banks specific control variables. Accounting bank homogeneity is measured by a bank’s Euclidean distance (as positioned in the
Euclidean space based on sectoral loan portfolio weights), where higher distance indicates less homogeneity. Pair wise distance (PWD) is
the average of all Euclidean distances of one bank to the other banks in a country, region or continent. Distance from average loan
portfolio (DAV) calculates the Euclidean distance from the average loan portfolio. The following OLS regression was run for the different
distance measures:
The unbalanced panel includes data on 466 banks’ sectoral loan portfolio allocation present in 64 countries for the years 2007-2011. The
value for 2007 is used as a proxy for 2002-2006 to extend the sample. Each column includes a different accounting-based distance
measure. Column (2) shows the result of the baseline regression including the preferred accounting distance and systemic risk measure
(regional PWD and local MES). The following four columns confirm this baseline result when using alternative accounting distance
measures. Column (1) - (5) use local MES as dependent variable, while column (6) uses global MES. Both local and global MES are
forwarded one year to mitigate the impact of reverse causality and all variables except the distance measures are trimmed at the 1% level.
Moreover, all regressions control for unobserved heterogeneity at the year level by including year fixed effects. Robust standard errors
corrected for clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at the 10%, 5% and 1%
level.
(1) (2) (3) (4) (5) (6)
VARIABLES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Forwarded
global MES
Pair wise distance (PWD) in a region 1.464 2.337*** 0.967 (1.164) (0.730) (0.552)
Pair wise distance (PWD) in a country 1.699*** (0.567)
Pair wise distance (PWD) in a continent 1.737* (0.969)
Distance from average loan portfolio
(DAV) in a region
1.478**
(0.583)
Ln(Total Assets) -0.005 -0.029 -0.014 -0.014 -0.024 (0.040) (0.034) (0.040) (0.043) (0.037)
Equity to Total Assets -9.928** -10.374** -9.468** -9.513** -2.965 (4.035) (4.098) (4.052) (4.101) (2.437)
Liquid Assets to Total Assets -0.002 -0.001 -0.002 -0.002 0.003 (0.007) (0.007) (0.007) (0.007) (0.004)
Return on Assets 0.098 0.134 0.101 0.091 -0.022 (0.223) (0.231) (0.227) (0.230) (0.095)
HHI – Asset Market Share -0.923 -0.989 -0.943 -0.977 0.068 (0.750) (0.734) (0.782) (0.783) (0.421)
Non-Interest Income Share 2.145** 2.039** 2.172** 2.183** 1.067** (0.903) (0.895) (0.917) (0.940) (0.485)
Growth in Total Assets 0.010*** 0.010*** 0.010*** 0.010*** -0.001 (0.002) (0.002) (0.002) (0.002) (0.002)
Non-Performing Loans ratio 9.279*** 10.050*** 8.795*** 8.895*** 5.653** (2.543) (2.367) (2.643) (2.646) (2.350)
Loans to Total Assets 0.215 -0.027 0.058 0.122 0.900* (0.973) (0.919) (0.999) (1.004) (0.431)
Total Deposits in Total Funding -1.269 -1.090 -1.285 -1.271 -1.964*** (0.876) (0.884) (0.858) (0.872) (0.549)
Constant 2.844*** 3.415* 4.041** 3.802** 4.018** 1.852* (0.473) (1.713) (1.576) (1.763) (1.689) (0.963)
Observations 2,639 2,136 2,104 2,136 2,136 2,154
Number of Countries in sample 60 54 50 54 54 56
Number of Regions in sample 16 16 15 16 16 16
Number of Continents in sample 6 6 6 6 6 6
Number of Banks in sample 323 303 298 303 303 306
Adjusted R-squared 0.298 0.389 0.397 0.385 0.387 0.405
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
- 26 -
its relation to the banks in the same country. This is intuitive when considering the current debate about
whether Europe should unite and become a full banking union8 or the fact that many banking crises do not
restrict to one country but spill over to the other banks in the same (international) region.
Continental PWD has the highest P-value, but is still significant at the 10%-level. Its coefficient and
economic impact (4.85%) are smaller than for regional PWD. Their distance is thus less relevant as an
indicator for local MES, which is intuitive as continents entail countries that are both geographically and
economically apart. Likewise, the regional DAV has a smaller coefficient, economic impact (5.61%) and
significance level, which can be explained by the fact that DAV is not as accurate as PWD even though
region is the preferred level.
Finally, in column (6) global MES is used as dependent variable, which changes the regional PWD
coefficient to marginally significant, as the P-value is precisely 10.00%. Besides that, some of the control
variables turn (in)significant.
Three other robustness checks were run. Firstly, as MES is a volatility measure, but table III
included no bank-specific volatility control variables, the volatility of a bank’s daily stock return and a
proxy for its distance to insolvency (Z-score) were included as robustness check (appendix Table A3).
Secondly, local MES was used instead of forwarded local MES (appendix Table A4). Both tables yield
equivalent results as Table III although the latter has to be considered with caution since reverse causality
might be an issue here. Third, the sample was split and regressions were run for 2002-2006 and 2007-
2011 separately (not shown). Again equivalent positive and significant coefficients were found.
4.2. Extension of the baseline regression
Table IV reports an extension of the baseline regression in order to check for other relations that might
influence previous results. For the regressions in Table IV, the same control variables as in the baseline
regression were used. Column (1) reports the baseline regression for comparison, column (2) looks at the
panel from a dynamic point of view and column (3) checks whether there is a nonlinear relation between
distance and MES.
4.2.1. Lagging the dependent variable
Often in time-series analysis successive observations of the dependent variable are strongly correlated. If
this is the case, it is difficult to draw conclusions on the relationship with the independent variables
because the variation of the dependent variable is partially caused by the variation of its own lagged
variable (Nieuwenhuis, 2009). Acharya et al. (2010) investigate the time-series variation in MES between
June 2006-June 2007 and June 2005-June 2006 and found “a fair amount of stability from year to year”
(Acharya et al., 2010; p. 27). Figure 1 plots all values of MES between 2002 and 2011 against their
forwarded value. Again quite some stability is seen between two consecutive years of MES, which also
have a correlation of 0.5420, significant at the 1% level. It is therefore straightforward to include a lagged
8 The creation of a single European Bank Supervisor in December 2012 was seen as a first step towards this European Banking
Union. More information and a discussion can be found at: http://www.debatingeurope.eu/2012/12/13/does-europe-need-a-
banking-union/#.UhIfEdI3CfU, retrieved August 19, 2013
- 27 -
dependent variable in the regression.
Since the forwarded measure of the
MES is used as dependent variable, just
including the MES is equivalent to
adding a lagged dependent variable.
The results in column (2) of Table IV
show an increased to 57%
and a decreased coefficient of regional
PWD, which does remain significant at
the 10% level. It can therefore be stated
that even when controlling for a relation
between the dependent variable over
time, a higher distance to other banks
still increases a bank’s exposure to
systemic risk.
When investigating global MES
roughly the same correlation between
current and forwarded values was found.
Besides that, regressions yield
approximately the same results and are therefore not reported.
4.2.2. Second order distance measure
Up until now, I have only investigated whether MES and distance between banks’ portfolios in a region
are linearly related, but the relationship can be nonlinear as well. In order to check for a parabolic nature,
a squared distance measure is included in the regression of which the results are demonstrated in column
(3) of Table IV. The squared distance measure slightly increases the and enters negative
and highly significant, indicating a mountain-shaped parabolic nature of the relation. In other words;
when moving from a bank that has a loan portfolio which is (hypothetically) entirely equal to all loan
portfolios in its region to a bank that has a loan portfolio which is (hypothetically) entirely different from
all others, systemic risk will first increase an at a later stage decrease again. A joint-F test validated the
joint significance of the two coefficients.
By using simple mathematics, the top of the parabola can be found. The regression, which is run in
column (3) of Table IV, is as follows:
The turning point of the parabola is then found when the first derivative of this formula to is
equal to zero.
Figure 1
Stability of local MES This figure shows the stability of local MES over the years. The scatter plot
is constructed by putting the values for local MES on the x-axis and
compare them to their equivalent value one year later (forwarded local
MES) on the y-axis. Both measures are trimmed at the 1% level. A best
fitted line is shown.
- 28 -
Table IV
Extension of the regression model This table extents the results found in Table III and investigates whether the model is autoregressive and the presence of a
nonlinear relation between distance and systemic risk. The starting point of these regressions is the baseline regression of Table
III (column (2)), that include regional pair wise distance (regional PWD) and local MES. The unbalanced panel includes data on
466 banks for 2002-20011. The results of the preferred regression model are displayed again in column (1) for comparison.
Column (2) tests for an autoregressive model via the following OLS regression:
Column (3) tests a nonlinear relation between distance and systemic risk via the following OLS regression:
The dependent variable, local MES, is forwarded one year to mitigate the impact of reverse causality. Regional PWD is
constructed using data on 466 banks’ sectoral loan portfolio allocations present in 64 countries for the years 2007-2011. The value
for 2007 is used as a proxy for 2002-2006 to extend the sample. All regressions include the following bank specific control
variables: ln(Total Assets), Equity to Total Assets, Liquid Assets to Total Assets, Return on Assets, HHI – Asset Market Share,
Non-Interest Income Share, Growth in Total Assets, Non-Performing Loans ratio, Loans to Total Assets and Total Deposits in
Total Funding, which are all trimmed at the 1% level as local MES to mitigate the effect of outliers. Moreover, all regressions
control for unobserved heterogeneity at the year level by including year fixed effects. Robust standard errors corrected for
clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level
(1) (2) (3)
VARIABLES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Local MES 0.554*** (0.046)
Pair wise distance (PWD) in a region 2.337*** 0.958* 9.291*** (0.730) (0.494) (2.767)
Squared pair wise distance in a region -8.172** (3.156)
Constant 3.415* 1.521 2.002 (1.713) (0.975) (1.618)
Observations 2,136 2,085 2,136
Number of Countries in sample 54 52 54
Number of Banks in sample 303 298 303
Adjusted R-squared 0.389 0.569 0.392
Control Variables YES YES YES
Year Fixed Effects YES YES YES
Regional Clustered Errors YES YES YES
By rewriting this equation, we get:
In the sample used in this thesis, there are 2837 observations spread among 327 banks that have regional
PWD lower than the turning point and 78 observation spread among 20 banks with a higher value. It can
thus be concluded that for the majority of banks in this sample, becoming less homogeneous (increasing
distance from other banks), increases their systemic risk (until they reach a distance of 0.5685).
4.3. Market-based distance and systemic risk
In Table V the regressions involving market-based distance data can be found. As MBD is calculated by
using all listed banks in a country, national PWD is included in column (1) to make a proper comparison.
- 29 -
4.3.1. Preferred model
Column (2) and (3) of Table V show the baseline regressions of which column (3) is the preferred
model since this regression includes the most restrictive market-based distance measure (explanation on
the different market-based distance measures can be found in section 3.2.2.). Regressions including the
other market-based distance measures are equivalent and therefore not tabulated.
Column (2) starts with MBD_A, which gives a statistically significant coefficient of which the
economic impact is 12.09%. Furthermore, the of the model is slightly higher than its
accounting equivalent (41% versus 39%) indicating that MBD_A explains approximately the same
proportion of the variation in MES as the accounting-based distances.
When we move to column (3), the coefficient of MBD_-FT becomes more significant and larger.
The latter is intuitive as Table I showed that the averages of MBD_AT and MBD_-FT are smaller than
the averages of MBD_A and MBD_-F. So, given that the dependent variable and all control variables are
the same, their relation to MES must be larger.
Another interesting outcome that can be observed is the fact that all MBD betas are positive again
indicating that a smaller correlation of exposures between banks increases their systemic risk. Remember
that in the Table II we also saw some negative pair wise correlations. Now that all relationships are tested
on causality, this disappears. This result is robust when using the same time-period for dependent and
independent variables and when splitting the sample between pre-crisis and crisis/post-crisis years (2002-
2006 and 2007-2011).
4.3.2. Lagging the dependent variable
In column (4) a lagged dependent variable (local MES) is added to the regression in order to control for
correlations within the values of the dependent variable over time (further explained in section 4.2.1.).
Again the increases, indicating that the lagged dependent variable alone explains a large
part of the variation in local MES. Besides that, similar to the regression including accounting-based
distance, the coefficient of MBD_-FT decreases, but remains highly significant.
4.3.3. Second order distance measure
In column (5) a second order MBD_-FT is added, which enters the regression with a negative coefficient
(mountain-shaped parabola) and is significant at the 10% level. A joint-F test indicated that the two
variables are jointly also significantly different from zero. Using formula (8) the top of this parabola is
found at
. In this dataset only 26 observations spread over 21 banks show a distance
larger than 7.1212 and 3829 observations from 435 banks have smaller MBD_-FT, which indicates that
the majority of the banks can increase distance (up to 7.1212) and as a result have higher MES.
Even though market-based distance is very different from accounting-based distances, they show the
same relation to systemic risk. Indicating that the level of homogeneity is important for systemic risk,
both internally via loan portfolio and externally via sectoral market exposures.
