Download Too Big To Fail Or To Save? Evidence from the

Too Big To Fail Or To Save?
Evidence from the CDS Market∗
Andreas Barth†
Johannes Gutenberg University Mainz and GSEFM
Isabel Schnabel‡
Johannes Gutenberg University Mainz, CEPR, and MPI Bonn
October 1, 2012
Abstract
This paper argues that bank size is not a satisfactory measure of systemic risk
because it neglects important aspects such as interconnectedness, correlation, and
the economic context. We show that, when controlling for systemic risk using the
CoVaR measure introduced by Adrian and Brunnermeier (2011), a bank’s size has
no or even a positive effect on banks’ CDS spreads, especially if the bank is based
in a highly indebted country. In contrast, a bank’s contribution to systemic risk
has a significant negative effect on banks’ CDS spreads. The effect of systemic
risk rises sharply at the onset of the financial crisis in August 2007. A country’s
∗
This is a preliminary version of a paper prepared for the 56th Panel Meeting of Economic Policy,
October 2012. We thank Valeriya Dinger, Charles Goodhart, Florian Hett, and Deyan Radev for valuable
comments and suggestions. We also benefited from comments by participants of the Brown Bag Seminar
at JGU Mainz and the 1st Research Workshop in Financial Economics at JGU Mainz.
†
Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, 55099
Mainz, Germany, telephone +49-6131-39-20746, fax +49-6131-39-25588, e-mail [email protected].
‡
Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, 55099
Mainz, Germany, telephone +49-6131-39-24191, fax +49-6131-39-25588, e-mail [email protected].
indebtedness starts to matter only after the large-scale bail-outs in response to the
Lehman default in September 2008. Hence, our results suggest that banks are not
too big to fail, but they may be too systemic to fail and too big to be saved.
Keywords: Too big to fail; too systemic to fail; too big to save; credit default
swaps.
JEL-Classification: G21, G28.
1
Introduction
The too-big-to-fail doctrine is a widely accepted hypothesis. It is argued that large banks
benefit from implicit bail-out guarantees because no government is willing to hazard the
consequences of a large bank’s default on the financial system and on the economy as
a whole. Such guarantees effectively constitute a government subsidy, which should be
reflected in market prices. We argue in this paper that bail-out probabilities depend on a
bank’s systemic importance rather than its size, and that the two concepts do not necessarily coincide. It is the systemic risk emanating from a bank, which justifies government
intervention. Therefore, banks are not too big to fail (TBTF), but too systemic to fail
(TSTF). Quite on the contrary, size may actually reduce bail-out expectations, as the
events in Iceland in the fall of 2008 have shown. Being a small country, Iceland had a
banking sector consisting mainly of three banks, which had vast balance sheets relative
to Icelandic GDP. When these institutions got in distress, the Icelandic government was
simply not able to bail them out. Consequently, the tremendous size made those banks
too big to save (TBTS) rather than too big to fail.
While the TBTF problem has been discussed extensively in the literature (see, e. g. Boyd
and Gertler, 1994; Kaufman, 2002; Stern and Feldman, 2004), the TBTS problem has
received attention only recently. An early discussion of the TBTS problem is by Hellwig
(1998) who argues that bank mergers in response to the too-big-to-fail problem may make
some institutions “too big to be rescued.” Hüpkes (2005) stresses that the TBTS problem
depends on a country’s size. Large complex financial institutions may be too large to
be saved especially in “small economies such as Belgium, the Netherlands, Sweden and
Switzerland” (Hüpkes, 2005). Rime (2005) empirically analyzes the effect of bank size
(as a proxy of too-big-to-fail expectations) on issuer ratings. Beside a measure of bank
size (total assets and a ratio of total assets to total assets of the banking sector), he
includes a proxy of the TBTS problem, namely the ratio of total bank liabilities to GDP.
However, he does not find any evidence that rating agencies “incorporate some TBTBR
[too-big-to-be-rescued] considerations in issuer ratings” (Rime, 2005) .
