Determinants of systemically important banks the case of Europe

Description
This paper aims to investigate the drivers of systemic risk and contagion among European
banks from 2007 to 2012. The authors explain why some banks are expected to contribute more to
systemic events in the European financial system than others by analysing the tail co-movement of
banks’ security prices.

Journal of Financial Economic Policy
Determinants of systemically important banks: the case of Europe
J acob Kleinow Tobias Nell
Article information:
To cite this document:
J acob Kleinow Tobias Nell , (2015),"Determinants of systemically important banks: the case of
Europe", J ournal of Financial Economic Policy, Vol. 7 Iss 4 pp. 446 - 476
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Determinants of systemically
important banks: the case
of Europe
Jacob Kleinow
Department of Finance, Freiberg University, Freiberg, Germany, and
Tobias Nell
Department of Accounting, Freiberg University, Freiberg, Germany
Abstract
Purpose – This paper aims to investigate the drivers of systemic risk and contagion among European
banks from 2007 to 2012. The authors explain why some banks are expected to contribute more to
systemic events in the European fnancial system than others by analysing the tail co-movement of
banks’ security prices.
Design/methodology/approach – First, the authors derive a systemic risk measure from the
concepts of marginal expected shortfall and conditional value at risk analysing tail co-movements of
daily bank stock returns. The authors then run panel regressions for the systemic risk measure using
idiosyncratic bank characteristics and a set of country and policy control variables.
Findings – The results comprise highly signifcant drivers of systemic risk in the European banking
sector with important implications for research and banking regulation. Using a set of panel
regressions, the authors identify bank size, asset and income structure, loss and liquidity coverage,
proftability and several macroeconomic conditions as drivers of systemic risk.
Research limitations/implications – Analysing the tail co-movement of security prices excludes a
number of “smaller” institutions without publicly listed securities. The other shortfall is that we do not
assess the systemic impact of non-bank fnancial institutions.
Practical implications – Regulators have to consider a broad variety of indicators for assessing
systemic risks. Existing microprudential-oriented rules are less effective, and policymakers may
consider new measures like asset diversifcation to mitigate systemic risks in the banking system.
Originality/value – The authors contribute to existing empirical analyses in three ways. First, they
propose a method to identify systemically important banks (SIBs). Second, they develop two measures
to assess their potential negative impact on the system. Third, they contribute to the closing of the
research gaps by analysing which macroprudential regulations for SIBs are most effective without
hampering free market forces.
Keywords Banks, Economics of regulation
Paper type Research paper
JEL classifcation – G01, G21, G28
This paper was presented at the 2015 Midwest Finance Association Annual Meeting, 2015
Southwestern Finance Association Annual Meeting, 2015 Eastern Finance Association Annual
Meeting. The authors thank Silvia Rogler, Andreas Horsch and the reviewer for their useful
remarks.
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
7,4
446
Received16 July2015
Revised16 July2015
Accepted12 August 2015
Journal of Financial Economic
Policy
Vol. 7 No. 4, 2015
pp. 446-476
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-07-2015-0042
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Introduction
Which factors determine the systemic importance of European banks? With the most
recent fnancial crises, interest in the concept of systemic risk has grown. In this paper,
we investigate the drivers of systemic risk[1] in the European fnancial sector as well as
contagion[2] among banks. To this end, we propose a novel measure of systemic risk –
the systemic risk index (SRI) – to capture the impact a single fnancial institution has on
the fnancial sector and vice versa. We then test how panel regressions with bank
characteristics and country variables explain systemic risk. The topic of our paper is of
considerable interest for regulators and economists because our results offer new
insights into the drivers of fnancial instability and provide implications for the
macroprudential regulation of banks.
Financial systems as a whole tend towards instability. This is due to the fragile
nature of their players, especially banks. Because of their role as a fnancial intermediary
(or delegated monitor), their opaqueness, their interconnectedness and the typical
characteristics of their lenders, banks are particularly prone to infecting other banks
with fnancial distress – or to being infected by them. This in particular holds for those
banks that almost certainly and rather quickly could destabilise the system as a whole:
the so-called systemically important fnancial institutions (SIFIs). Consequently, the
identifcation of drivers of systemically important banks (SIBs) is of vital importance.
Recent papers on systemic risk of banks produced substantial fndings. Existing
literature in this feld, however, is comparably young and leaves questions unanswered:
• It is unclear how to identify SIBs.
• There is no consensus on how to measure their potential negative impact on the
system.
• It is unknown which macroprudential regulations for SIBs are most effective
without hampering free market forces.
We contribute to the closing of the research gaps by using innovative key indicators for
systemic risk and running panel regressions with bank characteristics and country
variables to identify drivers of systemic risk. The remainder of this paper is organised as
follows.
Section 1 offers a reviewof related literature on systemic risk (of European banks) as
our background and starting point. Section 2 develops our measure of systemic risk –
the systemic risk index (SRI). The subsequent Section 3 explains our sample selection
and explanatory variables for systemic risk. The presentation of our results follows in
Section 4, while Section 5 concludes our fndings.
1. Related literature
In this section, we briefy discuss the related theoretical and empirical literature on
drivers of systemic risk in the European banking sector.
The frst step for the identifcation of drivers of systemic risk is the assessment of
systemic risk levels. The number of measures for systemic risk has grown rapidly in
recent years[3]. The literature can be divided into the streams of:
• Systemic risk contribution; and
• Systemic risk sensitivity (Prokopczuk, 2009), as illustrated in Figure 1.
447
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Approaches for systemic risk contribution try to determine systemic importance by
measuring a single institution’s contribution to systemic risk. Those measures assess
how one institution affects others. According to this understanding, it is of particular
interest to avoid and mitigate contagion effects. Conversely, designed measures dealing
with the systemic risk sensitivity try to determine systemic importance by measuring the
extent to which a single institution is affected in the case of a systemic event. The overall
functioning of the fnancial system and individual institutional resilience is the focus of
this approach. Table I summarises a selection of popular systemic risk measures for
fnancial institutions from both streams. Considering both approaches, we combine a
measure for systemic riskcontribution(relatedto ?CoVar byAdrianandBrunnermeier,
2011) and a measure for the systemic risk sensitivity (related to marginal expected
shortfall [MES] by Acharya et al., 2011) based on banks’ stock returns as presented in
Section 2.
The second step for the identifcation of drivers of systemic risk is to run panel
regression analyses with different potential factors from the micro- or macro-level that
may affect systemic risk. Previous papers arrive at the following fndings: applying the
?CoVar (systemic risk contribution) approach, Bori et al. (2012) fnd that market-based
variables are strong predictors for systemic risk in Europe. Their results show that
institutional factors like size and leverage contribute signifcantly to banks’ systemic
risk. Furthermore, the concentration of the banking system increases systemic risk.
Following a ?CoVar-related approach, Hautsch et al. (2014) fnd that unlike leverage
and funding risk (measured by maturity mismatch), size is not a dominant factor among
European banks.
Based on the SRISK measure (systemic risk sensitivity approach), Engle et al. (2012)
fnd that banks account for approximately 80 per cent of the systemic risk in Europe,
with UK and French institutions bearing the highest levels of systemic risk. With an
enhanced version of the MES and hand-collected data of European banks, Acharya and
Steffen (2014) fnd that banks’ sovereign debt holdings are major contributors to
systemic risk. Based on contingent claims analysis, Vallascas and Keasey (2012) fnd
several key drivers of systemic risk of European banks like high leverage, lowliquidity,
Individual shock
Systemic shock
Bank
A
Bank
B
…

Bank
C
…

Bank
A
Bank
B
…

Systemic risk contribution
Systemic risk sensitivity
Note: This figure illustrates the two different contagion channels
of systemic risk
Figure 1.