- 30 -
Table V
Market-based Distance and Systemic Risk This table captures the effect of market-based bank homogeneity on systemic risk. Systemic risk, as measured by MES, is the
dependent variable and is forwarded one year to mitigate the impact of reverse causality. Market-based bank homogeneity is
measured by a bank’s Euclidean distance (as positioned in the Euclidean space based on sectoral regression betas), where higher
distance indicates less homogeneity. The sectoral betas are retrieved when running regressions based on daily stock return of the
banks from Datastream as dependent variable and indices of the different sectors as independent variable. Distance all sector betas
(MBD_A) includes the betas for all sectors when calculating distance and Distance excl financial beta and if t-stat>1 (MBD_-FT)
both excludes the financial sector and only takes the beta’s into account of which the t-statistic is larger than 1.000. Column (1)
shows the accompanying accounting-based distance regression solely for comparison (equal to column (3) of Table III). In column
(2) MBD_A is added to the regression. Column (3) shows the result of the baseline regression including the preferred market-
based distance and systemic risk measure (MBD_-FT and local MES). Column (4) to (6) show extensions to the preferred model.
Column (4) tests for an autoregressive model by including the lagged dependent variable and column (5) tests for a nonlinear
relation by including a squared value for MBD_-FT. In column (6) it is tested whether herding with the market influences the
relation between market-based distance and systemic risk (formula (10)). All regressions include the following bank specific
control variables: ln(Total Assets), Equity to Total Assets, Liquid Assets to Total Assets, Return on Assets, HHI – Asset Market
Share, Non-Interest Income Share, Growth in Total Assets, Non-Performing Loans ratio, Loans to Total Assets and Total Deposits
in Total Funding, which are all trimmed at the 1% level, as local MES and the market-based distances, to mitigate the effect of
outliers. Moreover, all regressions control for unobserved heterogeneity at the year level by including year fixed effects. Robust
standard errors corrected for clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at
the 10%, 5% and 1% level
(1) (2) (3) (4) (5) (6)
Forwarded Forwarded Forwarded Forwarded Forwarded Forwarded
VARIABLES local MES local MES local MES local MES local MES local MES
Local MES 0.574***
(0.037)
Pair wise distance (PWD) in a country 1.699*** (0.567)
Distance all sector betas (MBD_A) 0.202** (0.069)
Distance excl financial beta and if
t-stat>1 (MBD_-FT)
0.232*** 0.120*** 0.470*** 0.181*** (0.059) (0.021) (0.150) (0.060)
Squared distance excl financial beta and if t-stat>1 -0.033*
(0.016)
Market beta 1.723*** (0.223)
Market beta * distance excl fin and if t-stat>1 -0.048 (0.060)
Constant 4.041** 5.092*** 5.093*** 2.197*** 4.816*** 2.583*** (1.576) (0.973) (0.959) (0.590) (0.934) (0.728)
Observations 2,104 3,010 3,010 3,008 3,010 3,010
Number of Countries in sample 50 53 53 53 53 53
Number of Banks in Sample 298 393 393 393 393 393
Adjusted R-squared 0.397 0.409 0.410 0.599 0.413 0.541
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
4.3.4. Interaction with market beta
It might occur that the relationship between a dependent and an independent variable varies for different
levels of another independent variable. In order to take this into account, an interaction term between the
two independent variables can be added to the model. If the coefficient of this interaction term is
- 31 -
Figure 2
Effect of Market Beta on the Relation
MBD_-FT versus Systemic Risk
significantly different from zero, it indicates that
different levels of the second independent variables
lead to different slopes of the linear relationship
(Nieuwenhuis, 2009).
If two banks are homogeneous, it might be that they
are both similar to the market or that they both
deviate from the market in the same matter. Both
cases will give a low MBD, but for different reasons;
the MBD is low for the former because the bank has
a high market beta (they herd with the market),
while in the latter MBD is low because the banks are
effectively ‘close’. In order to take this into account,
an interaction term between MBD_-FT and the
banks’ market beta is included in the regression in
column (6) of Table V. The interaction term enters
insignificantly indicating that market herding does
not influence the relation between MBD_-FT and
MES and banks are thus not found to herd strongly
with the market. Besides that, including an interaction term does not affect significance of MBD_-FT,
while increases to 54%, indicating that a bank’s market beta explains part of the variation in
MES as well.
It is important to note however, that even though the interaction term is insignificant, its inclusion
makes it impossible to consider the regression betas of both MBD_-FT and market beta as the
unconditional effect of an increase of the variable on MES. The unconditional beta of the former is only
accurate if the value of the latter equals zero (Brambor et al., 2006), which is hardly ever the case. The
same holds for interpreting the standard errors and thus the statistical significance of the variables
(Jaccard et al., 1990). For example, in this case, the value for MBD_-FT only has a standard error of
0.060 (and an accompanying significance level of 0.8%) when the market beta is zero.
In order to argue whether MBD_-FT still significantly influences MES after inclusion of the market
beta as an interaction variable some steps have to be taken. In order to find the marginal effect of an
increase in distance on MES, the derivative of the regression formula with respect to distance has to be
taken. In this case, the regression model is as follows:
The derivative and thus the marginal effect of distance is then (as adapted from Brambor, et al. (2006;
p.73)):
The accompanying standard error for will also depend on the value of and equals (as
adapted from Jaccard, et al. (1990; p.470)):
- 32 -
√
The conditional model thus states that the effect of a change in distance on MES and the significance
level of this relation both depend on the value of the market beta (the interaction variable).
Using both formulas (11) and (12), a graph that displays the reducing effect of market beta on the relation
between MBD_-FT and MES and the accompanying 5% significance bounds, is displayed in Figure 2.
This figure is constructed using a tool developed by Kristopher J. Preacher and R Development Core
Team (2011)9 as explained further in (Preacher et al., 2006). According to this graph, the lower and upper
bounds of market beta between which MBD_-FT significantly influences MES are -0.8817 and 1.3615, or
in 80.5% of all observations of market beta. The relationship thus remains strong even when market beta
is included as (interaction) variable. Overall, the market-based distances between banks still positively
influence systemic risk.
In conclusion, the accounting-based and market-based measures for a bank’s degree of homogeneity
positively affect systemic risk even when taking a nonlinear relation or a correlated dependent variable
over time, into account. The result is robust to using different accounting and market-based distance
measures, adding volatility as a control variable, running the regression while using the same time-period
observation for the dependent and independent variables and adding the market beta. In other words, the
results show that in order to decrease systemic risk, banks have to become more homogeneous both in
their accounting loan exposures and market-based exposures.
5. Effects of regulation on distance versus systemic risk
Banks are suspect to a great deal of regulation in order to “mitigate systemic risk, protect consumers and
ultimately the industry, from opportunistic behavior and achieving […] stability.” (Chortareas et al., 2011;
154). Still, the use and depth of certain regulations differ often, which might influence the relation
between a bank’s level of homogeneity and systemic risk. In this section, therefore, the effect of five
regulatory measures will be tested; namely: Overall Restriction on Banking Activities, The Fraction of
Entry Applicants Denied, The Capital Regulatory Index, Official Supervisory Power and External
Governance Index (elaborated upon in Barth, Caprio & Levine (2013)). A short overview of the existing
literature concerning the measures will be given, the sign as predicted will be conferred and the section
will conclude with a discussion of the results when the regulatory measures are added as interaction
variables to both the accounting and market-based preferred models.
9 This tool can be found on http://www.quantpsy.org./interact/mlr2.htm as assessed on August 3, 2013. More information
concerning the R Foundation for Statistical Computing can be found at http://www.R-project.org.
- 33 -
5.1. Explanation of the regulatory measures and prediction of their effects
5.1.1. Overall Restriction on Banking Activities
The issue of whether banks should diversify their activities or focus on their traditional role is still highly
debated. A short description of literature concerning this topic is given in section 2.4. The Overall
Restrictions on Banking Activities (Act. Rest.) index measures country regulation as to whether banks are
allowed to diversify their activities beyond traditional lending services (a higher index implies more
restrictions (Barth et al., 2013)).
The effect of Act. Rest. on the relation between distance and systemic risk is unclear. On the one
hand, when more restricted, banks will focus on traditional services and systemic risk will consequentially
depend more on loan portfolio (and accompanying market-based) sectoral exposures. Act. Rest. will then
have a positive effect on the relation tested in this thesis. On the other hand, conglomerate banks (which
are not or only slightly restricted) are found to have more correlated exposures (Wagner, 2008), indicating
that for small Act. Rest. the effect of distance on MES is larger than for a high level of Act. Rest. Adding
Act. Rest. as an interaction variable would then reduce the effect of distance on MES.
5.1.2. Fraction of Entry Applicants Denied
Fraction of Entry Applicants Denied (Frac. Den.) is a measure of concentration of the banking market
(Barth et al., 2013). There are two views concerning concentration and systemic risk. Firstly, authors that
support the concentration-stability view argue that concentration through increased profits decrease
excessive risk taking by managers and consequently systemic risk (Hellman et al., 2000, Allen and Gale,
2004). Authors in favor of the concentration-fragility view, argue that a more concentrated banking
system is more fragile because banks take more risk (Nicoló et al., 2004, Boyd and De Nicoló, 2005,
Caminal and Matutes, 2002).
These two opposing views indicate that distance will have a different effect on systemic risk in
countries with different levels of Frac. Den., but the effect can go both ways depending on which view to
follow. I still expect Frac. Den. to negatively affect the relation between distance and systemic risk,
because in concentrated markets (high Frac. Den.) one bank with a low distance value indicates that the
others have to be close as well as there are only few banks present to compare the accounting and market-
based exposures with. This means the entire sector will have a high degree of homogeneity and correlated
exposures, increasing systemic risk. For competitive sectors this is less important as there are many banks
present thus low distance value of one bank does not have to indicate that all banks are ‘close’.
5.1.3. Capital Regulatory Index
Explicit deposit insurance has shown to negatively influence bank risk taking (Buser et al., 1981, Keeley,
1990, Loannidou and Penas, 2010, DeLong and Saunders, 2011) and increase the probability of a
systemic crisis (Demirgüç-Kunt and Detragiache, 2002). In order to reduce these undesirable effects,
capital regulation has been introduced (i.e. banks need to hold a proportion of outstanding deposits in
cash). There are, however, different conclusions concerning the effect on the financial system as a whole.
Some authors argue that capital regulation strengthens the entire financial system (Haldane & Robert
- 34 -
(2011), Demirgüç-Kunt & Detragiache (2002)), but other authors argue that this only happens if the
regulation is based on the part of a bank’s risk that is correlated with the risks of other banks (Acharya,
2009).
I expect Capital Regulatory Index (Cap. Reg.) to negatively influence the relation between distance
and systemic risk as higher Cap. Reg. (more capital stringency (Barth et al., 2013)) will help control a
bank’s systemic risk in the presence of homogenous and heterogeneous banking sectors.
5.1.4. Official Supervisory Power
More powerful official supervisors can use monitoring and disciplining rights to abate corruption in bank
lending behavior (Beck et al., 2006), temper risk taking by managers (Fernandez and Gonzalez, 2005) and
lower banking inefficiencies (Chortareas et al., 2011), resulting in lower probabilities of a bank failure
and contagion. Demirgüç-Kunt and Detragiache (2010) and Becker (1983), however, find that more
supervisory power is related to riskier banks, suggesting that supervisory powers might be misused.
More supervisory power can therefore both soften and strengthen the relation of the level of
homogeneity of a bank and systemic risk. The index that measures Official Supervisory Power (Sup.
Power) is higher when supervisors have greater power (Barth et al., 2013).
5.1.5. External Governance
Banks are still part of one of the most opaque industries in the world due to the assets they hold on their
balance sheet (mostly not ‘physically’ fixed), their ability to take certain (short) positions in trading, their
monitoring role, high leverage, moral hazard and the fact that they still have a crucial role in the economy
(Morgan, 2002). This is the one of the reasons the sector is highly regulated, but regulation can be
complemented by external governance as this serves the same purpose. In order for shareholders to have
the possibility to govern, banks should be as transparent as possible about their activities and exposures.
Unfortunately, the rules revolving the degree of transparency and governance differ per country, which is
captured in the External Governance Index (Ext. Gov.).
Shareholders are unlikely to misuse external governance; therefore Ext. Gov. is predicted to reduce
the effect distance has on systemic risk for the same reasons as Sup. Power might have a reducing effect.
5.1.6. Summary
The Table below shows a summary of the predicted effect of the five regulatory measures on the relation
of distance to systemic risk. The predictions are valid for both accounting and market-based distances.
The left part of the Table displays the specific regulatory measure and the right side whether its predicted
effect is positive (+), negative (-) or unclear.
Variable Predicted effect on the relation of distance to MES (β)
Overall Restriction on Banking Activities unclear
Fraction of Entry Applicants Denied -
Capital Regulatory Index -
Official Supervisory Power unclear
External Governance -
- 35 -
5.2. Accounting-based pair wise distance and regulation
Table VI shows the effect of adding the before mentioned regulation indicators to the baseline regression
of regional PWD. Column (1) shows the baseline model as discussed in section 4.1.1. for comparison.
The first thing that can be noticed is the fact that the amount of observation falls, because for some
regulatory measures only little data is available.
In column (2) of Table VI, the interaction term including Act. Rest. enters the regression positively and
significant, indicating that the level systemic risk of more restricted banks indeed relies more on their loan
portfolio exposures and thus distance from other banks. In other words: when more activities are
restricted, an increase in regional PWD will have a larger impact on systemic risk.