Our paper is most closely related to the work by Völz and Wedow (2011) and DemirgüçKunt and Huizinga (2010), who both study the effect of bank size on CDS prices. Völz
and Wedow (2011) focus mainly on the too-big-to-fail phenomenon and refer to the TBTS
problem just as an aside. Size is measured by the ratio of a bank’s market capitalization
1
over the home country’s GDP. In order to allow for TBTS effects, they also include a
quadratic term of this variable. They confirm that larger banks generally exhibit lower
CDS spreads, supporting the presence of a TBTF problem. However, the relationship
between bank size and CDS spreads is shown to revert at some point, suggesting that
some banks have already reached a size that makes them too big to rescue (Völz and
Wedow, 2011). In a similar vein, Demirgüç-Kunt and Huizinga (2010) find evidence of
TBTF and TBTS problems in market valuations, but less so in CDS spreads. This finding
is somewhat surprising as the theoretical effect on CDS prices is more straightforward than
that on equity prices. To identify TBTF, the authors consider the effect of a bank’s total
assets on CDS prices. In contrast, TBTS is measured by the relative bank size, measured
as bank liabilities over GDP. Demirgüç-Kunt and Huizinga (2010) also consider interaction
terms between relative bank size and a country’s debt over GDP ratio or its fiscal balance.
Indeed, in some (though not all) regressions, the effect of relative bank size appears to
vary depending on the home country’s debt level and fiscal balance, supporting the idea
of a TBTS problem.
Although both papers claim to provide empirical evidence for banks being too big to
fail and too big to save, their analyses are in our view not able to clearly distinguish
between these phenomena. Both TBTF and TBTS are measured on the basis of banks’
size, and the findings can therefore not easily be attributed to one of the two phenomena.
Using non-linear regression functions or interaction terms does not adequately solve this
problem. The too-big-to-save phenomenon cannot fully be captured by the convexity of
the regression function because this does not take into account the fiscal situation of the
home country. The too-big-to-fail problem – or too-systemic-to-fail problem, as we prefer
to call it – is not adequately captured by a bank’s total assets because absolute bank
size is only a crude proxy of systemic relevance. Whether a bank is too systemic to fail
is not only determined by its size, but also by its interconnectedness and its correlation
with the remaining banking sector, as has also been argued recently by Zhou (2010).
Moreover, the economic context matters. For example, if a small bank is hit by a shock
in non-crisis times, it would hardly be regarded as being systemic. But in the middle of a
financial crisis, even such a small bank may be rescued due to the fear of contagion effects.
The rescue of the German bank IKB is a case in point. Given that total assets change
only slowly over time and that changes are only vaguely related to changes in systemic
significance, bank size is not able to capture the importance of the economic context.
Therefore, one has to consider broader measures of systemic relevance than just size.
2
We propose to identify the TSTF problem by employing a broad measure of systemic
risk, namely the contribution of an individual bank to the system’s risk, measured by
∆CoV aR as proposed by Adrian and Brunnermeier (2011). Once we control for the
TSTF phenomenon, we can identify the effect of being too big to save by a bank’s size
relative to GDP. In addition to measuring the overall importance of TSTF and TBTS, we
analyze how the importance changed over the financial crisis. For that purpose, we divide
our sample into three sub-periods. The first period starts in 2005 and ends in July 2007,
just before the beginning of the financial crisis. The second period comprises the first
crisis phase until August 2008. Finally, the third period starts with the failure of the US
bank Lehman Brothers in September 2008. Before the financial crisis, neither TSTF nor
TBTS are expected to play a large role. CDS spreads were very low even for non-systemic
banks. This is likely to change with the onset of the crisis when the TSTF problem is
expected to become much more pronounced. In contrast, the TBTS problem is likely to
be affected by the huge bail-outs taking place after the Lehman default and by the failure
of the banking system and the emergence of sovereign debt problems in Iceland, which
occurred at about the same time as the default of Lehman Brothers. These events may
have been a wake-up call for investors that the credibility of bank bail-out guarantees is
linked to the home country’s fiscal situation and that such guarantees may not be fulfilled
under all circumstances by ailing governments.
Our results suggest that markets indeed reflect banks’ contribution to systemic risk. A
higher contribution to systemic risk (corresponding to a lower ∆CoV aR) translates into
lower CDS spreads, supporting the existence of TSTF. Bank size does not matter when
the bank’s home country has low debt levels. However, the effect of bank size increases in
the debt ratio of the home country and becomes positive at moderate debt levels. These
results are robust across many different specifications. Splitting the sample in sub-periods
is also instructive. We find that neither TSTF nor TBTS were priced in the market before
the financial crisis erupted in 2007. However, the importance of TSTF rises sharply after
August 2007, whereas the TBTS problem remains insignificant. TBTS intensifies sharply
after the collapse of Icelandic banks in September 2008.