Systemic risk
contribution and
sensitivity
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size and high non-interest income. Using a comparable methodology, Varotto and Zhao
(2014) confrm the positive impact of size and leverage on systemic risk for a set of
European banks. Black et al. (2013) propose a sensitivity-related risk measure for
systemic risk, and fnd signifcant correlations with accounting- and market-based
bank-specifc measures of European banks: they confrm that systemic risk increases
with bank size. Interestingly, they also fnd that European banks with a more traditional
lending business and more liquid assets are less likely to increase systemic risk. Finally,
they fnd that bank proftability has no impact on systemic risk, and that the
market-to-book ratio has an infuence on banks’ systemic risk in Europe that can be
either positive or negative.
To measure systemic risk in the European banking system, we combine the
contribution and sensitivity approach used by recent literature (Guerra et al., 2013;
Bongini and Nieri, 2014) and propose a new risk measure, the systemic risk index (SRI),
to capture both equally. From the point of view of the method of frst measuring the
systemic risk of a bank, our paper is most closely related to Adrian and Brunnermeier
Table I.
Systemic risk
sensitivity and
systemic risk
contribution:
measures of systemic
risk
Systemic risk contribution
How does a single institution contribute to systemic risk?
?CoVar Captures the marginal contribution of a particular institution (in a
non-causal sense) to the overall systemic risk by applying
quantile regressions (Adrian and Brunnermeier, 2011)
Co-risk Analyses the tails of the default distributions for pairs of
institutions, or ?to put it simply ?it analyses how the default
risk of an institution affects the default risk of another institution
(Chan-Lau, 2010)
Granger causality Measures the directionality of relationships or causality of price
movements of securities issued by fnancial institutions (Billio
et al., 2012)
Principal component analysis
(PCA)
Is a technique to decompose asset returns of a sample of fnancial
institutions into linkages between those institutions (León and
Murcia, 2013)
Systemic risk sensitivity
To what extent is a single institution affected by systemic risk?
Marginal expected shortfall
(MES)
Determines the level of systemic risk by measuring an
institution’s losses (in terms of negative index returns) when the
(fnancial) system as a whole is doing poorly (Acharya et al., 2011)
SRISK Is an index formed by the leverage, size and the MES of a frm
(Brownlees and Engle, 2012)
Lower tail dependence (LTD) Is a measure of the propensity of a single fnancial institution to
experience joint extreme adverse effects (measured in price
returns) with the market (Weiß et al., 2014)
Contingent claims analysis
(CCA)
Measures systemic solvency risk based on market-implied
expected losses of fnancial institutions by generating aggregate
estimates of the joint default risk of multiple institutions as a
conditional tail expectation (Jobst and Gray, 2013)
Note: This table summarises a selection of common systemic risk measures for fnancial institutions
from the systemic risk sensitivity and systemic risk contribution streams
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(2011) and Acharya et al. (2011). Second, to analyse determinants on systemic risks, we
make use of the approaches elaborated by Acharya and Steffen (2014) and Weiß et al.
(2014).
2. Systemic risk index – measuring system risk
When the banking system is in distress, losses and liquidity shortages spread from one
bank to others, fnally affecting the systemas a whole (Hauptmann and Zagst, 2011). To
analyse the role of a single bank in closely knit and, thus, contagious networks, Adrian
and Brunnermeier (2011) propose the CoVaR, which is the value at risk (VaR) of the
banking system conditional on an institution being in distress. The CoVaR follows the
systemic risk contribution approach: It is meant to capture the bank-specifc potential for
spreading fnancial distress froma single institution i across the banking systemSys by
gauging the tail co-movement of the fnancial system with the institution’s stock
(Adrian and Brunnermeier, 2011). The CoVaR, however, does not satisfactorily capture
the tail co-movement of the banking systemand a single bank, since it ignores observed
values within the tail[4]. Conceptually, we follow the CoVaR approach to a large extent,
but avoid its shortcomings by proposing the measure SRC – the systemic risk
contribution – which considers the co-movement of the banking system returns and
individual bank returns within tails. Recall that VaR
q
i
– the value at risk of an
institution’s stock with the return r
i
– is implicitly defned as the q-quantile:
P(r
i
?VaR
q
i
) ?q. It measures the minimum return r
i
of an institution’s stocks within
the 1 ?q% confdence interval within a certain period of time (usually one year).
By the systemic risk contribution – SRC
q
i
– we denote the average return of a banking
system relative to an institution return i conditional on the institution’s return r
i
being
below its value at risk (VaR
q
i
):
SRC
q
i
: ? E
?
r
Sys
r
i
?
r
i
? VaR
q
i
?
? E
?
r
Sys
r
i
?
r
i
q?5%
?
, (1)
with r
Sys
denoting the return of the banking system[5].
Generally defned, the SRC
q
i
measures the reaction of the banking system at the q%
worst days of a certain bank’s stocks within one year[6]. In other words, an SRC
5%
i
of 0.5
would mean that the average return of the banking system r
Sys
would be positively
associated with a coeffcient of 0.5 with an institution’s stock returns r
i
, when the
respective institution’s losses exceeds their VaR limit. To put it simply: when the
institution’s stocks decline by, for example, 6 per cent on average during the worst 5 per
cent of days within one year, we expect the banking system’s stocks to decline by 3 per
cent on those days.
The SRS (systemic risk sensitivity) measure follows the systemic risk sensitivity
approach: It captures a single institution i’s return when the banking system is in
distress. The SRS we propose is very closely related to the marginal expected shortfall
(MES) employed by Acharya et al. (2011). Instead of measuring absolute values, we put
the institution’s losses in relation to the banking system’s losses. Finally, as we use
average values, we improve the explanatory power of the MES by better capturing the
tail co-movement of a single institution and the banking system. Analogously to SRC
q
i
,
we denote by systemic risk sensitivity, SRS
q
i
, the average return of a bank i relative to a
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banking system return conditional on the banking system’s return r
Sys
falling below its
value at risk (VaR
q
Sys
):
SRS
q
i
: ? E
?
r
i
r
Sys
?
r
Sys
? VaR
q
Sys
?
? E
?
r
i
r
Sys
?
r
Sys
q?5%
?
(2)
An SRS
5%
i
of 0.5 would mean that the respective institution’s mean stock return r
i
would
be positively associated with the banking system’s return r
Sys
, with a coeffcient of 0.5
when the banking system’s losses exceed their VaR limit. In other words, when the
banking system’s stocks decline by, for example, 6 per cent on average during the worst
5 per cent of days within one year, we would expect the institution’s stocks to decline by
3 per cent on those days.
At the fnal stage, we average the SRC
q
i
and SRS
q
i
to obtain our systemic risk index
SRI
q
i
for fnancial institutions, which considers both directions of risk transmission and
contagion equally[7]:
SRI
q
i
? ?