Adding Act. Rest. turns the coefficient for regional PWD negative and insignificant, but when recalling
section 4.3.4., this is only the case if Act. Rest. equals zero and as Act. Rest. has a range of 3-12 the
variable can never be zero. In order to gauge the effect of Act. Rest. on the relation of regional PWD and
systemic risk, a graph was constructed (Figure 3, constructed in the same way as Figure 2 and as
explained in section 4.3.4.). Figure 3 shows that the effect of regional PWD on MES already turns
positive when Act. Rest. equals 5.25, which is the case for 85.13% of the data. Moreover, the relation is
significant when Act. Rest. is higher than 7.24, which holds in 70.76% of the cases. Thus when
differentiating countries according to their level of Act. Rest. the positive relation between regional PWD
and MES remains economically relevant. The results in section 4 already indicated that in order to
decrease systemic risk, banks should be motivated (by policymakers) to decrease their distances from
other banks. The results found here, strengthen this relation, especially in countries that impose strict bank
activity restrictions.
Column (3) of Table VI adds Frac. Den. to the baseline regression. Again the interaction term enters
significantly, while increasing the to 43%. In this case, however, the relationship of PWD
and MES is negatively influenced by the interaction, as was predicted in 5.1.2. and which supports the
concentration-stability view. Figure 4 gives a graphical representation of the effect of Frac. Den. on the
relation between PWD and MES. The graph shows that different policy measures are optimal for
countries with different levels of Frac. Den. When Frac. Den. is smaller than 0.0441, MES is still
positively related to PWD, which holds in 67.6% of the observations in our sample. Policymakers in these
countries should focus on reducing the accounting-based distance between banks in order to reduce
systemic risk. On the other hand, countries where Frac. Den. is higher than 0.2439 (15.3% of the cases)
the relation turns negative. If Frac. Den. is higher than 0.7022 (0.59% of the cases) regional PWD
significantly negatively influences systemic risk. In these cases, the data thus encourages policymakers to
focus on reducing homogeneity in the banking sector. Overall, even though Frac. Den. reduces the effect
of regional PWD on MES, for most countries the empirical results still point to regulatory measures that
decrease distance between banks’ loan portfolio exposures as to decrease systemic risk.
In column (4) and (5) of Table VI Cap. Reg. and Sup. Power, respectively, are added to the
regression. Both interaction terms enter insignificantly indicating that both regulatory measures cannot be
proven to influence the effect PWD has on MES. Besides that, the coefficient for regional PWD turns
negatively and insignificant and there is no value for both Cap. Reg. and Sup. Power that changes the
effect of regional PWD on MES to significant (appendix Figure A1 and A2 show this graphically). In
- 36 -
Table VI
The Effect of Banking Regulation on the Relation between Accounting-
based Distance and Systemic Risk This table differentiates countries based on regulatory policy in order to check whether the relation between accounting-based
homogeneity and systemic risk differs between countries. The starting point of these regressions is the baseline regression of
Table III (column (2)), that include regional pair wise distance (regional PWD) and local MES. The result of the preferred
regression model is displayed again in column (1) for comparison. Column (2) – (6) add different regulatory measures solely
and in an interaction with regional PWD via the following OLS regression:
The dependent variable, local MES, is forwarded one year to mitigate the impact of reverse causality. Regional PWD is
constructed using data on 466 banks’ sectoral loan portfolio allocations for the years 2007-2011. The value for 2007 is used
as a proxy for 2002-2006 to extend the sample. All regressions include the following bank specific control variables: ln(Total
Assets), Equity to Total Assets, Liquid Assets to Total Assets, Return on Assets, HHI – Asset Market Share, Non-Interest
Income Share, Growth in Total Assets, Non-Performing Loans ratio, Loans to Total Assets and Total Deposits in Total
Funding, which are all trimmed at the 1% level as local MES to mitigate the effect of outliers. Moreover, all regressions
control for unobserved heterogeneity at the year level by including year fixed effects. Differences between countries used in a
sample occur due to differences in data availability of the country-specific regulatory measure. Robust standard errors
corrected for clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at the 10%,
5% and 1% level
(1) (2) (3) (4) (5) (6)
Forwarded Forwarded Forwarded Forwarded Forwarded Forwarded
VARIABLES local MES local MES local MES local MES local MES local MES
Pair wise distance in a region (PWD) 2.337*** -4.380 2.444** -4.038 -2.080 37.408** (0.730) (3.270) (0.966) (3.298) (2.804) (16.474)
Activity Restriction -0.303* (0.146)
Act. Rest.*Regional PWD 0.834** (0.372)
Fraction Denied 3.326** (1.226)
Frac. Den.*Regional PWD -10.021*** (3.212)
Capital Regulatory -0.241 (0.202)
Cap. Reg.*Regional PWD 0.712 (0.498)
Supervisory Power -0.053 (0.107)
Sup. Power*Regional PWD 0.284 (0.225)
External Governance 0.678* (0.325)
Ex. Gov.*Regional PWD -2.324* (1.089)
Constant 3.415* 4.595* 2.366 5.034* 3.438 -7.083 (1.713) (2.170) (2.376) (2.704) (2.389) (4.515)
Observations 2,136 1,796 1,179 941 1,253 1,104
Number of Countries in Sample 54 53 42 47 47 39
Number of Regions in sample 16 16 14 15 15 12
Number of Banks in sample 303 295 240 211 214 216
Adjusted R-squared 0.389 0.421 0.429 0.391 0.405 0.408
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
- 37 -
Figure 2-7
Graphically representation of the effect of interaction terms Figure 2-7 graphically show the effect of a interaction term on the relation between distance and systemic risk. These figures are
constructed using a tool developed by Kristopher J. Preacher and R Development Core Team (2011) as explained further in
(Preacher et al., 2006) and in section 4.3.4. Figure 2 can be found on page 31, and Figure 7 can be found on page 40.
Figure 3
Effect of Overall Restriction on Banking
Activities: Regional PWD versus MES
Figure 4
Effect of Fraction of Entry Applicants
Denied : Regional PWD versus MES
Figure 5
Effect of External Governance:
Regional PWD versus Systemic Risk
Figure 6
Effect of Overall Restriction on
Banking Activities: MBD versus MES
- 38 -
other words; when differentiating countries based on Cap. Reg. or Sup. Power no conclusions can be
drawn from the data concerning how regulators should respond to a homogeneous banking market in
order to decrease systemic risk.
Column (6) of Table VI adds Ext. Gov. as interaction term, increasing the to 41%. The
interaction term enters the regression negatively and significant as predicted in 5.1.5. The relation
between regional PWD and MES is still positive and significant for values of Ext. Gov. lower than 15.052,
or in 70.74% of the cases in the database used. In these cases, empirical results indicate that policymakers
would be wise to motivate banks to increase homogeneity in loan portfolios as this decreases systemic
risk.
In conclusion, the data shows that Act. Rest., Frac. Den. and Ext. Gov. are all significant indicators of
how policymakers should focus their regulation. Act. Rest. increases the effect of regional PWD on MES
and while Frac. Den. and Ext. Gov. reduce this relationship, most values lie in such a range that regional
PWD still positively influences MES. It is therefore wise for policymakers to take already existing policy
into account when trying to change systemic risk via banks’ homogeneity in their loan portfolio, but on
average the results show that the best-fitting policy involves lowering accounting-based distance between
banks.
5.3 Market-based distance and regulation
Table VII demonstrates the baseline regression using MBD_-FT and including the interaction terms as
described in section 5.1.. Column (1) shows the baseline regression for comparison. Again, the amount of
observations and countries in the sample drop due to lack of data. One of the first striking results, is the
fact that the signs and significance levels of the interactions and distance measures differ between Table
VII and Table VI, while this was not the case in the regressions excluding policy measures as discussed in
section 4. The sample including MBD is almost twice as large as the sample including PWD and the
nationalities of banks in the samples differ. This results in a overrepresentation of different countries
between the two samples. The accounting-based sample, for example, is highly influenced by Japan (63
banks) versus 25 U.S. banks and 4 France banks. In the sample of MBD, the number of Japanese banks
increases with 11% to 70 banks, while the number of U.S. more than doubles to 65 and France banks
more than quadruple to an inclusion of 17 banks. The market-based sample thus differs in focus from the
accounting-based sample. In order to make sure that the differences between Table VI and VII were not
due to different nationalities of the banks in the sample, the regressions in Table VII were run using the
accounting-based sample as well. These results can be found in appendix Table A5 and they are
equivalent to the results in Table VII indicating that the differences are not sample-based, but measure-
based. Thus there are fundamental differences between the two distance measures when policy measures
are included.
Column (2) of Table VII adds the interaction term including Act. Rest. Inclusion of this term increases the
to 46%. An interesting result of this regression is the fact that the interaction term enters the
regression negatively (while it entered positively in Table VI). Thus when considering market-based data,
the results show that the higher degree of correlation in the market-based exposures of conglomerate
- 39 -
Table VII
The Effect of Banking Regulation on the Relation between Market-based
Distance and Systemic Risk This table differentiates countries based on regulatory policy in order to check whether the relation between market-based
homogeneity and systemic risk differs between countries. The starting point of these regressions is the baseline regression of Table
V (column (3)), which includes MBD_-FT and local MES. The result of the preferred regression model is displayed again in
column (1) for comparison. Column (2) – (6) add different regulatory measures solely and in an interaction with MBD_-FT via the
following OLS regression:
The dependent variable, local MES, is forwarded one year to mitigate the impact of reverse causality. MBD_-FT is constructed
using data on 466 banks’ sectoral regression beta for the years 2002-2011. All regressions include the following bank specific
control variables: ln(Total Assets), Equity to Total Assets, Liquid Assets to Total Assets, Return on Assets, HHI – Asset Market
Share, Non-Interest Income Share, Growth in Total Assets, Non-Performing Loans ratio, Loans to Total Assets and Total Deposits
in Total Funding, which are all trimmed at the 1% level, as local MES and MBD_-FT, to mitigate the effect of outliers. Moreover,
all regressions control for unobserved heterogeneity at the year level by including year fixed effects. Differences between countries
used in a sample occur due to differences in data availability of the country-specific regulatory measure. Robust standard errors
corrected for clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at the 10%, 5%
and 1% level
(1) (2) (3) (4) (5) (6)
Forwarded Forwarded Forwarded Forwarded Forwarded Forwarded
VARIABLES local MES local MES local MES local MES local MES local MES
Distance excl financial beta and
if t-stat>1 (MBD_-FT) 0.232*** 0.676** 0.212** 0.755** 0.170 0.499
(0.059) (0.232) (0.083) (0.325) (0.360) (1.767)
Activity Restriction 0.107*
(0.058)
Act. Rest.*MBD_-FT -0.054* (0.028)
Fraction Denied 0.864* (0.442)
Frac. Den.*MBD_-FT -0.535 (0.343)
Capital Regulatory 0.086 (0.103)
Cap. Reg.*MBD_-FT -0.067* (0.036)
Supervisory Power 0.083* (0.046)
Sup. Power*MBD_-FT 0.001 (0.032)
External Governance -0.047 (0.153)
Ex. Gov.*MBD_-FT -0.017 (0.112)
Constant 5.093*** 3.376** 3.363 3.364* 2.951 4.377 (0.959) (1.304) (1.992) (1.743) (1.745) (3.601)
Observations 3,010 2,632 1,601 1,468 2,002 1,413
Number of Countries in sample 53 53 42 42 47 39
Number of Banks in sample 393 390 333 295 298 269
Adjusted R-squared 0.410 0.458 0.419 0.478 0.481 0.403
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
banks (small Act. Rest.) is indeed more relevant for systemic risk and this effect diminishes as banks
(have to) focus more on traditional activities (while this was the other way around when considering
solely accounting loan portfolio exposures). This outcome can be explained by the fact that market-based
- 40 -
exposures also include risky exposures that come from other activities than lending, which less restricted
banks can perform and which increase systemic risk (Baele et al., 2007, Brunnermeier et al., 2012, De
Jonghe, 2010). More restricted banks cannot perform these activities and therefore have a lower level of
systemic risk. Besides that, the accounting-based distance did not take exposures besides loan exposures
into account therefore focusing on only one part of a bank’s risk-generating activities, which explains the
difference between Table VI and VII.
Figure 6 graphically shows that even though the coefficient is negative, MBD_-FT has a positive
effect on MES throughout the entire range of Act. Rest. (3-12). A significant positive effect is found for
values of Act. Rest. below 9.36 or in 86.8% of the data. The relation between MBD_-FT and MES thus
remains significantly and economically relevant. From a policy point of view the results of column (2) in
Table VII indicate that it is still wise to motivate homogeneity in the banking sector in order to reduce
systemic risk even though the effect diminishes when more restrictions are imposed.
In column (4) of Table VII, the interaction
term including Cap. Reg. enters the regression
negative and significantly. This implies that Cap.
Reg. influences the market-based distances
between banks while it was irrelevant for
accounting-based distances. Besides that, Figure 7
and the negative coefficient of the interaction
show that Cap. Reg. reduces the effect MBD_-FT
has on systemic risk even though the effect is
positive for all values of Cap. Reg. (3-10) and
significant for values below a level of 9.54, which
is the case for 96.52% of the data. Differentiating
countries based on Cap. Reg. thus renders the
relation between MBD_-FT and MES both
statistically and economically relevant. Moreover,
the results indicate it is still wise to impose
regulations that reduce market-based distance in
order to reduce systemic risk, but the effect will be
smaller in countries with higher regulations on
capital holdings.