The paper is organized as follows. In Section 2, we introduce the concepts of banks being
too systemic to fail or too big to be saved, and discuss why bank size is only a crude proxy
of systemic risk. Section 3 introduces the empirical model, states the main hypotheses,
provides data sources and describes the major variables used in the empirical analysis.
The empirical results from our baseline regressions are shown in Section 4. In Section 5,
3
we show empirical results for the three sub-periods of our sample – pre-crisis, beginning
of crisis, and post-Lehman. Section 6 concludes.
2
2.1
TSTF versus TBTS
Systemic Risk and Bank Size
A financial institution contributes the more to overall systemic risk, the larger are the
repercussions of its failure on the financial system and the real economy. In order to
provide a proper definition of the concept of systemic importance, we have to analyze the
driving forces of different channels of contagion.
First, there is an information contagion channel, as introduced by Chen (1999) and
Acharya and Yorulmazer (2008). They show that contagion may arise due to new information revealed to depositors after a bank failure. Observing difficulties at one bank,
depositors of other banks fear that their bank subsequently experiences financial troubles
as well and withdraw their deposits, which can result in a bank run. The amplitude of
this contagion channel depends on the similarity of financial institutions. Second, the
failure of a financial institution may infect other institutions with significant direct credit
exposures at this bank. This form of contagion via the interbank market, as shown by
Allen and Gale (2000), arises when a default of one bank results in significant write-offs
of claims at the failed bank. This can be sufficient to generate trouble in the whole financial system. The dominant determinant of the magnitude of repercussion effects through
this channel is the interconnectedness of banks. The third contagion channel works via
macro-economic feedbacks. Brunnermeier and Pedersen (2009) model this form of contagion in terms of liquidity spirals. They show that, if banks hold similar assets and one
bank has to sell assets at a fire-sale price due to short liquidity, these fire sales of the
illiquid bank depress asset prices, which also affects other banks. Such a domino effect
through asset prices can happen even in the absence of bank defaults infecting other financial institutions. A decline in prices is already sufficient. The severity of this channel
is mostly driven by the correlation among financial institutions, but also by their size.
Furthermore, the impact of fire sales is driven to a large extent by the context. In times
of financial crises, many banks suffer simultaneously from short liquidity and must sell
assets at fire sale prices. Thus, in distress periods, the behavior of many small banks
4
with correlated portfolios has the same impact on prices as the action of one single huge
bank. Fourth, contagion may arise from self-fulfilling expectations. The argument of this
channel works similar to the bad equilibrium of the seminal work of Diamond and Dybvig
(1983). The failure of one financial institution coordinates expectations of all investors
and depositors, such that investors and depositors start a run. Since the crucial element
of this channel is expectations, the probability of contagion effects are mostly driven by
two factors: size and context. Investors are more likely to coordinate on the failure of a
large bank. However, if the financial sector is in a critical condition, the collapse of a small
bank can be sufficient to give rise to bad expectations of all investors and depositors. In
calm periods, the collapse of the same bank would not bother investors or depositors at
all.
Thus, we can conclude that it is not only the size of a bank which drives the systemic
importance. There are many other factors playing a vital role, especially interconnectedness, correlation, and the economic context. Hence, the preceding discussion suggests
that measuring the systemic importance of a bank by its size is too narrow. Nevertheless,
most empirical work so far has focussed on the too-big-to-fail problem. We argue that
one should take a broader perspective and call banks benefiting from implicit bail-out
guarantees too systemic to fail (TSTF) rather than too big to fail.
2.2
Measuring Systemic Risk by Conditional Value at Risk
In this paper, we measure systemic importance using the ∆CoV aRt measure introduced
by Adrian and Brunnermeier (2011).This measure tries to capture an individual institution’s contribution to overall systemic risk. Due to the comprehensive nature of this
variable, it captures not only a bank’s size, but also other factors, such as its correlation
with other banks, as well as the economic context.