SRC
q
i
? SRS
q
i
2
(3)
For the remainder of the paper, for q, we will use the 5 per cent quantile and simplify the
notation to SRI
i
. The systemic risk index SRI
i
is a good convention for approximating the
practical requirements of regulators and theoretical models on the systemic importance
of fnancial institutions. It demonstrates both how a single bank affects the fnancial
system and how it can be affected by that system. Furthermore, SRI is based on
well-known statistical measures of risk, and the results – expressed in natural units –
allow for an interpretation from an economic point of view.
3. Sample selection, bank and macro-data as explanatory variables
3.1 Sample selection
We start by selecting a representative sample of European banks. To obtain a testable
sample of systemically relevant banks in the European Union (EU), we use the 2014
European Banking Authority (EBA) EU-wide stress-test sample of banks (European
Banking Authority, 2014), as it includes quantitative and qualitative selection criteria.
The bank selection is based on asset value, importance for the economy of the country,
scale of cross-border activities and whether the bank requested/received public fnancial
assistance[8]. This initial EBA sample contains 123 banks/bank holdings from
22 countries[9]. To calculate our systemic risk proxy, we collect share-price data of
the publicly listed banks from the EBA sample from Thomson Reuters Financial
Datastream. However, for a variety of reasons, many European banks are not publicly
listed, or are listed but not traded in actuality. Hence, their stocks exhibit constant prices
over long periods and trading volumes slightly above zero. After excluding those bank
shares with more than 25 per cent zero daily returns for the analysed periods,
approximately 60 banks remain for each observed year. Lacking or inconsistent
accounting data necessitate the exclusion of a further number of banks[10], so that we
fnally produce a full sample (unbalanced panel) of 334 bank observations for the period
from2007 to 2012 (Table II). Our sample includes 14 of the 24 European banks that failed
the EBA stress test at the end of October 2014 (European Banking Authority, 2014 and
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Table AVI). The full bank sample as a balanced panel with 294 observations is used for
robustness checks (Table AIII).
To demonstrate that the value at risk is not equal to our systemic risk index, we plot
VaR
i
(with reverse signs) against the SRI
i
for our sample of European banks (Figure 2):
it illustrates that there is only a very weak link between the individual (idiosyncratic)
risk of the institutions we analyse, measured by VaR
i
(abscissa), and the institutions’
systemic risk, measured by SRI
i
(ordinate). There is only a weak, statistically
insignifcant correlation between the VaR
i
and SRI
i
of ?0.0322 (p-value ?0.583). The
observed SRI
i
is also more stable since its mean coeffcient of variation (0.201) is lower
than in the case of the VaR
i
(0.380). Finally, the issue of choosing an appropriate index
for calculating our SRI measure is not trivial (Benoit, 2014). As our aim is to estimate
systemic risk in the European fnancial sector, we use the MSCI Europe Financials
Index. This value-weighted equity index captures around 100 large and mid-cap entities
across 15 countries in Europe from the banking, fnancial services, insurance and real
estate sectors[11]. We provide a robustness check for our results using the EU
Datastream Banks Index (Table AIV).
3.2 Bank characteristics as determinants of systemic risk
The purpose of our study is to identify sources for systemic risk of banks in Europe.
With this paper, we investigate the extent to which panel regressions could explain why
Table II.
Bank sample
distribution
Year 2007 2008 2009 2010 2011 2012 Total
Banks 56 58 56 57 54 53 334
Notes: The table presents the distribution of European banks we analyse in our sample; the
distribution per country is reported in Table AV. Table AVI provides the names of all banks included
in the sample
–0.5
0
0.5
1
1.5
0 5 10 15 20
S
R
I
VaR of banks' stock returns (%)
Note: The figure presents a comparison of the value at risk (VaR) and the
systemic risk index (SRI) for the sample of European banks
Figure 2.
VaR of banks’ stock
returns (in per cent)
and SRIs (2007-2012)
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some banks have a higher infuence on fnancial market stability than others[12]. With
this objective in mind, we collect a dataset on idiosyncratic bank characteristics as well
as information concerning countries’ regulatory environments and macroeconomic
conditions. The data on banks’ cash fows, balance sheets and proft or loss statements
are obtained from Thomson Reuters Worldscope (for a full variable defnition, see
Table AI). Where available, we fll data gaps manually with data from banks’ websites.
Our frst explanatory variable is SIZE, which is defned as the decimal logarithm of
a bank’s total assets. Large banks may be better diversifed and carry less individual
(idiosyncratic) risk. However, large banks are more closely connected to and within the
fnancial system through interbank liabilities and other exposures to the fnancial
system, making them particularly hard to replace (Basel Committee on Banking
Supervision, 2013)[13]. Additionally, banks deemed “too big to fail” are thought to
receive implicit state guarantees, so that subsequent bailout expectations increase the
risk appetite of banks enjoying this governmental support, as protected actors feel less
incentivised to apply market discipline (Gropp et al., 2014; Kleinow and Horsch, 2014).
Therefore, we expect bank size to have a positive infuence on systemic risk.
To describe the type of business a bank is mainly engaged in on the asset side and the
level of revenue diversifcation, we obtain data on banks’ share of total loans to total
assets (LOAN) – loan ratio – and the share of non-interest income to total income
(NON_INT). Although employing different approaches, both are indicators for the
banks’ dependency on – riskier – non-commercial-banking activities such as investment
banking or trading. In contrast, it is also argued and empirically supported in the
literature that low ratios of total loans to total assets and relatively high non-interest
incomes are an indicator of innovative business models, better diversifcation and, as a
consequence, lower systemic risk exposures (see e.g. Laeven and Levine, 2009; Demsetz
et al., 1997; Morgan and Stiroh, 2005). However, for the case of small banks in countries
with more private/asymmetric information, De Jonghe et al. (2014) showthat the “bright
side of innovation” disappears – a situation that is less likely in Europe. Consequently,
from the literature, we cannot derive a clear hypothesis of the impact of LOAN and
NON_INT on systemic risk. To control for the infuence of a bank’s loan portfolio
quality (credit risk), we use NON_PERF – the share of loan loss provisions to the total
book value of loans – as an explanatory variable in our regression. We assume that
NON_PERF captures the risk level of a bank’s loan portfolio, and we expect banks with
riskier loan portfolios to affect the fnancial system more negatively than others.
To measure the infuence of banks’ capital structure, we include LEVERAGE and
DEPOSIT in our regression. For LEVERAGE, i.e. the ratio of debt to equity, we expect
a clear positive relationship with the systemic risk a bank poses on the fnancial system
because higher leverage means higher default risks due to a smaller cushion that could
absorb losses, as well as a higher ratio of fxed expenses. As a proxy for the banks’
liability portfolio and business type, we utilise DEPOSIT, i.e. the ratio of total deposits
to total liabilities. Traditional commercial banks with a focus on non-securitised savings
and loan business usually have high deposit ratios. In particular, banks with high
deposit ratios are fnanced less via securities or by the capital market in general.
Therefore, they are less connected to other banks or other institutional investors. For
these reasons, we expect DEPOSITto have a negative infuence on banks’ systemic risk.
Afurther variable we use is the regulatory measure TIER1 ratio (or Basel core capital
ratio), which is the ratio of core equity capital to total risk-weighted assets, measuring
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the capacity of loss absorption. According to bank regulators, a high TIER1 ratio
indicates that the bank is in a solid state. In this scenario, we would expect a negative
impact on a bank’s systemic risk. On the contrary, banks that are forced to have a higher
regulatory coverage ratio may also be incentivised to take even more risk because they
do not internalise the negative realisations of tail risk projects (Perotti et al., 2011).