Column (3), (5) and (6) of Table VII include Frac. Den., Sup. Power and Ext. Gov. respectively. All
three regulatory measures enter the regression insignificantly as an interaction while the stand-alone
regression coefficients of Frac. Den. and Sup. Power do enter significantly at the 10% level. A graphical
representation of the effect of the interaction term on the relation between MBD_-FT and MES can be
found in appendix Figure A3-A5. The graphs and the (small) coefficients show that the effect of
especially Sup. Power and Ext. Gov. is arbitrarily small while Frac. Den. seems to influence the
relationship to some extent. Nevertheless, all three graphs show an area where the relation between
distance and systemic risk is statistically significant, which is the case in 69.3% (Frac. Den.), 61.94%
(Sup. Power) and 17.34% (Ext. Gov.) of the cases. The relation of MBD_-FT on MES thus remains
Figure 7
Effect of Capital Regulatory Index:
MBD versus Systemic Risk
- 41 -
statistically and economically relevant when including Frac. Den. and Sup. Power while its economic
relevance is small when differentiating countries based on Ext. Gov.. Policy advice concluded from these
results, would be to not take a country’s level of Frac. Den., Sup. Power and Ext. Gov. into account when
deciding on how to influence homogeneity to decrease systemic risk. For Sup. Power the same was found
in the regression including accounting-based distance, but for Frac. Den. and Ext. Gov. this was not the
case. These regulatory measures apparently affect the banking sector more internally (accounting-wise)
than externally (market exposure-wise).
In review, when including MBD_-FT instead of accounting distances, the relations differ. The policy
implications however are still the same and indicate that overall, it is best to decrease distance based on
both accounting and market exposures in order to decrease systemic risk although the effect of this policy
from a market-based point of view is smaller for countries with higher Act. Rest., and /or Cap. Reg.
6. Conclusions
This thesis studies the effect of a homogeneous banking system for systemic risk. The accompanying goal
is: providing advice to policymakers regarding how to direct their regulatory measures as to decrease
systemic risk. The results given here are interesting for both policymakers and academics though. Up
until today mainly theoretical models exist that show that homogeneity in banking increase systemic risk,
but hardly empirical evidence to substantiate these models. This thesis therefore tries to fill this gap by
using a manually collected database including banks’ (accounting) loan portfolio and market-based
exposures and testing the effect of homogeneity on systemic risk. Finally, this thesis investigates whether
differentiating countries based on regulatory policy changes this relationship.
Overall, both accounting and market-based measures show a positive, statistically significant and
economically relevant relation between distance (lower distance implies more homogeneity) and systemic
risk. Summarizing: the more homogeneous the banking sector is, the lower systemic risk. This finding is
robust for several checks performed throughout the thesis (e.g. including volatility measures, splitting the
sample between a before-crisis period and in/after-crisis period and using same time period for both
dependent and independent variables). Moreover, the relation appears to be of a mountain-parabolic
nature, where most observations in the database lie on the right-hand side of the parabola, still suggesting
a positive relation on average. Furthermore, herding with the market cannot be proven to affect the
relation between market-based distances and systemic risk. Besides that, the panel used in this database
seems to be dynamic as including a lagged dependent variable highly increases model explanation power.
When differentiating countries based on their regulatory policy, a difference between accounting-
based exposures and market-based exposures is found. The effect on average is still positive and
significant though (indicating higher systemic risk in less homogeneous banking sectors). An increase in
accounting-based distance (less homogeneous banks) will have a stronger effect on systemic risk in
countries with stricter activity restrictions, with a banking market that is (more) open for entry and with
little regulation concerning external governance. However, an increase in market-based distance, has a
- 42 -
larger impact on systemic risk in countries where banks can undertake more activities and where banks
are exposed to little capital regulation.
In conclusion, the data shows that homogeneity decreases systemic risk significantly. Based on the
empirical findings in this thesis, my advice to policymakers would be to impose regulations with a focus
on increasing homogeneity both in lending and in market-based exposures, as this is found to decrease
systemic risk. Since the economic rationale for this conclusion is not straightforward, this remains a topic
for future research.
Limitations and further research
First of all, future research should focus on investigating the channels through which homogeneity
(empirically) decreases systemic risk and how this finding can thus be motivated economically. An
example is to check for cultural differences between the countries as these could influence both the level
of systemic risk and homogeneity in the banking sector. Besides that, other future research should focus
on the limitations of this thesis.
There are some limitations to the research presented in this thesis, which are therefore open for
improvement by future research. First of all, as the results in this thesis entirely contradict current
theoretical literature, an extension would be to use different measures for herding and systemic risk in
order to check whether the results are measure-based and correctly review the banking sector. Secondly,
gathering data on sectoral allocation of loan portfolio was a very intense process and thus only few banks
could be investigated. The research can be improved by expanding the database with more countries and
more (also unlisted) banks. Third, if distance was based on more sectors (instead of ten), it would capture
the real value of homogeneity of a bank in a better way, which is thus a topic for improvement. Fourth, in
this thesis only banks are investigated while many theoretical papers model homogeneity in the entire
financial sector. The research can thus be improved by also investigating the level of homogeneity
between banks and other financial institutions. Fifth, the database used in this thesis includes the crisis
years. In these years, the average MES of the banks was significantly higher than before, influencing the
entire regression outcome. Future research should therefore focus on the years before and after the credit
crisis or include e.g. a crisis dummy. Lastly, and probably most importantly, the sample suffers from a
survivor bias and a selection bias. A survivor bias is created when the restriction was posed to only
investigate banks that were listed throughout 2007 to 2011. A selection bias (Heckman, 1979) is created
due to the manual collection of the data. When banks did not provide a sufficient description of the
sectoral allocation of their loan portfolio, they were discarded in the accounting-based model. More
transparent banks might have lower systemic risk and the results found here might therefore give a wrong
representation of the actual relations. Both biases can distort the results to a large extent. Future research
can improve by adding e.g. a Heckman selection model to the already existing model or by investigate the
banks for which no information could be found.
- 43 -
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- 47 -
Appendices
Appendix 1. Tables
Table A1:
Variation in Distance Measures over Time This table gives an overview of the summary statistics of the distance measure over time in order to see the evolution of the
main independent variable in this thesis. Distance between two banks is measured as their Euclidean distance (their positions in
the Euclidean space based on sectoral loan portfolio weights or sectoral regression betas). Panel A shows the accounting-based
distance measures and panel B the distance measures based on market exposures. Data on sectoral division of loan portfolios
was hand collected from the annual reports of listed banks. In total, the 466 largest banks worldwide were investigated for the
years 2007-2011. Data for at least one of the years mentioned was found for 344 banks and distance measures were constructed
on a country, regional and continental level. Market-based exposures were found by regressing the daily returns of the banks
against indices of different sectors, the betas found in these regressions were used to calculate a distance measure (all country-
level). Data on market exposures is collected for 2002-2011. Distance all banks includes all betas found for all banks, Distance
all banks if t-stat>1 only included the betas for which the t-statistics were larger than 1.000, other betas were set to zero,
Distance all banks excl finance excludes the financial sector beta and Distance all banks excl finance and if t-stat>1 both
excludes the financial sector and only takes the beta’s into account of which the t-statistic is larger than 1.000.
A: Accounting-based distance measure
2007 2008 2009 2010 2011
Pair wise distance in a country Obs. 316 323 324 323 316
Mean 0.3011 0.3127 0.3252 0.3293 0.3235
St. Dev. 0.1371 0.1259 0.1318 0.1325 0.1363
Min 0.0000 0.0000 0.0000 0.0000 0.0000
Max 0.7521 0.7531 0.8039 0.8517 0.9716
Pair wise distance in a region Obs. 324 331 332 331 324
Mean 0.3446 0.3589 0.3754 0.3800 0.3765
St. Dev. 0.0961 0.0983 0.1051 0.1051 0.1093
Min 0.0000 0.0000 0.0000 0.0000 0.1515
Max 0.7022 0.7720 0.8865 0.8689 0.9070
Pair wise distance in a continent Obs. 325 332 333 332 325
Mean 0.3694 0.3838 0.4036 0.4076 0.4033
St. Dev. 0.0901 0.0905 0.0908 0.0920 0.0935
Min 0.1865 0.2036 0.2665 0.2627 0.2673
Max 0.7852 0.8727 0.9098 0.9053 0.8922
Distance from average loan portfolio N 315 322 323 322 315
in a country Mean 0.2715 0.2636 0.2622 0.2630 0.2630
St. Dev. 0.1482 0.1385 0.1380 0.1371 0.1394
Min 0.0540 0.0514 0.0529 0.0555 0.0553
Max 0.8454 0.9943 1.0066 0.9759 0.9509
Distance from average loan portfolio N 324 331 332 331 324
in a region Mean 0.2903 0.2853 0.2857 0.2871 0.2862
St. Dev. 0.1271 0.1229 0.1247 0.1228 0.1253
Min 0.0622 0.0574 0.0685 0.0673 0.0609
Max 0.8614 0.9254 0.9503 0.9321 0.9236
Distance from average loan portfolio N 325 332 333 332 325
in a continent Mean 0.3032 0.2987 0.3005 0.3013 0.3008
St. Dev. 0.1268 0.1234 0.1225 0.1220 0.1230
Min 0.0574 0.0612 0.0710 0.0758 0.0769
Max 0.8867 0.9271 0.9433 0.9264 0.9236
- 48 -
B: Market-based distance measure
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Distance all sectors
Obs 299 319 351 378 394 411 419 427 429 428
Mean 1.2438 1.0660 1.2548 1.1192 1.3695 1.1282 1.3107 1.3429 2.2321 1.4735
St. Dev. 1.1971 1.0571 1.4126 1.0772 1.5452 1.1793 0.7480 1.2514 2.0366 1.2152
Min 0.1856 0.1856 0.1856 0.1856 0.1856 0.1856 0.1856 0.1856 0.1856 0.1856
Max 9.5199 11.4954 12.8645 8.1114 12.8645 12.8645 5.1698 12.8645 12.8645 9.9760
Distance all sectors if t-stat>1
Obs 299 319 351 378 394 411 419 427 429 428
Mean 1.1396 0.9987 1.1822 1.0164 1.0771 0.9643 1.3061 1.2282 2.0862 1.3903
St. Dev. 1.0890 0.9580 1.2097 1.0192 1.1033 1.0488 0.7440 1.0333 1.8773 1.1397
Min 0.1480 0.2220 0.1480 0.1480 0.1480 0.1480 0.1798 0.1480 0.1480 0.1480
Max 6.7908 9.7586 10.8673 9.8590 10.8673 10.8673 5.1607 10.8673 10.8673 9.5155
Distance excl financial beta
Obs 299 319 351 378 394 411 419 427 429 428
Mean 1.2101 1.0540 1.2417 1.1110 1.3609 1.0980 1.1991 1.2467 2.2158 1.4518
St. Dev. 1.1960 1.0590 1.3984 1.0791 1.5476 1.1857 0.6729 1.1743 2.0426 1.2129
Min 0.1720 0.1720 0.1720 0.1720 0.1720 0.1720 0.1720 0.1720 0.1720 0.1720
Max 9.5155 11.4909 12.8636 8.1112 12.8636 12.8636 4.5641 12.8636 12.8636 9.9646
Distance excl financial beta and if t-stat>1
Obs 299 319 351 378 394 411 419 427 429 428
Mean 1.1042 0.9856 1.1642 1.0074 1.0654 0.9321 1.1924 1.1089 2.0671 1.3659
St. Dev. 1.0868 0.9610 1.1768 1.0213 1.1072 1.0545 0.6693 0.9232 1.8859 1.1392
Min 0.1339 0.1766 0.1339 0.1339 0.1339 0.1339 0.1690 0.1339 0.1339 0.1339
Max 6.7848 9.7180 10.8673 9.8590 10.8673 10.8673 4.5516 10.8673 10.8673 9.5153
- 49 -
Table A2:
Testing Clustered Errors This table follows the method of Pederson (2009) in order to choose the correct level of standard errors clustering
(standard errors are reported between parentheses). The table specifically shows the differences in significance level
when using different levels of clustering. All columns show the same regression, namely the baseline regression for the
accounting-based model. In the first column errors are not clustered, column (2) clusters on the bank-level, column (3)
on the country level, column (4) on the regional level and column (5) on continental level. The unbalanced panel
includes data on 466 banks’ sectoral loan portfolio allocation present in 64 countries for the years 2007-2011. The value
for 2007 is used as a proxy for 2002-2006 to extend the sample. Local MES is forwarded one year to mitigate the
impact of reverse causality and all variables except the distance measures are trimmed at the 1% level. Moreover, all
regressions control for unobserved heterogeneity at the year level by including year fixed effects. *, ** and *** denote
significance at the 10%, 5% and 1% level.