A bank’s CoV aR relative to the system is defined as the conditional value at risk, i. e.
the q% − V aR of the whole financial sector conditional on the fact that an institution i
is at its V aR level:
system|X i =V aRiq
CoV aRq
:= V aRqsystem |V aRqi
(1)
It thus gives the maximum dollar loss of the financial system at probability q conditional
on institution i being in distress.
5
Following the procedure proposed by Adrian and Brunnermeier (2011), we estimate CoVaR by using quantile regression. The CoVaR is the predicted value of a quantile regression of the financial system on an individual institution for the q-quantile, i. e.
CoV aRqsystem = α̂qi + β̂qi · V aRqi .
(2)
To allow for time variation of the estimated CoV aR, we model the conditional distributions as a function of a number of state variables. Following Adrian and Brunnermeier
(2011), we get CoV aR as the predicted value from the following quantile regressions:
CoV aRti (q) = α̂system|i + β̂ system|i V aRti (q) + γ̂ system|i Mt−1
(3)
where Mt−1 is a vector of lagged state variables. For our calculations, we focus on the
CoV aRti (q) of the same variable as Adrian and Brunnermeier (2011), i. e. the growth rates
of market-valued total financial assets, where we get the market equity from Thomson
Reuters Datastream and the leverage from Bureau van Dijk’s Bankscope. Furthermore,
we use use the same vector of lagged state variables as Adrian and Brunnermeier, but
exclude the real estate sector return in excess of the market return. We run the quantile
regressions on a 1% stress level.
∆CoV aRt is defined as the difference between CoV aRti (q) conditioned on distress times
and normal times of bank i, where normal times are characterized by the median, i. e.
∆CoV aRti (1%) = CoV aRti (1%) − CoV aRti (50%).
(4)
From these regressions, we obtain a panel of daily ∆CoV aRt , measuring the risk contribution of individual institutions to the system. It should capture all the determinants
of the contagion channels described in section 2, i. e. the systemic importance deriving
from correlation, size, interconnectedness, and from the economic context.1 Note that
∆CoV aRt is typically negative, with a more negative value indicating a greater contribution to systemic risk. For the ease of interpretation, we use −∆CoV aRt throughout the
paper, implying that an increase in this variable is to be interpreted as an increase in the
contribution to systemic risk.
1
For a detailed explanation of CoVaR and its estimation, see Adrian and Brunnermeier (2011).
6
8
6
4
2
0
0
1
2
3
Liabilities / GDP
−∆ CoVaR
4
5
Fitted values
Figure 1: Scatter plot of −∆CoV aR and the ratio of bank liabilities to GDP. The figure
is based on individual bank data between 2005 and 2011. Sources: Own calculations,
Bankscope, WDI.
Our data illustrate the difference between size and systemic risk.2 We find a low correlation of 0.241 of the ratio of bank liabilities to GDP (which is used in the literature to
measure the TBTF and TBTS phenomena) and −∆CoV aR (as our measure of TSTF).
The scatter plot in Figure 1 confirms our doubts about the suitability of size as a proxy
of systemic relevance. In a regression of −∆CoV aR on the size ratio, only 5.80% of the
variation of systemic risk contributions are explained. The most prominent examples of
banks that are small relative to home country’s GDP but that contribute to a large extent
to systemic risk are given by US investment banks. For example, Merrill Lynch has a
ratio of liabilities to GDP of only 5% in October 2008 (15% quantile of our sample), but
a contribution to systemic risk, −∆CoV aR, of 5.80 (99% quantile). Similarly, Goldman
Sachs had in November 2008 a −∆CoV aR of 6.30, with a size ratio of only 5.92% of GDP.
In contrast, there also are banks with huge relative size, but only a limited contribution to
overall systemic risk. For example, the Japanese bank Mitsubishi UFJ had in December
2010 a large relative size of 41.11% of its home country’s GDP, but a −∆CoV aR of only
0.36.
Moreover, a systemic risk measure should reflect the economic context, i. e. it should be
higher in times of crisis. As is shown in Table 1, −∆CoV aR suggests a sharp increase
2
See Section 3.3 for a description of data sources.