Another bank-specifc variable we consider for our panel regression is LIQUIDITY
(the ratio of cash and tradable securities to total deposits). A large portion of cash and
security reserves is probably advantageous at times of negative shocks in the fnancial
system, when interbank markets easily dry out and liquidity becomes scarce
(Brunnermeier, 2009). According to this account, LIQUIDITY is expected to decrease
systemic risk. FIN_POW, the ratio of net cash fow of operating activities to total
liabilities, is also a proxy for liquidity risks and indicates the time banks need to settle
their total liabilities with their operating cash fow. Similarly to LIQUIDITY, we expect
a negative infuence on systemic risk.
Next, we control for the infuence of banks’ proftability on systemic risk by
employing the operating proft margin – OP_MARG – (the ratio of operating income to
net sales) and the rather capital-oriented return on invested capital (ROIC). In principle,
as Weiß et al. (2014) argue, both measures could be coincident with stability or risk: high
values of OP_MARG or ROIC could shield from the risk of defaulting, so that those
banks could be a pillar of stability. Higher proftability, on the other hand, could also be
the result of extended yet successful engagement in risky lending/non-lending activities,
which may suddenly cause or contribute to the bank’s – as well as general systemic –
instability. Therefore, we expect an undirected effect on systemic risk. The same is true
for INCOME, the annual growth of income (mainly consisting of interest and fees – for
a full variable defnition, see Table AI). We consider it a good proxy for bank activity
growth, as it is comparatively less vulnerable to accounting manipulations.
Next, we employ the ratio of the market capitalisation to the book value of the bank’s
common equity: market-to-book ratio – MBR. A high MBR can be an indicator of
disproportionately high expectations for earnings prospects on the side of investors.
High earning prospects are normally associated with higher risks. In most cases, this
development is intensifed by bank managers (“empire building”), as they are
incentivised towards excessive risk taking to increase frm value to form a “glamour
bank”, as Weiß et al. (2014) argue. Following a different line of thought, Demsetz et al.
(1996) argue that a high market-to-book ratio helps to reduce excessive risk taking
because banks have a great deal to lose if a risky business strategy leads to insolvency.
However, this may not or may less strongly apply to banks deemed “too big to fail” due
to their increased appetite for risk. Overall, we expect the MBR ratio to affect systemic
risk, even though the direction is indecisive. The last bank-specifc variable we consider
is the banks’ long-term rating (LTR). Owing to data gaps in bank rating histories, we
frst collect the long-term ratings from Moody’s and fll missing values with
hand-collected rating data from S&P’s and Fitch. We use an 18-notch rating scale. For
numerical reasons, values from0 to 1 in steps of 1/18 are assigned, with 0 denoting AAA
(the highest rating) and 1 denoting D(default). The mean LTRvalue of 0.276 in Table III,
therefore, indicates the relatively high mean rating of Afor the banks in the sample. We
expect (the assessment of) individual banks’ default probability to have an increasing
effect on systemic risk, and so LTR should positively affect systemic risk.
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3.3 Country controls as determinants of systemic risk
To control for the impact of different macroeconomic conditions and regulations among
the EU jurisdictions, we include another fve country-related variables. Differences in
(capital) regulation are of special interest because stricter regulations and powerful
supervisors could limit systemic risks. The data we use are provided by the World Bank
or Eurostat databases (Table AI provides detailed defnitions and data sources).
The frst index we employ from the World Bank Worldwide Governance Indicators
database is political stability (POLITIC_STAB): it is designed as an indicator of the
likelihood that a government will be destabilised or overthrown by unconstitutional or
violent means (e.g. political violence or terrorism). We expect high instability to increase
banks’ (systemic) risk, as demonstrated by Uhde and Heimeshoff (2009). The second
index from the World Bank is regulatory quality (REGULATION): it captures the
ability of the government to formulate and implement sound policies and regulations
that permit and promote private system development. We expect REGULATION to
decrease systemic risks of European banks.
Furthermore, we analyse how the concentration of the banking industry affects the
stability of the fnancial system (CONCENTRATION: the sum of assets of the three
largest national commercial banks as a share of total commercial banking assets). Prior
research disagrees regarding the infuence of concentration on the stability of a banking
system. To extend this argumentation, Blundell-Wignall et al. (2011) and Carletti and
Hartmann (2002) fnd that the trade-off between banking concentration and stability
does not generally hold. In this case, we would expect high banking concentration to
increase stability. However, there are also theoretical justifcations and relevant
Table III.
Summary statistics
for bank
characteristics
Variable
Expected
infuence Symbol Observation Mean Median SD Minimum Maximum
Size ? SIZE 334 11.160 11.094 0.680 9.530 12.483
Loan ratio ?/? LOAN 334 0.640 0.670 0.152 0.110 0.909
Non-interest income
ratio ?/? NON_INT 334 0.452 0.381 0.351 ?0.157 2.807
Non-performing loan
ratio ? NON_PERF 334 0.010 0.007 0.011 ?0.006 0.100
Leverage ratio ? LEVERAGE 334 6.922 6.635 16.797 ?231.857 99.737
Deposit ratio ? DEPOSIT 334 0.459 0.444 0.184 0.038 0.964
Tier 1 ratio ?/? TIER1 334 0.102 0.100 0.031 ?0.073 0.189
Liquidity ratio ? LIQUIDITY 334 0.969 0.693 0.952 0.048 9.077
Financial power ? FIN_POW 334 0.064 0.043 0.073 0.002 0.545
Operating margin ?/? OP_MARG 334 0.064 0.092 0.203 ?1.670 0.440
Return on invested
capital ?/? ROIC 334 0.025 0.023 0.048 ?0.294 0.354
Income growth ?/? INCOME 334 0.186 0.016 2.761 ?0.798 50.221
Market-to-book ratio ?/? MBR 334 0.934 0.780 0.698 ?2.350 4.410
Long-term rating ? LTR 334 0.276 0.222 0.179 1.000 0.889
Notes: The table presents descriptive statistics for bank-specifc fnancial data (from balance sheets
and proft or loss statements) used in the panel regressions. Bank-specifc data are taken from the
databases Thomson Worldscope and Thomson Reuters Financial Datastream. Further variable
defnitions and data sources are provided in Table AI
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empirical indications that defend the opposing view of fragility increasing with
concentration, such as Beck et al. (2013). Supporting this theory, Kleinow et al. (2014)
argue that this appears particularly plausible for SIFIs that have incentives to increase
risk taking. Therefore, we hypothesise that CONCENTRATION has the effect of
increasing systemic risk.
To control for the country’s indebtedness we use the government debt ratio (DEBT),
which is the government gross debt in relation to the respective gross domestic product
(GDP). Policymakers in countries with high levels of debt have lower chances to bail out
banks, as fnancial resources are scarce. We, therefore, expect high government debt
ratio levels to increase domestic banks’ systemic risk. Finally, to capture the infuence of
inter-relations between a country and its domestic banking sector, we use the claims of
the institutions on their respective central government (as a percentage of GDP) as
another variable (BANK_CL). If the domestic banking sector holds a relatively high
share of its government’s public debt, this should increase the systemic risk of banks in
the fnancial system. Table IV provides univariate statistics for all country and
regulatory controls of our panel regressions. Further information on the evolution of all
variables is reported in Table AV.