(1) (2) (3) (4) (5) VARIABLES Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Forwarded
local MES
Pair wise distance in a region 2.337*** 2.337*** 2.337*** 2.337*** 2.337*** (0.420) (0.669) (0.730) (0.730) (0.384)
Ln(Total Assets) -0.005 -0.005 -0.005 -0.005 -0.005 (0.022) (0.036) (0.047) (0.040) (0.046)
Equity to Total Assets -9.928*** -9.928*** -9.928** -9.928** -9.928* (1.834) (2.884) (4.049) (4.035) (4.157)
Liquid Assets to Total Assets -0.002 -0.002 -0.002 -0.002 -0.002 (0.004) (0.005) (0.006) (0.007) (0.003)
Return on Assets 0.098 0.098 0.098 0.098 0.098 (0.077) (0.103) (0.194) (0.223) (0.269)
HHI – Asset Market Share -0.923*** -0.923** -0.923 -0.923 -0.923 (0.337) (0.358) (0.574) (0.750) (0.515)
Non-Interest Income Share 2.145*** 2.145*** 2.145*** 2.145** 2.145* (0.351) (0.481) (0.778) (0.903) (0.853)
Growth in Total Assets 0.010*** 0.010*** 0.010*** 0.010*** 0.010*** (0.004) (0.004) (0.003) (0.002) (0.001)
Non-Performing Loans ratio 9.279*** 9.279*** 9.279*** 9.279*** 9.279** (1.320) (2.001) (2.717) (2.543) (3.189)
Loans to Total Assets 0.215 0.215 0.215 0.215 0.215 (0.449) (0.661) (0.863) (0.973) (0.554)
Total Deposits in Total Funding -1.269*** -1.269* -1.269 -1.269 -1.269 (0.451) (0.733) (0.997) (0.876) (0.814)
Constant 3.415*** 3.415*** 3.415** 3.415* 3.415** (0.770) (1.255) (1.698) (1.713) (1.109)
Observations 2,136 2,136 2,136 2,136 2,136
Adjusted R-squared 0.389 0.389 0.389 0.389 0.389
Year Fixed Effects YES YES YES YES YES
Bank Clustered Errors NO YES NO NO NO
Country Clustered Errors NO NO YES NO NO
Regional Clustered errors NO NO NO YES NO
Continental Clustered errors NO NO NO NO YES
- 50 -
Table A3:
Robustness check: added volatility measures This table shows the effect of adding a bank’s Z-score (which is a proxy for distance to default) and volatility of daily stock
returns to the regressions in table III. Except the additions of Bank’s Z-score and Volatility of daily stock return the regressions
are exactly the same as in table III. The unbalanced panel includes data on 466 banks’ sectoral loan portfolio allocation present in
64 countries for the years 2007-2011. The value for 2007 is used as a proxy for 2002-2006 to extend the sample. Each column
includes a different accounting-based distance measure. Column (2) shows the result of the baseline regression including the
preferred accounting distance and systemic risk measure (regional PWD and local MES). The following four columns confirm
this baseline result when using alternative accounting distance measures. Column (1) - (5) use local MES as dependent variable,
while column (6) uses global MES. Both local and global MES are forwarded one year to mitigate the impact of reverse
causality. All regressions include the following bank specific control variables: ln(Total Assets), Equity to Total Assets, Liquid
Assets to Total Assets, Return on Assets, HHI – Asset Market Share, Non-Interest Income Share, Growth in Total Assets, Non-
Performing Loans ratio, Loans to Total Assets and Total Deposits in Total Funding, which are all trimmed at the 1% level as
local MES to mitigate the effect of outliers. Moreover, all regressions control for unobserved heterogeneity at the year level by
including year fixed effects. The number of countries, regions, continents and banks in each regression sample are given at the
bottom. Differences in the number of banks and countries used in the regressions are due to the restriction that per area (country,
region, and continent) at least two banks should be present to calculate a distance measure. Per continent, there were always at
least two banks present so a distance measure could always be measured. Robust standard errors corrected for clustering at the
regional-level are reported between parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level.
(1) (2) (3) (4) (5) (6)
Forwarded Forwarded Forwarded Forwarded Forwarded Forwarded
VARIABLES local MES local MES local MES local MES local MES local MES
Pair wise distance in a region 1.464 2.128** 0.747 (1.164) (0.743) (0.531)
Pair wise distance in a country 1.415** (0.628)
Pair wise distance in a continent 1.345 (0.891)
Distance from average loan portfolio in
a region
1.237**
(0.563)
Bank’s Z-score -0.017 -0.046 -0.026 -0.020 0.119* (0.075) (0.078) (0.074) (0.075) (0.061)
Volatility of daily stock return 0.694*** 0.665*** 0.692*** 0.697*** 0.489*** (0.164) (0.167) (0.167) (0.163) (0.068)
Constant 2.844*** 1.952 2.773 2.473 2.563 0.734 (0.473) (1.832) (1.606) (1.871) (1.790) (1.014)
Observations 2,639 1,644 1,619 1,644 1,644 1,655
Adjusted R-squared 0.298 0.450 0.449 0.446 0.447 0.493
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
- 51 -
Table A4
Trimmed, but no forwarded MES This table shows the effect of using the same time-period for both dependent and independent variables. Besides that, the regression
is exactly the same as in table III. The results have to be considered with caution since reverse causality might be an issue here. The
unbalanced panel includes data on 466 banks’ sectoral loan portfolio allocation present in 64 countries for the years 2007-2011. The
value for 2007 is used as a proxy for 2002-2006 to extend the sample. Each column includes a different accounting-based distance
measure. Column (2) shows the result of the baseline regression including the preferred accounting distance and systemic risk
measure (regional PWD and local MES). The following four columns confirm this baseline result when using alternative accounting
distance measures. Column (1) - (5) use local MES as dependent variable, while column (6) uses global MES. Both local and global
MES are forwarded one year to mitigate the impact of reverse causality. All regressions include the following bank specific control
variables: ln(Total Assets), Equity to Total Assets, Liquid Assets to Total Assets, Return on Assets, HHI – Asset Market Share,
Non-Interest Income Share, Growth in Total Assets, Non-Performing Loans ratio, Loans to Total Assets and Total Deposits in Total
Funding, which are all trimmed at the 1% level as local MES to mitigate the effect of outliers. Moreover, all regressions control for
unobserved heterogeneity at the year level by including year fixed effects. The number of countries, regions, continents and banks
in each regression sample are given at the bottom. Differences in the number of banks and countries used in the regressions are due
to the restriction that per area (country, region, and continent) at least two banks should be present to calculate a distance measure.
Per continent, there were always at least two banks present so a distance measure could always be measured. Robust standard errors
corrected for clustering at the regional-level are reported between parentheses. *, ** and *** denote significance at the 10%, 5%
and 1% level.
(1) (2) (3) (4) (5) (6)
VARIABLES Local MES Local MES Local MES Local MES Local MES Global MES
Pair wise distance in a region 1.680 2.670*** 1.429*** (1.026) (0.675) (0.428)
Pair wise distance in a country 1.614** (0.608)
Pair wise distance in a continent 1.805* (0.896)
Distance from average loan portfolio in a
region
1.704***
(0.576)
Constant 2.771*** 3.528** 4.359** 4.080** 4.214** 1.308* (0.421) (1.638) (1.568) (1.576) (1.522) (0.714)
Observations 2,586 2,095 2,063 2,095 2,095 2,113
Adjusted R-squared 0.305 0.395 0.400 0.389 0.392 0.373
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
- 52 -
Table A5
Banking Regulation, Market-based Distance and Systemic Risk – Reduced
sample This table shows the same regression as Table VII, but uses the sample of Table VI in order to check whether the
differences between Table VI and Table VII are sample based or measure based since the accounting-based sample is
smaller than the market-based sample. Column (1) is the baseline regression concerning market-based distance, but while
using the accounting-based sample. Column (2) – (6) add different regulatory measures solely and in an interaction with
MBD_-FT just like in Table VII. The dependent variable, local MES, is forwarded one year to mitigate the impact of
reverse causality. MBD_-FT is constructed using data on 466 banks’ sectoral regression beta for the years 2002-2011. All
regressions include the following bank specific control variables: ln(Total Assets), Equity to Total Assets, Liquid Assets to
Total Assets, Return on Assets, HHI – Asset Market Share, Non-Interest Income Share, Growth in Total Assets, Non-
Performing Loans ratio, Loans to Total Assets and Total Deposits in Total Funding, which are all trimmed at the 1% level,
as local MES and MBD_-FT, to mitigate the effect of outliers. Moreover, all regressions control for unobserved
heterogeneity at the year level by including year fixed effects. The number of countries, regions and banks in each
regression sample are given at the bottom. Differences between countries used in a sample occur due to differences in data
availability of the country-specific regulatory measure. Robust standard errors corrected for clustering at the regional-level
are reported between parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level
(1) (2) (3) (4) (5) (6)
Forwarded Forwarded Forwarded Forwarded Forwarded Forwarded
VARIABLES local MES local MES local MES local MES local MES local MES
Distance excl financial beta and
if t-stat>1 (MBD_-FT) 0.221*** 0.717*** 0.204** 0.620* 0.111 0.168
(0.067) (0.213) (0.091) (0.290) (0.407) (1.816)
Activity Restriction 0.107** (0.049)
Act. Rest.*MBD_-FT -0.058** (0.026)
Fraction Denied 0.083 (0.680)
Frac. Den.*MBD_-FT -0.404 (0.295)
Capital Regulatory 0.086 (0.101)
Cap. Reg.*MBD_-FT -0.048 (0.032)
Supervisory Power 0.046 (0.059)
Sup. Power*MBD_-FT 0.005 (0.037)
External Governance -0.109 (0.153)
Ex. Gov.*MBD_-FT 0.005 (0.116)
Constant 4.934*** 3.127 3.594 3.430 3.235 6.102 (1.527) (1.870) (2.239) (2.200) (2.464) (3.837)
Observations 2,087 1,758 1,169 932 1,229 1,093
Number of Countries in sample 53 53 42 47 47 39
Number of Regions in sample 16 16 14 15 15 12
Number of Banks in sample 298 295 240 211 214 214
R-squared 0.393 0.430 0.427 0.402 0.402 0.408
Control Variables YES YES YES YES YES YES
Year Fixed Effects YES YES YES YES YES YES
Regional Clustered Errors YES YES YES YES YES YES
- 53 -
Appendix 2. Figures
This appendix shows the figures displaying the effects of regulatory measures on the effect of distance on
systemic risk of which the interaction term entered the regression insignificantly. These figures are
constructed using a tool developed by Kristopher J. Preacher and R Development Core Team (2011) as
explained further in (Preacher et al., 2006) and in section 4.3.4. Figure A1 will be displayed in a large size
for clarification. The other figures have the same intuition and are therefore reported smaller.
Figure A1
Effect of Capital Regulatory Index on the Relation Regional PWD versus
Systemic Risk
- 54 -
Figure A2
Effect of Official Supervisory Power:
Regional PWD versus Systemic Risk
Figure A4
Effect of Official Supervisory Power:
MBD versus Systemic Risk
Figure A3
Effect of Fraction of Entry Applicants
Denied: MBD versus Systemic Risk
Figure A5
Effect of External Governance on the
Relation MBD versus Systemic Risk
- 55 -
Appendix 3. Formation of the database
A3.1 Elaborated description of the hand collected database
A hand collected database was used in order to analyze the data. This database was constructed by eight
students by means of taking the annual reports of the banks gathered via Bankscope, for the years 2007
until 2011. In Bankscope we only looked at active and listed banks that are classified as commercial
banks, savings banks, cooperative banks, bank holdings or holding companies, which also reported the
most basic ratios. After these restrictions, 924 banks remained which were sorted on size of which data
was gathered top-down. The goal of the data collection was to obtain a database with sectoral allocation
of the loan portfolios of the banks. Since the research will be based on corporate loans only,
personal/consumer loans, loans to central governments and interbank loans were excluded. Data on the
sectoral allocation was collected by investigating the annual reports of the banks that were retrieved from
Bankscope.
There was no consistency in the way banks reported the sectoral breakdown of their loan portfolio in
the annual reports. Therefore, it was decided to create ten sectors according to the SIC List, to which
eventually the different reported sectors were allocated. After defining the list, there were still many
sectors reported by banks in their reports which were unclear concerning their allocation, hence
occasionally assumptions had to be made. These assumptions were listed in a separate file in order to
ensure consistency among group members. If the allocation of a listed sector to one of the ten sectors was
doubtful, it was discussed during one of the frequent group meetings.
For example, when a bank reports an item that needs to be allocated to two different categories in the
defined list, the amount was split after consensus was reached. More specifically, a sector in the annual
report of the bank could be ‘Agriculture and Mining’. The amount in this sector was then divided over the
two separate sectors in the defined list ‘Agriculture’ and ‘Mining’.
In the data file it was mentioned whether a bank’s information was considered useful. At the end all
the data that was considered unsuitable, was checked again by another group member in order to ensure
the lack of transparency of the bank.
The data on the sectoral allocation was then merged with data from Bankscope, Datastream, Barth
Caprio Levine database, World Development Indicators database, DoingBusiness Database, Financial
Structure database and Heritage Foundation database.
A3.2 Example data gathering
An example will be presented here on how the data is gathered from the annual reports. Chiba Bank Ltd.
is a Japanese bank and the 90th in the list retrieved from Bankscope. First, the annual reports were
downloaded from the company website from 2007 to 2011. In this example, only 2007 will be discussed10
.
10 The annual report can be downloaded at: http://www.chibabank.co.jp/english/pdf/annual_2008/an_08_whole.pdf
- 56 -
At first, the sectoral breakdown of corporate loans was looked for in the annual report. For Chiba
Bank Ltd. the fiscal year ends in March. Therefore, March 2008 refers to fiscal year 2007. The
breakdown for 2007 is found on page 44 (look at the 2008 columns) and is shown below:
First, ‘Other’ is excluded since it obtains mainly consumer loans. Besides that, all sectors added
together do not make up the total amount given in the table (in this case the difference is ¥5). If this was
the case, the sum of all the individual sectors was used as a ‘total amount’ and ‘total’ in the report was
disregarded. The total amount of the relevant sectors for Chiba Bank Ltd. is ¥4,323,528. Based on the list
defined by the group members, the different sectors were allocated. For Chiba Bank Ltd. most of the
sectors are quite straightforward. Some sectors that might bring some doubt are ‘Electricity, Gas, Heat
Supply and Water’, ‘Information and Communications’ and ‘Government and Local Public Sector’.