7
in systemic risk during the financial crisis by more than one standard deviation of the
pre-crisis −∆CoV aR and a further rise after the default of Lehman Brothers. The ratio of
bank liabilities to GDP, however, does hardly mirror the crisis: the maximum drops, and
the mean increases only slightly in the crisis period and even drops in the post-Lehman
period.
Table 1: Evolution of the ratio of bank liabilities to GDP (TBTS) and −∆CoV aR during
the crisis
Variable
Mean
Std. Dev.
Min.
Max.
N
0.005
0.004
2.669
4.848
1472
1472
0.015
0.009
4.297
4.789
835
835
8.590
4.031
1956
1956
pre-crisis period
−∆CoV aR
T BT S
0.854
0.556
0.503
0.827
crisis period
−∆CoV aR
T BT S
1.509
0.606
0.860
0.816
post-Lehman period
−∆CoV aR
T BT S
1.886
0.558
1.398
0.597
0.011
0.007
Descriptive statistics for −∆CoV aR and T BT S. The
pre-crisis period denotes the time until July 2007. The
crisis period is the period from August 2007 to August 2008. The post-Lehman period starts in September
2008.
2.3
Too Big to Save
The recent crisis has shown that banks cannot just be too big to fail, but also too big
to save (TBTS). In particular, if the size of a financial institution exceeds a country’s
ability of a bail-out, the bank simply cannot be rescued by the safety net. Therefore,
we do not expect that banks will benefit the more from a country’s safety net the bigger
they are, once we control for systemic risk. While the too-big-to-fail doctrine argues that
banks’ size increases the bail-out probability, we argue that it is the systemic importance
of banks that drives bail-out expectations. Hence, a bank’s size should decrease the bailout probability conditional on systemic risk. In this sense, TBTS can be seen as the
antagonist of the too-systemic-to-fail problem.
8
The most prominent recent example of banks being too big to save was the case of
Iceland, which experienced considerable economic trouble due to its banking sector. The
financial turmoil resulted in strong losses of its internationally active banks, which had
vast balance sheets relative to the size of the economy.3 Consequently, the Icelandic
Financial Supervisory Authority took control of these banks within one week in October
2008. It is instructive to compare the CDS prices, as indicators of the market’s default
expectation, of the Icelandic bank Kaupthing and the German bank IKB, two institutions
of similar size who both faced serious troubles during the recent financial crisis. Figure 2
suggests that markets perceived a sharp disparity in the bail-out probabilities of the two
banks.
4500.00
4000.00
3500.00
3000.00
2500.00
2000.00
Kaupthing
1500.00
IKB
1000.00
500.00
0.00
Figure 2: CDS spreads of the Icelandic bank Kaupthing and the German bank IKB (in
basis points). Source: Thomson Reuters Datastream.
3
Empirical Analysis
3.1
Hypotheses
Our empirical analysis tries to explain the evolution of bank CDS spreads. A credit
default swap (CDS) is an insurance contract against default or another type of credit
event. More precisely, the protection buyer pays a default swap premium for receiving
3
Total assets of the three largest banks relative to GDP skyrocketed from less than 200% in 2003 to
almost 1000% in 2008, cf. The Central Bank of Iceland (2009).
9
the guarantee that the protection seller covers the incurred losses if a default is triggered.
Therefore, the price of a bank CDS is a function of the market’s expectations of a bank’s
actual probability of default (PD):
CDS
= f (market expectation of actual PD)
(5)
The market expectation of banks’ actual PD is a function of the bank-specific fundamental
PD and the probability of a government bail-out in case of distress (cf. Gropp, Hakenes,
and Schnabel, 2010):
actual PD = (1 − bail-out probability | fundamental default ) · fundamental PD.
(6)
We now formulate four hypotheses that will be tested in the empirical analysis. The first
hypothesis refers to the too-systemic-to-fail problem. Since the consequences of the failure
of a systemic institution for the rest of the financial system can be enormous, a bank is
the more likely to be rescued by the government, the higher is its systemic risk. We
therefore expect that systemic banks have a lower market expectation of default, which
should show up in lower CDS spreads, as postulated by Hypothesis 1.
Hypothesis 1 (Too systemic to fail) Ceteris paribus, CDS spreads are smaller for
banks with a higher contribution to systemic risk.