4. Results
In this section, we frst present the results for the estimates of banks’ systemic risk and
then turn to the panel regressions of the dependent systemic risk measure for our sample
of 334 bank observations during 2007 and 2012.
4.1 Systemic risk of European banks
We frst compute the SRI for all banks in the sample. The distribution results (Table V)
demonstrate that, possibly due to monetary and supervisory interventions, median
values of SRC spreading fromone bank to the fnancial systemwere highest in 2008 but
declined till 2012 (with the exception of 2010). The SRC shows that European banks are
becoming less “infuential”. The other side of the coin, however, indicates an increasing
SRS between 2007 and 2012 (with the exceptions of 2009 and 2011). This shows that
contagiosity between banks is increasing, possibly due to the increasing alertness of
banks’ stockholders.
The statistics on SRI demonstrate that the highest systemic risk levels can be
observed for the period during the Eurozone crisis from 2010 to 2012. Looking at the
standard deviation and the minimum/maximum values, we also fnd evidence that
Table IV.
Summary statistics
for country controls
Variable
Expected
infuence Symbol Observation Mean Median SD Minimum Maximum
Political stability ? POLITIC_STAB 334 0.620 0.631 0.438 ?0.466 1.495
Regulatory quality ? REGULATION 334 1.262 1.213 0.402 0.498 1.924
Bank concentration ? CONCENTRATION 334 0.705 0.712 0.140 0.422 0.999
Government debt
ratio ? DEBT 334 0.799 0.746 0.323 0.249 1.703
Bank claim ratio ? BANK_CL 334 0.177 0.181 0.118 ?0.129 0.431
Notes: The table presents descriptive statistics for country-specifc data used in the panel regressions; data are taken from
the World Bank or Eurostat database. Further variable defnitions and data sources are provided in Table AI
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Table V.
Summary statistics
for the systemic risk
of European banks
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there is an increasing inequality among banks with respect to systemic risk, i.e. some are
becoming less systemically relevant, whereas others are becoming more systemically
relevant. We provide the names of the top fve banks in the sample in the systemic risk
ranking for each year in Table AVII.
4.2 Determinants of systemic risk
Turning to our main research question, we try to identify the drivers of systemic risk for
our sample of European banks. To this end, we estimate several linear panel regression
models using SRI and its components SRC and SRS as the dependent variables as well
as our bank-specifc and country-/policy-specifc explanatory variables: Table VI
presents the results of our main regressions for the full period of 334 bank observations,
while results of numerous robustness checks and panel data tests/diagnostics are
reported in the Appendix 1.
The random effects estimator is used to account for time-invariant bank-specifc
infuences, and guarantees consistent coeffcient estimates. However, the Hausmann
(1978) specifcation test indicates that the random effects estimator is only
consistent for the baseline regression (Table AII). Therefore, we use the fxed effects
estimator model for the other panel regressions. The assumption behind the fxed
effects model is that, unlike the random effects model, variation across entities is
neither random nor uncorrelated with the predictor or independent variables
included in the model. All estimation results of the linear panel regression models
are based on heteroscedasticity-consistent White (1980) standard errors because
unreported results confrm the presence of heteroscedasticity in our regressions.
Further results of various test diagnostics (random effects, fxed effects, cross-
sectional dependence, autocorrelation) are reported in Table AII.
The panel regression models in Table VI present the interesting result that numerous
explanatory variables have a signifcant effect on systemic risk as measured by the SRC,
SRS and SRI. Most resulting signifcant coeffcients, however, match closely with our
estimated direction of infuence.
To start with SIZE, the coeffcient indicates that bank size is signifcant for SRI: the
larger the banks are, the larger the probability that they infect others should they get
into fnancial problems. Analogously observed froma macroeconomic view, a systemis
more vulnerable if it relies to a large extent on a small number of larger banks, as it
makes them being rescued or replaced by a competitor more unlikely. We confrm the
fndings of Haq and Heaney (2012), Black et al. (2013) and Varotto and Zhao (2014) for
European banks. For SRC as the dependent variable, however, we fnd that the largest
European banks did not increase systemic risks, but had a calming effect on the system.
This may be due to their implicit “too big to fail” insurance. In this case, regulation of
banks’ size may simply be counterproductive in mitigating systemic risks.
The proxy for the asset structure – loan ratio (LOAN) – and the proxy for income
structure – non-interest income (NON_INT) – showa clear positive relation to systemic
risk. Hence, the result for LOANindicates that high volumes of loans can be a signal for
defcits in risk diversifcation and increase the systemic risk of banks, while NON_INT
(indicating a positive correlation of non-interest income business and systemic risk)
indicates that the “bright side of innovation” (Beck et al., 2013) cannot be observed for
European banks during the sample period. Our results for the non-performing loan
(NON_PERF), leverage (LEVERAGE) and deposit ratio (DEPOSIT), however, show
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Table VI.
Unbalanced panel
regressions of banks’
systemic risk index
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a
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A
I
459
Systemically
important
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only insignifcant coeffcients. In particular, the insignifcance of LEVERAGE is
interesting. Regulators include this measure in Basel III, whereas we cannot empirically
support an enhancing infuence on systemic risk.
The coeffcient of TIER1 has a signifcantly positive impact on the SRS and the SRI.
It means that for European banks, our counterintuitive fnding – that high regulatory
capital ratios drive systemic risk – confrms the theoretical and empirical literature on
regulatory disincentives, as explained in Section 3.2 (Perotti et al., 2011; Black et al.,
2013). Furthermore, most coeffcients demonstrate consistent and equal signs within the
three observed systemic risk measures. For example, LIQUIDITY demonstrates
consistently positive coeffcients, whereas the cash fow-based FIN_POW has negative
coeffcients in all regression models (Table VI). Both liquidity-related measures indicate
different infuences: the frst means that liquidity of banks would increase systemic
risks – an outcome that the literature and theory do not support. One explanation could
be that a higher pool of cash indicates a lower proftability due to less-effcient allocation
of capital. The negative impact of FIN_POW on systemic risk is more reasonable, as
solvent banks are able to endow suffcient capital and current asset reserves, i.e.
cushions against losses or liquidity shortages, making them resistant against fnancial
distortions.
The signifcant coeffcients for the ratio of operating income to net sales (OP_MARG)
and the return on invested capital (ROIC) provide very interesting results, illustrating
both a risk-enhancing and a risk-reducing effect of proftability: In the short run, banks
may successfully engage in risky lending/non-lending activities. This high proftability
would shield from the risk of defaulting, and lower systemic risk. In the long run,
however, risk exposure may prove to be the other side of the proftability-coin. The
annual growth of bank income (INCOME) does not have a clear signifcant infuence on
systemic risk and, therefore, basically confrms our hypotheses from theory and
empirical literature. Our results also support the positive correlation of systemic risk
and MBR with signifcant results for SRS – and thus support the results of
Brunnermeier (2009), Varotto and Zhao (2014) and Weiß et al. (2014). Our proxy for bank
creditworthiness (LTR) shows signifcant, though contrary, impacts of fnancial
creditworthiness on systemic risk. Ahigh LTRmeasure denotes a lowcreditworthiness.
Therefore, the result suggests that banks with better/higher ratings have a higher SRC,
but that their SRI is lower.