These sectors are allocated to S4, S4 and S9 respectively (See list below for the definitions of S4 and S9).
In this case ‘Government and Local Public Sector’ probably contains loans to central government (which
should be excluded) and loans to local government (which should be included) but since there is no
composition given and since there is no other sector given that refers to only central government or local
government, the entire sector is included and allocated to S9. The final breakdown and the defined list
based on the SIC codes is displayed below:
- 57 -
List based on SIC Code List Sector Chiba Bank Ltd.
Agriculture, Forestry and Fishing S1 0.17%
Mining & Construction S2 7.90%
Manufacturing S3 15.91%
Transportation, communication, Electric, Gas and Sanitary service S4 5.82%
Wholesale trade and Retail trade S5 14.69%
Finance and Insurance S6 7.31%
Real estate S7 32.03%
Services S8 12.92%
Public administration S9 3.25%
Other industries S10 0%
100 %
Total amount ¥4,323,528
A3.3. Assumptions
Sector encountered in annual
report:
Allocated
to:
(All sorts of) textiles S3
(basic) groceries S5
(Loans to) micro enterprises S10
Academic research, professional and
technical services S8
Accommodation, cafes, restaurants,
food and beverages S8
Administrative and support services S8
Administrative public sector S9
Aerospace/defense/aircraft S3
Agribusiness and vegetable origin S1
Agribusiness capital assets S3
Agriculture and livestock S1
Agriculture and mining, quarrying
50% S1
and S2
Aircraft S3
Airlines S4
Arts, amusement and recreation,
leisure, tourism S8
Asset backed securities/ capital
market/ shares/ margin lending excluded
Asset financing S6
Automobile and autoancillary S3
Auxiliary service for transportation S4
Banking, investment, insurance,
financial services S6
Beverages S3
BTP S2
Building materials S2
Busines services S8
Business groups S8
Capital market intermediaries S6
Cement and cement products S3
Central and local government and
defence S9
Channels and other electronic
products S3
Chemical, rubber, plastics, fertilizers,
pesticides, chemical good production S3
Chemicals and oil
50% S2
and S3
- 58 -
Coal and petroleum products S3
Commercial property S7
Commercial real estate finance S7
Commercial services and supplies S8
Communal, social services S8
Communication (device), information,
information transmission, computer,
software S4
Communication and transportation S4
Community services S8
Construction industry and public
works
50% S2
and S4
Construction of industrial real
estate/commercial real
estate/housing/roads S2
Construction, engineering, and
building products
50% S2
and S3
Consumer discretionary, consumer
staples, consumer goods, cars S5
Consumer products and services
50% S5
and S8
Contingent liabilities excluded
Contractors S2
Corporate S10
Crude petroleum / refining and
petrochemicals
50% S2
and S3
Culture, sports and entertainment S8
Discounted bills S10
Distribution, trade S5
Diversified financials S6
Domestic store name to credit S10
Drugs and pharmaceuticals S3
Durables trade S5
Education, training and other public
services S8
Elderly/child care services S8
Electrical and electronic goods or
components S3
Electricity, gas, steam and hot water
supply S4
Electricity, water, gas, and health
services
50% S4
and S8
Energy and mining
50% S2
and S4
Energy and utilities S4
Engineering & Management Services S8
Entertainment & recreation S8
Equipment rent S8
Estate agents and consultants S7
Finance lease receivables S8
Finance, real estate and other business
services
50% S6
and S7
Financial activities, concerns S6
Financial institutions, investment and
holding companies S6
Financial intermediaries S6
Financial services, insurance and real
estate
50% S6
and S7
Financing, insurance and business
services
50% S6
and S8
FMCG S3
Food production S3
Food, beverages, tabacco S3
Fuel industry S3
Gems&jewellery S3
General commerce S5
Goods leasing S8
Government & municipal S9
Government (only include when
nothing else is reported) S9
Government administration, defense
and mandatory social security S9
Government and quasi-government S9
Government/ central bank / sovereign excluded
Grocery and retail S5
Healthcare, pharmaceuticals,
healthcare equipment, services S8
Hire purchase loans excluded
Holding companies and
conglomerates S10
- 59 -
Home loans/personal lending/
consumer lending/ individuals excluded
Hospital care materials & equipment S3
Hospitality S8
Hotels S8
Household & personal products S5
household goods S3
Housing finance companies S7
Import & export S4
Individual and community services S8
Industrial capital assets S3
Industry (if manufacturing is in the
report as well) S10
Industry (if manufacturing is not in
there) S3
Industry and mining
50% S2
and S3
Infractructure and services
50% S2
and S8
Integrated food services S1
International organization services S8
Internet and multimedia S4
Iron/steel & products S3
IT & electronics S4
IT services and telecommunications S4
Land transport, transport via pipelines S4
Leasing & commercial services S8
Leather and shoes S3
Legal services S8
Lessors of professional offices S8
Light and heavy vehicles S3
Local authorities or public institutions S9
Luxury industry S3
Machinery and instrument S3
Maintenance of machinery and
equipment S8
Management, consulting, advertising S8
Manufacturing and commerce
50% S3
and S5
Manufacturing and processing S3
Materials S3
Mechanical vehicle sale, repair and
service
50% S5
and S8
Mechanical, electrical, electronic, and
manfucturing S3
Media S4
Medical and welfare S8
Medical office space S8
Membership organizations S8
Metals production S3
Mining, quarrying, gravel extraction S2
Miscellaneous manufacturing industry S3
Monolines S6
Motion pictures S8
Multinational financial institutions S6
NBFC/financial intermediaries S6
Non-ferrous metals and products
50% S2
and S3
Non-metallic mineral processing
industries S3
Non-profit organizations excluded
Oil & gas S2
Operations with real estate S7
Opto-electonics S3
Other activities/ miscellaneaous S10
Other community service activities S8
Other domestic activities, other
international activities S10
Other financials S6
Other services S8
Other transforming industries, other
industrial (and commercial) S10
Paper & forestry / mining & basic
materials
50% S1
and S2
Petroleum S3
Post office and telecommunication
service S4
Power industry S4
- 60 -
Printing and Publishing S3
production/manufacture of chemicals S3
production/manufacture of computers
and office equipment S3
production/manufacture of electrical
machinery S3
Production/manufacture of food
products and beverages S3
production/manufacture of furtniture
and other goods S3
production/manufacture of machinery,
equipment and appliances S3
production/manufacture of medical,
precision and optical instruments S3
production/manufacture of metal
finished goods except for machinery
and equipment S3
production/manufacture of mineral-
based products S3
production/manufacture of motor
vehicles, trailers and parts S3
production/manufacture of other
transport equipment S3
production/manufacture of paper and
pulp S3
production/manufacture of rubber and
plastic products S3
production/manufacture of textiles,
clothes and footwear S3
Production/manufacture of transport S3
Professional sports S8
Professional, scientific and technical
services S8
Project Finance S6
Property development, property
investment, property services S7
Public administration, safety, defence,
and social insurance institutions S9
Public authorities S9
Public finance S9
Public management and social
organization S9
Public sector S9
Public services S9
Real estate & construction S7
Real estate and goods rental and
leasing
50% S8
and S7
Real estate development, real estate
investment, real estate services S7
Real estate, business and leasing
services
50% S7
and S8
Refined petroleum, coke and nuclear
products S3
Regional and international
organisations S10
Religious & social organizations S8
Research and development S8
Retail assets, retail finance, retail
lending, retail sales S5
Retailers, catering and
accommodation
50% S5
and S8
Road transport, Railway and other
transportation S4
Road, port, telecom, urban
development & other infrastructure S2
Rural S1
salaried excluded
Sale and repair of motor vehicles S5
Science, education, culture and
sanitation S8
Securitization S6
Semiconductor & equipment S3
Service and other
50% S8
and S10
Services/wholesale & retail
50% S8
and S5
Shipbuilding S3
Solar energy S4
Special trade services S2
Stationary products S3
- 61 -
Steel & metal lurgy S2
Stockbrokers S6
Sugar S3
Sundry industries S3
Taxi medaillon S4
Tea S3
Technological hardware & equipment S3
Textile and garments S5
Trade and sundry services
50% S5
and S8
Trade, restaurant and hotel
50% S5
and S8
Trading, restaurant and hotel
50% S5
and S8
Transport and transport equipment
50% S3
and S4
50%
Transportation and other services
50% S4
and S8
Utilitiesandservices
50% S4
and S8
Various services, coordination of
financial management companies S8
Venture capital funds S6
Water conservancy, environmental
and other public services S4
Water supplying and garbage and
sewage treatment and management S4
Water, environment and public utility
management S4
Wholesale and retail services S5
Wholesale and sundry industries
50% S5
and S3
Wholesale trade and commission trade S5
Wood & cork S1
Wood (processing), furniture, timber,
paper industry S3
- 62 -
Appendix 4 Banks in the sample and their regional/continental classification
This table provides all banks used in this research, their home country and regional/continental allocation,
based on the allocation from the United Nations Statistics Division.
Bank name Country Region Continent
ABSA Group Limited SOUTH AFRICA SouthernAfrica Africa
AXIS Bank Limited INDIA SouthernAsia Asia
Aareal Bank AG GERMANY WesternEurope Europe
Abu Dhabi Commercial Bank UNITED ARAB EMIRATES WesternAsia Asia
Agricultural Bank of China Limited CHINA EasternAsia Asia
Ahli United Bank BSC BAHRAIN WesternAsia Asia
Ahli United Bank KSC KUWAIT WesternAsia Asia
Akbank T.A.S. TURKEY WesternAsia Asia
Aktia Plc FINLAND NorthernEurope Europe
Allied Irish Banks plc IRELAND NorthernEurope Europe
Alpha Bank AE GREECE SouthernEurope Europe
American Express Company UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Aomori Bank Ltd. (The) JAPAN EasternAsia Asia
Arab Bank Plc JORDAN WesternAsia Asia
Arab Banking Corporation BSC BAHRAIN WesternAsia Asia
Arab National Bank SAUDI ARABIA WesternAsia Asia
Asia Commercial Joint-stock Bank-Ngan Hang a
Chau VIETNAM SouthEasternAsia Asia
Associated Banc-Corp. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Astoria Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Attijariwafa Bank MOROCCO NorthernAfrica Africa
Australia and New Zealand Banking Group AUSTRALIA AustraliaNewZealand Oceania
Awa Bank (The) JAPAN EasternAsia Asia
BB&T Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
BBVA Banco Frances SA ARGENTINA SouthAmerica LatinAmerica
BDO Unibank Inc PHILIPPINES SouthEasternAsia Asia
BIMB Holdings Berhad MALAYSIA SouthEasternAsia Asia
BKS Bank AG AUSTRIA WesternEurope Europe
BNP Paribas FRANCE WesternEurope Europe
BOC Hong Kong (Holdings) Ltd HONG KONG EasternAsia Asia
BOK Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
BRD-Groupe Societe Generale SA ROMANIA EasternEurope Europe
BRE Bank SA POLAND EasternEurope Europe
BTA Bank JSC KAZAKHSTAN CentralAsia Asia
Banca Carige SpA ITALY SouthernEurope Europe
Banca Monte dei Paschi di Siena SpA-Gruppo
Monte dei Paschi di Siena ITALY SouthernEurope Europe
- 63 -
Banca Popolare di Milano SCaRL ITALY SouthernEurope Europe
Banca Popolare di Sondrio Societa Cooperativa
per Azioni ITALY SouthernEurope Europe
Banca popolare dell'Emilia Romagna ITALY SouthernEurope Europe
Banca popolare dell'Etruria e del Lazio Soc. coop. ITALY SouthernEurope Europe
Banco BPI SA PORTUGAL SouthernEurope Europe