For a given level of systemic risk, there are no incentives for a government to bail out
a bank just because of its size. To the contrary, if a financial institution has reached a
particular size relative to its home country’s GDP, the country may not be able to bail
out this institution. Therefore, we expect a non-linear relationship between bank size and
CDS spreads. For relatively small bank sizes, CDS spreads should not be affected by bank
size when controlling for systemic risk. For large banks, the effect of bank size should be
positive. Note that this prediction differs from the prediction by Völz and Wedow (2011)
who use bank size as a proxy for systemic risk. We can now establish Hypothesis 2.
10
Hypothesis 2 (Too big to save: Nonlinear size effect) Ceteris paribus, a bank’s size
does not matter for CDS spreads if the bank is small. The effect of a bank’s size on CDS
spreads increases in the bank’s size and becomes positive for large banks, controlling for
banks’ contributions to systemic risk.
However, the ability of a country to bail out banks does not only depend on the banks’
size, but also on the country’s fiscal situation (cf. Buiter and Sibert, 2008; DemirgüçKunt and Huizinga, 2010). Even if financial institutions are large relative to GDP, a
government may still be able to bail them out if the country has a high debt capacity. In
this situation, CDS spreads may not respond much to banks’ size. In contrast, a bail-out
may not be feasible for countries with already high debt levels. In this case, the effect of
bank size on CDS spreads is expected to be much stronger. By controlling for systemic
risk, we can isolate a country’s willingness to conduct a bail-out (Hypothesis 1) from its
ability to do so. This leads us to Hypothesis 3.
Hypothesis 3 (Too big to save: Debt capacity) Ceteris paribus, the effect of a bank’s
size on CDS spreads increases in a country’s debt level.
Finally, banks are more likely to be bailed out if their services are hardly substitutable
by other parts of the financial system. For example, in a bank-based system, banks play
a more crucial role than in a market-based system. Therefore, if the real economy in a
country is strongly reliant upon the banking sector (showing up in a high ratio of domestic
credit relative to GDP), a government may be more likely to bail out financial institutions,
implying lower CDS spreads. This yields the prediction of Hypothesis 4.
Hypothesis 4 (Substitutability) Ceteris paribus, CDS spreads are larger for banks in
countries where banks are more easily substitutable.
Summing up, our main hypotheses are that (i) a bank’s systemic importance lowers CDS
spreads, (ii) the size of a financial institution relative to its home country’s GDP raises
CDS spreads when banks are large (controlling for its contribution to systemic risk), (iii)
the size effect is stronger for banks in highly indebted countries, and (iv) substitutability
raises CDS spreads.
11
3.2
Empirical Model
In our empirical analysis, we explain the market indicator of banks’ probability of default
as a function of bank-specific and country-specific characteristics. We model the CDS
price of bank i in country j at time t as
CDSi,j,t =β0 + β1 · T ST Fi,j,t−1 + β2 · T BT Si,j,t−1 + β3 · T BT Si,j,t−1 · debtratioj,t−1
+ δ1 · Xi,j,t−1 + µi + γt + ui,t .
(7)
In order to take the unobserved heterogeneity of banks and over time into account, we
include bank-specific fixed effects µi , as well as time fixed effects γt . TSTF measures a
bank’s contribution to systemic risk, −∆CoV aR (see Section 2.2).4 TBTS, defined as
bank liabilities over GDP, captures the ability of governments to bail out banks: The
larger a bank is relative to its home country’s GDP, the less likely it is to be bailed out,
controlling for systemic risk.5 In some specifications, this variable also enters in squared
form to capture nonlinear effects. We further include an interaction term of TBTS and
the ratio of government debt to GDP, as the effect of a bank’s size may depend on the debt
level of its home country. Xi,j,t are control variables determining the bail-out probability
and the fundamental probability of default. They are explained below. All explanatory
variables are lagged by one period to avoid endogeneity problems. Moreover, since CDS
prices can hardly be regarded as being stationary (especially in times of financial turmoil),
we estimate our model in first differences. Standard errors are clustered at the country
level to account for the correlation of banks within the same country.
3.3
Data Sources
We collect daily CDS data from Thomson Reuters Datastream. We focus on senior CDS
with a maturity of five years, since it has been shown that trading liquidity is highest
at this maturity (see European Central Bank, 2008). We also collect the yields on home
countries’ government bonds with a maturity of ten years from Thomson Reuters. Our
4
5
∆CoV aR enters with a negative sign to facilitate interpretation.