Our country controls are insightful too: the measure for political stability of a country
(POLITIC_STAB) demonstrates an infuence that is different from what the literature
proposes and fromwhat we would expect: political stability and the absence of violence
signifcantly increase the systemic risk of European banks. One possible explanation is
that in a stable system, actors found, operate and interconnect fnancial institutions
beyond the level that the institutional framework reliably provides. This is in contrast to
political instability, in which links between fnancial units may disappear and, as a
result (and somewhat paradoxically), reduce the systemic risk and the possibility of
contagion.
REGULATION, capturing the World Bank’s assessment of ability of a government
to formulate and implement sound policies and regulations, seems to exert signifcant
infuence on systemic risk for all periods. For both the SRI and the SRS, we fnd a
negative correlation to systemic risk, i.e. that banks headquartered in countries that
promote sound policies and private systemdevelopment have less systemic risk. For the
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SRC, however, we see an inverse relationship of regulation and systemic risk that could
stemfromreactions to blind action-taking national regulators. One outcome that we did
not expect is the signifcant negative correlation of government debt ratio (DEBT) and
systemic risk. We explain this with the high government debt ratios in Western
European countries with the most stable and best-developed fnancial markets, i.e. in
countries with such institutional frameworks, higher debt ratios are not inducing a
higher systemic risk of banks. Finally, BANK_CL (the banks’ claims against national
governments) demonstrates a signifcant positive infuence on the systemic risk index.
We also provide evidence that this is especially the case for banks’ SRS. Large
government bond/loan exposures of banks indicate the strong interconnectedness of the
fnancial and governmental systems, making the transfer of (fnancial) problems
between them more likely. A higher volume of those assets can also be interpreted as a
particular diversifcation failure, as the government is already a source of political/
regulatory/legal risk, and now adds credit and market price risk.
4.3 Robustness checks
We perform numerous checks to examine the robustness of our results to alternate
model specifcations and different data (e.g. explanatory variables and the equity index
as a proxy for the fnancial system)[14]. Robustness check specifcation I (Table AIII)
provides results for the fxed effects regression of the full (unbalanced and balanced)
sample. We can assume that the results of the baseline regressions depend neither on
insignifcant explanatory variables nor on the unbalanced nature of our panel data nor
on the choice of a fxed or random panel regression model. Robustness check
specifcation II (Table AIV) provides results for the baseline regressions (Table VI)
using another bank equity index (EU Datastream Banks Index) as a proxy for the
fnancial system. This supports the assumption that our results do not depend on the
bank equity index we chose. Additionally, we estimate alternative specifcations of
the panel regressions using different sets of explanatory variables. We fnd that the
results from our baseline regressions are not substantially affected. To conclude, our
robustness checks generally suggest that the fndings obtained in the baseline
specifcations are robust.
5. Conclusion
In this study, we analyse the major drivers for systemic risk of banks in Europe. In
particular, we identify why some banks are expected to contribute more to systemic
events in the European fnancial system than others. In our panel regressions, we fnd
empirical evidence supporting existing literature on SIFIs, identifying bank size, asset
and income structure, loss and liquidity coverage, proftability and several
macroeconomic conditions as drivers of systemic risk. We also fnd that simple
approaches in measuring systemic risk – as proposed by Rodríguez-Moreno and Peña
(2013) – would not be suitable because systemic risk contribution may be driven by
different factors than systemic risk sensitivity.
Regulators have to consider a broad variety of indicators for assessing systemic
risks. Although we propose different measures for systemic risk, we empirically support
the urgency of recent regulatory approaches to identify SIBs in Europe by using a broad
set of fnancial indicators (Basel Committee on Banking Supervision, 2013).
Macroprudential regulation is essential to prevent systemic risk crises in the banking
461
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system. We provide evidence that existing microprudential-oriented rules for liquidity,
liable equity capital and leverage are less effective, and that policymakers may consider
new measures like asset diversifcation to mitigate systemic risks in the banking
system.
However, some limitations of our research remain: even though our systemic risk
measures avoid the shortcomings of the existing approaches, the assessment of the tail
co-movement of security prices excludes a number of (admittedly, “smaller”)
institutions without publicly listed securities[15]. The second shortfall is that we do not
assess the systemic impact of other fnancial institutions, such as insurers, investment
funds and players from the growing shadow banking system. Finally, to confrm our
fndings in the long run, future research could try to make use of fnancial and country
data over longer periods.
Notes
1. Systemic risk is the risk “that cumulative losses will accrue from an event that sets in motion
a series of successive losses along a chain of institutions or markets comprising a system[…]
That is, systemic risk is the risk of a chain reaction of falling interconnected dominoes”
(Kaufman, 1995). Essentially, we follow this idea by measuring the contagion from banks to
the fnancial system and vice versa. The European Systemic Risk Board/European
Commission (2010) defnes systemic risk as the risk of disruption in the fnancial systemwith
the potential to have serious negative consequences for the internal market and the real
economy. Similarly to this idea, Acharya et al. (2011) and Adrian and Brunnermeier (2011)
quantify systemic risk by measuring a bank’s (risk) contribution to the overall fnancial
system. For a list of further possible defnitions of systemic risk in the literature, see
Prokopczuk (2009).
2. Banking contagion, concentrating on the transmission of a bank shock to other banks or the
fnancial system, lies at the heart of systemic risk. Long before the recent fnancial crises,
Bagehot (1873) diagnoses as follows: “In wild periods of alarm, one failure makes many, and
the best way to prevent the derivative failures is to arrest the primary failure which causes
them.”
3. Bisias et al. (2012) provide a survey of systemic risk measures.
4. This criticism is similar to the general criticism of the VaR.
5. Theoretically, r
i
could be equal to zero, and for that case, formula (1) would not work. This
case is, however, hardly possible in practice, as an average of the worst stock return
observations is unlikely to be zero.
6. We derived the SRC from the CoVaR measure proposed by Adrian and Brunnermeier (2011).
7. As we understand the SRI as an index, we use equal weighting of SRCand SRS. Instead of the
arithmetic, other weightings like the geometric mean could also be applied. We believe,
however, that the economic interpretation of the SRI would not beneft from this.
8. The newer, but slightly shorter, European Central Bank list of “signifcant” supervised
entities from September 2014 is equal to the EBA 2014 list (European Banking Authority,
2014) – with a fewexceptions (European Central Bank, 2014). We do not use this list, as it does
not include UK banks.
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9. Namely, Australia, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Hungary,
Ireland, Italy, Latvia, Luxemburg, Malta, the Netherlands, Norway, Poland, Portugal,
Slovenia, Spain, Sweden and the United Kingdom.
10. We manually check missing accounting values, fnding and adding most of them. In some
cases, however, we do not fnd the necessary data, which may bias our results because balance
sheet composition may affect the bank opacity; see Flannery et al. (2013). In a recent paper on
bank opaqueness, Mendonça et al. (2013) fnd that a decrease in bank opaqueness fosters an
environment favourable to the development of a sound banking system and the avoidance of
fnancial crises.
11. The MSCI Europe Financials Index (Datastream code: M1URFNE) offers the best available
coverage for the European fnancial sector. We also create our own indices by value weighting
the stock returns of all banks in our samples (as proposed by e.g. Weiß et al., 2014), leading to
the same core results for our regression. However, as we are more interested in analysing the
determinants for systemic risk in the European fnancial sector as a whole, those results are
not presented in this paper.
12. Interestingly and in contrast to most of the literature, Dungey et al. (2012) fnd cases where
frm characteristics make little difference to the systemic risks of banks.