Banco BTG Pactual SA BRAZIL SouthAmerica LatinAmerica
Banco Bilbao Vizcaya Argentaria Chile CHILE SouthAmerica LatinAmerica
Banco Bilbao Vizcaya Argentaria SA SPAIN SouthernEurope Europe
Banco Comercial Português, SA-Millennium bcp PORTUGAL SouthernEurope Europe
Banco Continental-BBVA Banco Continental PERU SouthAmerica LatinAmerica
Banco Davivienda COLOMBIA SouthAmerica LatinAmerica
Banco Desio - Banco di Desio e della Brianza SpA ITALY SouthernEurope Europe
Banco Espanol de Crédito SA, BANESTO SPAIN SouthernEurope Europe
Banco Espirito Santo SA PORTUGAL SouthernEurope Europe
Banco Industrial e Comercial S.A. - BICBANCO BRAZIL SouthAmerica LatinAmerica
Banco Macro SA ARGENTINA SouthAmerica LatinAmerica
Banco Pichincha C.A. ECUADOR SouthAmerica LatinAmerica
Banco Popolare ITALY SouthernEurope Europe
Banco Popular Espanol SA SPAIN SouthernEurope Europe
Banco Provincial VENEZUELA SouthAmerica LatinAmerica
Banco Santander (Brasil) S.A. BRAZIL SouthAmerica LatinAmerica
Banco Santander Chile CHILE SouthAmerica LatinAmerica
Banco Santander Rio S.A. ARGENTINA SouthAmerica LatinAmerica
Banco Santander SA SPAIN SouthernEurope Europe
Banco de Bogota COLOMBIA SouthAmerica LatinAmerica
Banco de Chile CHILE SouthAmerica LatinAmerica
Banco de Credito del Peru PERU SouthAmerica LatinAmerica
Banco de Credito e Inversiones - BCI CHILE SouthAmerica LatinAmerica
Banco de Galicia y Buenos Aires SA ARGENTINA SouthAmerica LatinAmerica
Banco de Occidente COLOMBIA SouthAmerica LatinAmerica
Banco de Sabadell SA SPAIN SouthernEurope Europe
Banco de Valencia SA SPAIN SouthernEurope Europe
Banco de Venezuela, S.A.C.A. VENEZUELA SouthAmerica LatinAmerica
Banco di Sardegna SpA ITALY SouthernEurope Europe
Banco do Brasil S.A. BRAZIL SouthAmerica LatinAmerica
Bancolombia COLOMBIA SouthAmerica LatinAmerica
Bancorpsouth, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Bangkok Bank Public Company Limited THAILAND SouthEasternAsia Asia
Bank Audi SAL - Audi Saradar Group LEBANON WesternAsia Asia
Bank BPH SA POLAND EasternEurope Europe
Bank Central Asia INDONESIA SouthEasternAsia Asia
Bank Coop AG SWITZERLAND WesternEurope Europe
- 64 -
Bank Danamon Indonesia Tbk INDONESIA SouthEasternAsia Asia
Bank Gospodarki Zywnosciowej SA-Bank BGZ POLAND EasternEurope Europe
Bank Handlowy w Warszawie S.A. POLAND EasternEurope Europe
Bank Hapoalim BM ISRAEL WesternAsia Asia
Bank Internasional Indonesia Tbk INDONESIA SouthEasternAsia Asia
Bank Leumi Le Israel BM ISRAEL WesternAsia Asia
Bank Mandiri (Persero) Tbk INDONESIA SouthEasternAsia Asia
Bank Millennium POLAND EasternEurope Europe
Bank Muscat SAOG OMAN WesternAsia Asia
Bank Negara Indonesia (Persero) - Bank BNI INDONESIA SouthEasternAsia Asia
Bank Pan Indonesia Tbk PT-Panin Bank INDONESIA SouthEasternAsia Asia
Bank Permata Tbk INDONESIA SouthEasternAsia Asia
Bank Polska Kasa Opieki SA-Bank Pekao SA POLAND EasternEurope Europe
Bank Rakyat Indonesia (Persero) Tbk INDONESIA SouthEasternAsia Asia
Bank Saint-Petersburg RUSSIAN FEDERATION EasternEurope Europe
Bank Tabungan Negara (Persero) INDONESIA SouthEasternAsia Asia
Bank UralSib RUSSIAN FEDERATION EasternEurope Europe
Bank Zachodni WBK S.A. POLAND EasternEurope Europe
Bank für Tirol und Vorarlberg AG-BTV (3 Banken
Gruppe) AUSTRIA WesternEurope Europe
Bank of America Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Bank of Ayudhya Public Company Ltd. THAILAND SouthEasternAsia Asia
Bank of Baroda INDIA SouthernAsia Asia
Bank of Beijing Co Ltd CHINA EasternAsia Asia
Bank of Beirut S.A.L. LEBANON WesternAsia Asia
Bank of China Limited CHINA EasternAsia Asia
Bank of Communications Co. Ltd CHINA EasternAsia Asia
Bank of East Asia Ltd HONG KONG EasternAsia Asia
Bank of Hawaii Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Bank of India INDIA SouthernAsia Asia
Bank of Iwate, Ltd JAPAN EasternAsia Asia
Bank of Kyoto JAPAN EasternAsia Asia
Bank of N.T. Butterfield & Son Ltd. (The) BERMUDA NorthernAmerica NorthernCentralAmerica
Bank of Nagoya JAPAN EasternAsia Asia
Bank of Nanjing CHINA EasternAsia Asia
Bank of New York Mellon Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Bank of Ningbo CHINA EasternAsia Asia
Bank of Okinawa JAPAN EasternAsia Asia
Bank of Queensland Limited AUSTRALIA AustraliaNewZealand Oceania
Bank of Saga, Ltd. (The) JAPAN EasternAsia Asia
Bank of The Philippine Islands PHILIPPINES SouthEasternAsia Asia
Bank of Yokohama, Ltd (The) JAPAN EasternAsia Asia
Bank of the Ryukyus Ltd. JAPAN EasternAsia Asia
- 65 -
Bankinter SA SPAIN SouthernEurope Europe
Banque Centrale Populaire MOROCCO NorthernAfrica Africa
Banque Marocaine du Commerce Extérieur-
BMCE Bank MOROCCO NorthernAfrica Africa
Banque Marocaine pour le Commerce et l'Industrie
BMCI MOROCCO NorthernAfrica Africa
Banque Saudi Fransi SAUDI ARABIA WesternAsia Asia
Barclays Plc UNITED KINGDOM NorthernEurope Europe
Bendigo and Adelaide Bank Limited AUSTRALIA AustraliaNewZealand Oceania
Burgan Bank SAK KUWAIT WesternAsia Asia
Byblos Bank S.A.L. LEBANON WesternAsia Asia
Caisse Régionale de Crédit Agricole Mutuel Brie
Picardie-Crédit Agricole Brie Picardie FRANCE WesternEurope Europe
Caisse Régionale de Crédit Agricole Mutuel
Toulouse 31-Crédit Agricole Mutuel Toulouse 31
CCI
FRANCE WesternEurope Europe
Caisse Régionale de crédit agricole mutuel
Atlantique Vendée-Crédit Agricole Atlantique
Vendée
FRANCE WesternEurope Europe
Caisse régionale de Crédit Agricole mutuel du
Morbihan-Crédit Agricole du Morbihan FRANCE WesternEurope Europe
Caisse régionale de credit agricole mutuel Sud
Rhône -Alpes-Credit Agricole Sud Rhône Alpes FRANCE WesternEurope Europe
Caisse régionale de credit agricole mutuel d'Alpes-
Provence-Credit Agricole Alpes Provence FRANCE WesternEurope Europe
Caisse régionale de credit agricole mutuel de la
Touraine et du Poitou-Credit Agricole de la
Touraine et du Poitou
FRANCE WesternEurope Europe
Caisse régionale de crédit agricole mutuel Loire
Haute-Loire-Crédit Agricole Loire Haute-Loire FRANCE WesternEurope Europe
Caisse régionale de crédit agricole mutuel Nord de
France-Crédit Agricole Nord de France FRANCE WesternEurope Europe
Caisse régionale de crédit agricole mutuel de
Normandie-Seine FRANCE WesternEurope Europe
Caisse régionale de crédit agricole mutuel de Paris
et d'Ile-de-France-Crédit Agricole d'Ile-de-France FRANCE WesternEurope Europe
Caisse régionale de crédit agricole mutuel de l'Ille-
et-Vilaine-Crédit Agricole de l'Ille-et-Vilaine FRANCE WesternEurope Europe
Capital One Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Cathay General Bancorp Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Central Bank of India INDIA SouthernAsia Asia
Chang Hwa Commercial Bank Ltd. TAIWAN EasternAsia Asia
Chiba Bank Ltd. JAPAN EasternAsia Asia
Chiba Kogyo Bank JAPAN EasternAsia Asia
China CITIC Bank Corporation Limited CHINA EasternAsia Asia
China Construction Bank Corporation CHINA EasternAsia Asia
China Development Financial Holding Corp TAIWAN EasternAsia Asia
China Everbright Bank Co Ltd CHINA EasternAsia Asia
China Merchants Bank Co Ltd CHINA EasternAsia Asia
China Minsheng Banking Corporation CHINA EasternAsia Asia
Chinatrust Financial Holding Company TAIWAN EasternAsia Asia
Chong Hing Bank Limited HONG KONG EasternAsia Asia
- 66 -
Chugoku Bank, Ltd. (The) JAPAN EasternAsia Asia
Chukyo Bank Ltd JAPAN EasternAsia Asia
Citigroup Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Citizens Republic Bancorp, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
City National Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Comerica Incorporated UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Commerce Bancshares, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Commercial Bank of Dubai P.S.C. UNITED ARAB EMIRATES WesternAsia Asia
Commercial Bank of Qatar (The) QSC QATAR WesternAsia Asia
Commercial International Bank (Egypt) S.A.E. EGYPT NorthernAfrica Africa
Commerzbank AG GERMANY WesternEurope Europe
Commonwealth Bank of Australia AUSTRALIA AustraliaNewZealand Oceania
CorpBanca CHILE SouthAmerica LatinAmerica
Credicorp Ltd. BERMUDA NorthernAmerica NorthernCentralAmerica
Credit Suisse Group AG SWITZERLAND WesternEurope Europe
Credito Bergamasco ITALY SouthernEurope Europe
Credito Emiliano SpA-CREDEM ITALY SouthernEurope Europe
Credito Valtellinese Soc Coop ITALY SouthernEurope Europe
Crédit Agricole S.A. FRANCE WesternEurope Europe
Crédit Industriel et Commercial - CIC FRANCE WesternEurope Europe
Cullen/Frost Bankers, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
DBS Group Holdings Ltd SINGAPORE SouthEasternAsia Asia
Dah Sing Banking Group Limited HONG KONG EasternAsia Asia
Dah Sing Financial Holdings Ltd HONG KONG EasternAsia Asia
Daisan Bank, Ltd. JAPAN EasternAsia Asia
Daishi Bank Ltd (The) JAPAN EasternAsia Asia
Daito Bank JAPAN EasternAsia Asia
Danske Bank A/S DENMARK NorthernEurope Europe
Dena Bank INDIA SouthernAsia Asia
Denizbank A.S. TURKEY WesternAsia Asia
Deutsche Bank AG GERMANY WesternEurope Europe
Deutsche Postbank AG GERMANY WesternEurope Europe
Dexia BELGIUM WesternEurope Europe
DnB ASA NORWAY NorthernEurope Europe
Doha Bank QATAR WesternAsia Asia
E. Sun Financial Holding Co Ltd TAIWAN EasternAsia Asia
EFG International SWITZERLAND WesternEurope Europe
EFG-Hermes Holding Company EGYPT NorthernAfrica Africa
East West Bancorp, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Ecobank Transnational Incorporated TOGO WesternAfrica Africa
Ehime Bank, Ltd. (The) JAPAN EasternAsia Asia
Eighteenth Bank (The) JAPAN EasternAsia Asia
Emirates NBD PJSC UNITED ARAB EMIRATES WesternAsia Asia
- 67 -
EnTie Commercial Bank TAIWAN EasternAsia Asia
Erste Group Bank AG AUSTRIA WesternEurope Europe
Espirito Santo Financial Group S.A. LUXEMBOURG WesternEurope Europe
Eurobank Ergasias SA GREECE SouthernEurope Europe
FNB Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Far Eastern International Bank TAIWAN EasternAsia Asia
Federal Bank Ltd. (The) INDIA SouthernAsia Asia
Fifth Third Bancorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Finansbank A.S. TURKEY WesternAsia Asia
First BanCorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
First Citizens BancShares UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
First Financial Holding Company Limited TAIWAN EasternAsia Asia
First Gulf Bank UNITED ARAB EMIRATES WesternAsia Asia
First Horizon National Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
First International Bank of Israel ISRAEL WesternAsia Asia
First National of Nebraska, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
First Niagara Financial Group, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
FirstCaribbean International Bank Limited BARBADOS Caribbean NorthernCentralAmerica
FirstMerit Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
FirstRand Limited SOUTH AFRICA SouthernAfrica Africa
Flagstar Bancorp Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Fubon Financial Holding Co Ltd TAIWAN EasternAsia Asia
Fukui Bank Ltd. (The) JAPAN EasternAsia Asia
Fukuoka Financial Group Inc JAPAN EasternAsia Asia
Fukushima Bank JAPAN EasternAsia Asia
Fulton Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Getin Holding SA POLAND EasternEurope Europe
Grupo Aval Acciones y Valores S.A. COLOMBIA SouthAmerica LatinAmerica
Grupo Financiero BANORTE MEXICO CentralAmerica NorthernCentralAmerica
Grupo Financiero Galicia SA ARGENTINA SouthAmerica LatinAmerica
Grupo Financiero Inbursa MEXICO CentralAmerica NorthernCentralAmerica
Grupo Financiero Santander, S.A.B. de C.V. MEXICO CentralAmerica NorthernCentralAmerica
Grupo Security CHILE SouthAmerica LatinAmerica
Gulf Bank KSC (The) KUWAIT WesternAsia Asia
Gunma Bank Ltd. (The) JAPAN EasternAsia Asia
HDFC Bank Ltd INDIA SouthernAsia Asia
HSBC Holdings Plc UNITED KINGDOM NorthernEurope Europe
Habib Bank Limited PAKISTAN SouthernAsia Asia
Hachijuni Bank JAPAN EasternAsia Asia
Hancock Holding Company UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Hang Seng Bank Ltd. HONG KONG EasternAsia Asia