13. The BCBS uses exposures (a method comparable to our SIZE) as an indicator of systemic
importance.
14. The regression diagnostics for the robustness checks are not reported to save space, but are
available upon request.
15. The most useful measures of systemic risk may be ones that have yet to be tried because they
require proprietary data only regulators can obtain, see Bisias et al. (2012).
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Ausruhen”, Wirtschaftsdienst, Vol. 94 No. S1, pp. 28-34. doi: 10.1007/s10273-014-1647-0.
Appendix 1
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Systemic risk sensitivity (SRS)
Note: The table reports the two systemic risk measures we use to construct the systemic risk
index: SRS and SRC
Figure A1.
Banks’ systemic risk
contribution (SRC)
and sensitivity (SRS)
from 2007 to 2012
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Appendix 2
Table AI.
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Table AII.
Panel data
tests/diagnostics
Test/diagnostic 2007-2012 unbalanced panel 2007-2012 balanced panel
Time-fxed effects Prob ?F ?0.240 Prob ?F ?0.560
Random effects
Lagrange multiplier (LM) test
Hausman test
Prob ?chi
2
?0.000
Prob ?chi
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?1.000
Prob ?chi
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?0.230
Prob ?chi
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?0.029
Cross-sectional dependence
Breusch–Pagan LM test
Pesaran test
Friedman test
Frees test
Not enough observation
Not enough observation
Not enough observation
Not enough observation
Not enough observation
Pesaran: Pr. 1.666
Frees: 0.769
Friedman: Pr. 1.000
Autocorrelation
(Wooldridge test)
Prob ?F ?0.308 Prob ?F ?0.687
Heteroscedasticity We use heteroscedasticity-robust standard error estimates
(Huber/White) to account for heteroscedasticity
Notes: The table provides results of eight tests for time-fxed/random effects, cross-sectional
dependence and autocorrelation for all shown panel regressions. There are no time-fxed effects, but
only random effects. We are able to reject cross-sectional dependence only for the balanced panel.
Although autocorrelation is not a problem in panels with few years, we test it anyway. Tests do not
indicate autocorrelation. To account for heteroscedasticity, we use heteroscedasticity-robust standard
errors estimates (Huber/White estimators)
JFEP
7,4
470
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Table AIII.
Robustness check
specifcation I –
panel regressions
(fxed effects) of
banks’ systemic risk
index
Dependent variable
Dependent variable
Unbalanced fxed
effects
Unbalanced fxed
effects
Balanced fxed
effects
Balanced fxed
effects
SIZE 0.007 (0.968) 0.001 (0.994)
LOAN 0.306 (0.189) 0.253 (0.232)
NON_INT 0.105*** (0.004) 0.121*** (0.002) 0.112*** (0.004) 0.125*** (0.003)
NON_PERF 1.269 (0.581) 1.225 (0.611)
LEVERAGE 0.001 (0.228) 0.006*** (0.000) 0.002 (0.161)
DEPOSIT 0.638** (0.013) 0.691*** (0.003) 0.586** (0.022) 0.814*** (0.001)
TIER1 1.179** (0.042) 0.900 (0.113) 1.477*** (0.007) 0.593 (0.285)
LIQUIDITY 0.137*** (0.001) 0.116*** (0.001) 0.064** (0.019) 0.126*** (0.008)
FIN_POW ?1.955*** (0.001) ?2.185*** (0.001) ?0.979*** (0.003) ?0.850*** (0.000)
OP_MARG 0.185* (0.066) 0.134 (0.202) 0.175* (0.087) 0.138 (0.222)
ROIC ?1.187* (0.060) ?1.153** (0.038) ?1.749*** (0.009) ?1.105* (0.060)
INCOME 0.031 (0.280) 0.029 (0.337)
MBR 0.108** (0.018) 0.129** (0.017) 0.026 (0.409)
LTR 0.239 (0.120) 0.194 (0.190)
POLITIC_STAB 0.211*** (0.009) 0.186** (0.017) 0.169** (0.025) 0.198*** (0.009)
REGULATION ?0.163 (0.203) ?0.107 (0.383)
CONCENTRATION 0.270 (0.330) 0.392 (0.122)
DEBT ?0.413* (0.051) ?0.507** (0.024) ?0.559*** (0.006) ?0.680*** (0.001)
BANK_CL 0.962** (0.026) 0.991** (0.019) 1.039** (0.020) 1.189*** (0.004)
Observations 334 334 294 294
Groups 60 60 49 49
R
2
Within 0.323 Within 0.297 Within 0.391 Within 0.281
Notes: This table shows that our results from the baseline regressions does not depend on
insignifcant explanatory variables, on the unbalanced nature of our panel data or on the choice of a
fxed or random panel regression model. For the estimation of the linear panel regression model,
we use heteroscedasticity-robust White (1980) standard errors. The p-values are denoted in
parentheses; *; **; ***indicate coeffcient signifcance at the 10, 5 and 1% levels, respectively.
Variable defnitions and sources are provided in Table AI
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Table AIV.
Robustness check
specifcation II –
unbalanced panel
regressions of banks’
systemic risk index
using an alternative
index (EU
Datastream Banks
Index) as a proxy for
the banking system
Dependent variable
Fixed effects Random effects
Systemic risk
contribution (SRC)
Systemic risk
sensitivity (SRS)
Systemic risk
index (SRI)
SIZE ?0.213 (0.280) 0.416 (0.162) 0.198*** (0.000)
LOAN ?0.010 (0.961) 0.658 (0.177) 0.504*** (0.000)
NON_INT 0.029 (0.404) 0.167** (0.012) 0.142*** (0.000)
NON_PERF ?2.023 (0.276) 3.380 (0.439) 0.834 (0.685)
LEVERAGE 0.000 (0.308) 0.001 (0.485) 0.000 (0.716)
DEPOSIT 0.275 (0.296) 0.860 (0.129) 0.223 (0.160)
TIER1 0.020 (0.958) 2.118* (0.050) 0.910* (0.054)
LIQUIDITY 0.017 (0.432) 0.241*** (0.007) 0.047** (0.025)
FIN_POW ?0.208 (0.572) ?3.337*** (0.005) ?0.618 (0.233)
OP_MARG 0.019 (0.873) 0.145 (0.421) 0.109 (0.233)
ROIC 0.183 (0.701) ?1.597* (0.090) ?1.316*** (0.009)
INCOME 0.033 (0.197) 0.009 (0.893) ?0.006*** (0.000)
MBR ?0.036 (0.230) 0.229** (0.015) 0.014 (0.806)
LTR ?0.580*** (0.000) 0.046 (0.882) 0.385*** (0.001)
POLITIC_STAB ?0.041 (0.502) 0.494*** (0.001) 0.137*** (0.000)
REGULATION 0.477*** (0.001) ?0.924*** (0.000) ?0.187*** (0.006)
CONCENTRATION ?0.393 (0.134) 0.884* (0.059) ?0.184 (0.114)
DEBT 0.228 (0.204) ?1.056** (0.016) ?0.349*** (0.000)
BANK_CL 0.062 (0.778) 1.419* (0.073) 0.579*** (0.005)
Observations 334 334 334
Groups 60 60 60
R
2
Within 0.396 Within 0.325 Overall 0.494
Notes: To prove our results do not depend on the bank equity index, we chose for our baseline
regression; this table presents the results of the panel regression of banks’ systemic risk on the
European banking sector using another bank equity index. For the estimation of the linear panel
regression model, we use heteroscedasticity-robust White (1980) standard errors. The p-values are
denoted in parentheses; *; **; ***indicate coeffcient signifcance at the 10, 5 and 1% levels,
respectively. Variable defnitions and sources are provided in Table AI
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Table AV.
Sample description
Country (banks) 2007 2008 2009 2010 2011 2012 Sum
AUT 1 1 1 1 1 1 6
BEL 2 2 2 2 1 1 10
CYP 1 2 2 2 1 0 8
DNK 2 3 3 3 3 3 17
ESP 5 5 5 5 5 5 30
FIN 1 1 1 1 1 1 6
FRA 3 3 3 3 3 3 18
GBR 4 4 4 4 4 4 24
GER 6 6 5 5 5 3 30
GRC 4 4 4 4 4 4 24
HUN 1 1 1 1 1 1 6
IRL 3 3 3 3 3 3 18
ITA 10 11 11 11 11 11 65
MLT 1 1 0 1 0 1 4
NED 2 1 1 1 1 1 7
POL 3 3 3 3 3 4 19
PRT 3 3 3 3 3 3 18
SWE 4 4 4 4 4 4 24
Sum 56 58 56 57 54 53 334
Variables (mean values) 2007 2008 2009 2010 2011 2012
SRI 0.723 0.737 0.725 0.766 0.756 0.732
SIZE 11.149 11.151 11.158 11.151 11.18 11.174
LOAN 0.633 0.649 0.65 0.639 0.637 0.631
NON_INT 0.414 0.266 0.514 0.567 0.449 0.511
NON_PERF 0.003 0.006 0.013 0.011 0.013 0.014
LEVERAGE 9.924 6.335 7.625 7.643 6.672 3.126
DEPOSIT 0.429 0.443 0.471 0.47 0.452 0.489
TIER1 0.083 0.085 0.104 0.108 0.111 0.121
LIQUIDITY 1.113 1.141 0.878 0.873 0.91 0.89
FIN_POW 0.119 0.047 0.064 0.058 0.043 0.055
OP_MARG 0.163 0.08 0.068 0.072 0.009 ?0.013
ROIC 0.05 0.039 0.022 0.023 0.001 0.014
INCOME 0.243 0.077 ?0.121 ?0.056 0.062 0.955
MBR 1.884 0.774 0.905 0.797 0.594 0.63
LTR 0.16 0.186 0.225 0.271 0.372 0.458
POLITIC_STAB 0.681 0.665 0.503 0.597 0.649 0.626
REGULATION 1.328 1.324 1.284 1.267 1.193 1.165
CONCENTRATION 0.692 0.707 0.7 0.713 0.714 0.705
DEBT 0.638 0.68 0.786 0.85 0.907 0.946
BANK_CL 0.135 0.127 0.164 0.206 0.208 0.228
Notes: The table provides the number of banks in the sample selected for each country. We
additionally report the evolution of the mean values of the variables used in our empirical investigation.
Variable defnitions and sources are provided in Table AI
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Table AVI.
Bank sample
constituents
Country Bank name
Austria Erste Group Bank AG
Belgium Dexia NV
Belgium KBC Group NV
Cyprus Bank of Cyprus Public Company Ltd.
Cyprus Hellenic Bank Public Company Ltd.*
Denmark Danske Bank
Denmark Jyske Bank
Denmark Sydbank
Finland OP-Pohjola Group
France BNP Paribas
France Groupe Crédit Agricole
France Société Générale
Germany Aareal Bank AG
Germany Commerzbank AG
Germany Deutsche Bank AG
Germany Hypo Real Estate Holding AG
Germany IKB Deutsche Industriebank AG
Germany Landesbank Berlin Holding AG
Greece Alpha Bank SA
Greece Eurobank Ergasias SA*
Greece National Bank of Greece SA*
Greece Piraeus Bank SA*
Hungary OTP Bank Ltd
Ireland Allied Irish Bank plc
Ireland Bank of Ireland
Ireland Permanent TSB plc*
Italy Banca Carige SpA*
Italy B. Monte dei Paschi di Siena SpA*
Italy B. Piccolo Credito Valtellinese SC*
Italy B. Pop. Dell’Emilia Romagna SC*
Italy Banca Popolare Di Milano SC*
Italy Banca Popolare di Sondrio SpA*
Italy Banco Popolare SC*
Italy Credito Emiliano SpA
Italy Intesa Sanpaolo SpA
Italy Mediobanca SpA
Italy UniCredit SpA
Italy Unione Di Banche Italiane SpA
Malta Bank of Valletta plc
The Netherlands ABN AMRO Bank NV
The Netherlands ING Bank NV
Poland BANK BPH SA
Poland Bank Handlowy w Warszawie SA
Poland Getin Boble Bank SA
Poland PKO Bank Polski
Portugal Banco BPI SA
(continued)
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Table AVI.
Country Bank name
Portugal Banco Comercial Português SA*
Portugal Espírito Santo Financial Group SA
Spain BBVA SA
Spain Banco de Sabadell SA
Spain Banco Popular Español SA
Spain Banco Santander SA
Spain Bankinter SA
Sweden Nordea Bank AB (publ)
Sweden Skandinaviska Enskilda B. AB (SEB)
Sweden Svenska Handelsbanken AB (publ)
Sweden Swedbank AB (publ)
UK Barclays plc
UK HSBC Holdings plc
UK Lloyds Banking Group plc
UK Royal Bank of Scotland Group plc
Notes: The table provides the full list of banks in the sample including the names of the countries
where the respective bank is headquartered. Banks that failed the EBAstress test at the end of October
2014 are denoted with *
Table AVII.
Systemic risk
ranking (mean values
for 2007-2012)
Measure Ranking Value Country Bank name
Systemic risk contribution (SRC) 1 0.822 United Kingdom Lloyds Banking Group plc
2 0.750 Spain Banco de Sabadell SA
3 0.750 Spain Banco Santander SA
4 0.731 Spain BBVA SA
5 0.686 Italy Banca Carige SpA
Systemic risk sensitivity (SRS) 1 1.501 France Société Générale
2 1.496 Poland PKO Bank Polski
3 1.469 United Kingdom HSBC Holdings plc
4 1.450 Belgium KBC Group NV
5 1.421 France Groupe Crédit Agricole
Systemic risk index (SRI) 1 1.011 France Société Générale
2 0.996 France Groupe Crédit Agricole
3 0.964 United Kingdom HSBC Holdings plc
4 0.952 Italy UniCredit SpA
5 0.947 France BNP Paribas
Note: The table provides the systemic risk ranking of the banks in the sample according to the
systemic risk measures SRC, SRS and SRI presented in Section 2
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About the authors
Jacob Kleinowis a PhDcandidate at Freiberg University in Germany. His main research felds are
the regulation of fnancial intermediaries and systemically important fnancial institutions. Jacob
is Lecturer at Freiberg University and presents his papers at several international conferences. He
has published in various international peer-reviewed journals. Jacob Kleinowis the corresponding
author and can be contacted at: [email protected]
Tobias Nell is a PhDcandidate at Freiberg University in Germany. His main research felds are
the regulation of fnancial reporting under IFRS/US GAAP and systemically important fnancial
institutions. Tobias is Lecturer at Freiberg University with focus on business analysis and
fnancial reporting for business combinations
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: [email protected]
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