Higashi-Nippon Bank JAPAN EasternAsia Asia
Higo Bank (The) JAPAN EasternAsia Asia
- 68 -
Hiroshima Bank Ltd JAPAN EasternAsia Asia
Hokkoku Bank Ltd. (The) JAPAN EasternAsia Asia
Hokuetsu Bank Ltd. (The) JAPAN EasternAsia Asia
Hokuhoku Financial Group Inc. JAPAN EasternAsia Asia
Housing Bank for Trade & Finance (The) JORDAN WesternAsia Asia
Hua Nan Financial Holdings Co Ltd TAIWAN EasternAsia Asia
Hua Xia Bank co., Limited CHINA EasternAsia Asia
Hudson City Bancorp Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Huntington Bancshares Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Hyakugo Bank Ltd. JAPAN EasternAsia Asia
Hyakujushi Bank Ltd. JAPAN EasternAsia Asia
ICICI Bank Limited INDIA SouthernAsia Asia
IDB Holding Corporation Ltd ISRAEL WesternAsia Asia
ING Bank Slaski S.A. - Capital Group POLAND EasternEurope Europe
ING Vysya Bank Ltd INDIA SouthernAsia Asia
Iberiabank Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Indian Overseas Bank INDIA SouthernAsia Asia
Indusind Bank Limited INDIA SouthernAsia Asia
Industrial & Commercial Bank of China (The) -
ICBC CHINA EasternAsia Asia
Industrial Bank of Taiwan TAIWAN EasternAsia Asia
International Bancshares Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Intesa Sanpaolo ITALY SouthernEurope Europe
Investec Limited SOUTH AFRICA SouthernAfrica Africa
Israel Discount Bank LTD ISRAEL WesternAsia Asia
Itau Unibanco Holdings BRAZIL SouthAmerica LatinAmerica
Iyo Bank Ltd JAPAN EasternAsia Asia
JP Morgan Chase & Co. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
JSC Rosbank RUSSIAN FEDERATION EasternEurope Europe
Jammu and Kashmir Bank Ltd INDIA SouthernAsia Asia
Joint Stock Commercial Bank - Bank of Moscow RUSSIAN FEDERATION EasternEurope Europe
Joint Stock Commercial Bank for Foreign Trade of
Vietnam- VIETCOMBANK VIETNAM SouthEasternAsia Asia
Joint-Stock Investment Commercial Bank Novaya
Moskva-NOMOS-Bank RUSSIAN FEDERATION EasternEurope Europe
Joyo Bank Ltd. JAPAN EasternAsia Asia
Jyske Bank A/S (Group) DENMARK NorthernEurope Europe
KBC Groep NV/ KBC Groupe SA-KBC Group BELGIUM WesternEurope Europe
Kagoshima Bank Ltd. (The) JAPAN EasternAsia Asia
Kansai Urban Banking Corporation JAPAN EasternAsia Asia
Kasikornbank Public Company Limited THAILAND SouthEasternAsia Asia
Kazkommertsbank KAZAKHSTAN CentralAsia Asia
Keiyo Bank, Ltd. (The) JAPAN EasternAsia Asia
KeyCorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Kiyo Holdings Inc JAPAN EasternAsia Asia
- 69 -
Komercni Banka CZECH REPUBLIC EasternEurope Europe
Kotak Mahindra Bank Limited INDIA SouthernAsia Asia
Krung Thai Bank Public Company Limited THAILAND SouthEasternAsia Asia
Lloyds Banking Group Plc UNITED KINGDOM NorthernEurope Europe
M&T Bank Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
MB Financial Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
MDM Bank RUSSIAN FEDERATION EasternEurope Europe
MIE Bank Ltd (The) JAPAN EasternAsia Asia
Macquarie Group Ltd AUSTRALIA AustraliaNewZealand Oceania
Mashreqbank UNITED ARAB EMIRATES WesternAsia Asia
Mega Financial Holding Company TAIWAN EasternAsia Asia
Mercantil Servicios Financieros, C.A. VENEZUELA SouthAmerica LatinAmerica
Metlife, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Metropolitan Bank & Trust Company PHILIPPINES SouthEasternAsia Asia
Michinoku Bank, Ltd. (The) JAPAN EasternAsia Asia
Minami-Nippon Bank, Ltd. JAPAN EasternAsia Asia
Minato Bank Ltd JAPAN EasternAsia Asia
Mitsubishi UFJ Financial Group Inc-Kabushiki
Kaisha Mitsubishi UFJ Financial Group JAPAN EasternAsia Asia
Miyazaki Bank JAPAN EasternAsia Asia
Mizrahi Tefahot Bank Ltd. ISRAEL WesternAsia Asia
Mizuho Financial Group JAPAN EasternAsia Asia
Musashino Bank JAPAN EasternAsia Asia
Nagano Bank Ltd. JAPAN EasternAsia Asia
Nanto Bank Ltd. (The) JAPAN EasternAsia Asia
National Australia Bank Limited AUSTRALIA AustraliaNewZealand Oceania
National Bank of Abu Dhabi UNITED ARAB EMIRATES WesternAsia Asia
National Bank of Greece SA GREECE SouthernEurope Europe
National Penn Bancshares, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Natixis FRANCE WesternEurope Europe
Nedbank Group Limited SOUTH AFRICA SouthernAfrica Africa
New York Community Bancorp, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Nishi-Nippon City Bank Ltd (The) JAPAN EasternAsia Asia
Nordea Bank AB (publ) SWEDEN NorthernEurope Europe
Nordea Bank Polska SA POLAND EasternEurope Europe
North Pacific Bank-Hokuyo Bank JAPAN EasternAsia Asia
Northern Trust Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
OJSC Halyk Savings Bank of Kazakhstan KAZAKHSTAN CentralAsia Asia
OTP Bank Plc HUNGARY EasternEurope Europe
Oberbank AG AUSTRIA WesternEurope Europe
Oesterreichische Volksbanken AG AUSTRIA WesternEurope Europe
Ogaki Kyoritsu Bank JAPAN EasternAsia Asia
Old National Bancorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
- 70 -
Oriental Bank of Commerce Ltd. INDIA SouthernAsia Asia
Orix Corporation JAPAN EasternAsia Asia
Oversea-Chinese Banking Corporation Limited
OCBC SINGAPORE SouthEasternAsia Asia
PNC Financial Services Group Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
PT Bank CIMB Niaga Tbk INDONESIA SouthEasternAsia Asia
Paragon Group of Companies Plc UNITED KINGDOM NorthernEurope Europe
People's United Financial, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Piraeus Bank SA GREECE SouthernEurope Europe
Pohjola Bank plc-Pohjola Pankki Oyj FINLAND NorthernEurope Europe
Popular, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Powszechna Kasa Oszczednosci Bank Polski SA -
PKO BP SA POLAND EasternEurope Europe
Privatebancorp, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Privredna Banka Zagreb d.d-Privredna Banka
Zagreb Group CROATIA SouthernEurope Europe
Prosperity Bancshares, Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Prudential Financial Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Qatar National Bank QATAR WesternAsia Asia
Raiffeisen Bank International AG AUSTRIA WesternEurope Europe
Raiffeisenlandesbank Oberösterreich AG AUSTRIA WesternEurope Europe
Regions Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Resona Holdings, Inc JAPAN EasternAsia Asia
Riyad Bank SAUDI ARABIA WesternAsia Asia
SVB Financial Group UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Samba Financial Group SAUDI ARABIA WesternAsia Asia
Saudi British Bank (The) SAUDI ARABIA WesternAsia Asia
Saudi Hollandi Bank SAUDI ARABIA WesternAsia Asia
Saudi Investment Bank (The) SAUDI ARABIA WesternAsia Asia
Sberbank of Russia RUSSIAN FEDERATION EasternEurope Europe
Scotiabank Chile CHILE SouthAmerica LatinAmerica
Scotiabank Peru SAA PERU SouthAmerica LatinAmerica
Shanghai Pudong Development Bank CHINA EasternAsia Asia
Shiga Bank, Ltd (The) JAPAN EasternAsia Asia
Shikoku Bank Ltd. (The) JAPAN EasternAsia Asia
Shimizu Bank Ltd (The) JAPAN EasternAsia Asia
Shin Kong Financial Holding Co.,Ltd TAIWAN EasternAsia Asia
Shinsei Bank Limited JAPAN EasternAsia Asia
Shizuoka Bank JAPAN EasternAsia Asia
Siam Commercial Bank Public Company Limited THAILAND SouthEasternAsia Asia
Signature Bank UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Sinopac Financial Holdings TAIWAN EasternAsia Asia
Skandinaviska Enskilda Banken AB SWEDEN NorthernEurope Europe
Société Générale FRANCE WesternEurope Europe
Spar Nord Bank DENMARK NorthernEurope Europe
- 71 -
SpareBank 1 SMN NORWAY NorthernEurope Europe
SpareBank 1 SR-Bank NORWAY NorthernEurope Europe
Sparebank 1 Nord-Norge NORWAY NorthernEurope Europe
Sparebanken Vest NORWAY NorthernEurope Europe
Standard Bank Group Limited SOUTH AFRICA SouthernAfrica Africa
Standard Chartered Bank (Thai) Public Company
Limited THAILAND SouthEasternAsia Asia
Standard Chartered Plc UNITED KINGDOM NorthernEurope Europe
State Bank of Bikaner and Jaipur INDIA SouthernAsia Asia
State Bank of India INDIA SouthernAsia Asia
State Bank of Mysore INDIA SouthernAsia Asia
State Bank of Travancore INDIA SouthernAsia Asia
State Street Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Sterling Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Sumitomo Mitsui Financial Group, Inc JAPAN EasternAsia Asia
Sumitomo Mitsui Trust Holdings, Inc JAPAN EasternAsia Asia
SunTrust Banks, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Suruga Bank, Ltd. (The) JAPAN EasternAsia Asia
Susquehanna Bancshares, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Svenska Handelsbanken SWEDEN NorthernEurope Europe
Swedbank AB SWEDEN NorthernEurope Europe
Sydbank A/S DENMARK NorthernEurope Europe
Synovus Financial Corp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
TCF Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
TMB Bank Public Company Limited THAILAND SouthEasternAsia Asia
Ta Chong Bank Ltd. TAIWAN EasternAsia Asia
Taichung Commercial Bank TAIWAN EasternAsia Asia
Taiko Bank Ltd JAPAN EasternAsia Asia
Taishin Financial Holding Co., Ltd TAIWAN EasternAsia Asia
Taiwan Business Bank TAIWAN EasternAsia Asia
Thanachart Capital Public Company Limited THAILAND SouthEasternAsia Asia
Toho Bank Ltd. (The) JAPAN EasternAsia Asia
Tohoku Bank JAPAN EasternAsia Asia
Tokyo Tomin Bank, Ltd. (The) JAPAN EasternAsia Asia
Tomato Bank, Ltd JAPAN EasternAsia Asia
Tottori Bank JAPAN EasternAsia Asia
Towa Bank JAPAN EasternAsia Asia
TransCreditBank Group-TransCreditBank RUSSIAN FEDERATION EasternEurope Europe
Trustmark Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Tsukuba Bank Ltd JAPAN EasternAsia Asia
Turk Ekonomi Bankasi A.S. TURKEY WesternAsia Asia
Turkiye Garanti Bankasi A.S. TURKEY WesternAsia Asia
Turkiye Halk Bankasi A.S. TURKEY WesternAsia Asia
- 72 -
Turkiye Vakiflar Bankasi TAO TURKEY WesternAsia Asia
Turkiye is Bankasi A.S. - ISBANK TURKEY WesternAsia Asia
UBS AG SWITZERLAND WesternEurope Europe
UCO Bank INDIA SouthernAsia Asia
UMB Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
US Bancorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Umpqua Holdings Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
UniCredit SpA ITALY SouthernEurope Europe
Union Bank of Israel Ltd ISRAEL WesternAsia Asia
Union Bank of Taiwan TAIWAN EasternAsia Asia
Union National Bank UNITED ARAB EMIRATES WesternAsia Asia
Unione di Banche Italiane Scpa-UBI Banca ITALY SouthernEurope Europe
United Bank Ltd. PAKISTAN SouthernAsia Asia
United Bank of India INDIA SouthernAsia Asia
United Bankshares, Inc. UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
United Overseas Bank Limited UOB SINGAPORE SouthEasternAsia Asia
VTB Bank, an Open Joint-Stock Company (JSC) RUSSIAN FEDERATION EasternEurope Europe
Valiant Holding SWITZERLAND WesternEurope Europe
Valley National Bancorp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Vietnam Export Import Commercial Joint Stock
Bank VIETNAM SouthEasternAsia Asia
Vietnam Joint-Stock Commercial Bank for
Industry and Trade VIETNAM SouthEasternAsia Asia
Vijaya Bank INDIA SouthernAsia Asia
Vontobel Holding AG-Vontobel Group SWITZERLAND WesternEurope Europe
Vseobecna Uverova Banka a.s. SLOVAKIA EasternEurope Europe
Washington Federal Inc UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Webster Financial Corp UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Wells Fargo & Company UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Westpac Banking Corporation AUSTRALIA AustraliaNewZealand Oceania
Wing Hang Bank Ltd HONG KONG EasternAsia Asia
Wintrust Financial Corporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica
Wüstenrot & Württembergische GERMANY WesternEurope Europe
YES BANK Limited INDIA SouthernAsia Asia
Yachiyo Bank JAPAN EasternAsia Asia
Yamagata Bank Ltd. JAPAN EasternAsia Asia
Yamanashi Chuo Bank Ltd (The) JAPAN EasternAsia Asia
Yapi Ve Kredi Bankasi A.S. TURKEY WesternAsia Asia
Yuanta Financial Holding Co Ltd TAIWAN EasternAsia Asia
Zagrebacka Banka dd CROATIA SouthernEurope Europe
Zenith Bank Plc NIGERIA WesternAfrica Africa
Zions Bancorporation UNITED STATES OF AMERICA NorthernAmerica NorthernCentralAmerica