In a robustness check, we use total bank assets instead of liabilities to construct the TBTS measure.
12
second main data source is Bureau van Dijk’s Bankscope, which contains balance sheet
information for a large number of banks from a broad set of countries. We use banks’
consolidated statements for two reasons. On the one hand, CDS spreads refer to the
entire financial institution, not only for the parent company. On the other hand, the
events in Iceland have shown that the home country of the parent company might also
be responsible for its branches abroad.6 Moreover, we use national accounts data from
the World Bank’s World Development Indicators database (WDI). Since most of our
exogenous variables are only available at yearly frequency, we use the monthly average of
all variables with daily frequency and use cubic spline interpolation for variables that are
available only at yearly frequency.
Our analysis is based on the top 100 largest banks in the world, measured by total assets
at the end of 2008, for which data was available. We expand the dataset by several banks,
which already failed in 2008 over the course of the financial crisis. However, we do not
restrict our sample regarding bank specialization. We start our sample in 2005 since before
that time, trading activity in the CDS market was limited to only some banks and pricing
information incomplete, and we collect the data until the end of 2011.7 Our final dataset
includes 76 banks from 23 countries and spans seven years. In the following subsection,
we will describe the included control variables and present descriptive statistics of the
data used in the analysis.8
3.4
Control Variables
The systemic relevance of a bank is measured by ∆CoV aR. In order to facilitate interpretation, we multiply ∆CoV aR by −1 such that higher values of the variable indicate
higher contributions to systemic risk.
We follow Buiter and Sibert (2008) in defining the determinants of the too-big-to-save
problem (what they call the “vulnerable quartet”). According to these authors, TBTS is
particularly considerable in “(i) small countries with (ii) a large, internationally exposed
6
A letter of Iceland’s Minister of Business Affairs to the British Treasury from 2008 says that “if
needed the Icelandic Government will support the Depositors’ and Investors’ Guarantee Fund in raising
the necessary funds, so that the Fund would be able to meet the minimum compensation limits in the
event of a failure of Landsbanki and its UK branch.”
7
Note that data coverage is relatively low in 2011 due to missing data.
8
All details on the preparation of the dataset are listed in Table A1 in Appendix 1.
13
banking sector, (iii) its own currency, and (iv) limited fiscal spare capacity relative to the
possible size of the banking sector solvency gap.” In line with this characterization, we
measure bank size relative to a country’s GDP. To allow for nonlinear effects, we enter
this variable as a second order polynomial in some of the regressions. To control for
the limited fiscal spare capacity, we include the amount of government debt relative to
GDP and government’s foreign exchange reserves including gold relative to GDP from the
World Bank’s World Development Indicators (WDI), as well as the yield on a ten-year
government bond from Thomson Reuters Datastream. All three variables are essential
for the question whether a country has enough resources or whether it can raise enough
money for bailing out its banking sector. The higher the ratio of government debt to
GDP, the more likely a bank is to be TBTS and the lower is the probability of a bank
bail-out. Higher reserves indicate a higher ability of a sovereign to bail out its banks and
thus makes a bank less likely to be TBTS. Similarly, a high government bond yield is
associated with a country facing more difficulties in raising money. Therefore, we expect
that it is more likely for a bank to be TBTS, the higher the government’s bond yield.
Finally, the substitutability of banks is measured by the amount of domestic credit provided by the banking sector in relation to the GDP from the World Bank’s World Development Indicators (WDI) database.
We also control for bank-specific characteristics affecting a bank’s fundamental probability of default. In order to capture the bank-specific risk of an individual institution, we
use two alternative approaches. First, we include the value at risk (VaR) at the 1% level,
the most common measure of risk used by financial institutions.9 Since our measure for
systemic importance, ∆CoV aR, is by construction correlated with the VaR, we alternatively control for the bank-specific risk by additionally considering different balance sheet
ratios of risk in banking. First, we control for banks’ leverage ratio (total assets over equity). Second, we include a bank’s return on average assets to capture bank performance
and profitability. Finally, we include the bank’s recurring earning power, which measures
after tax profits adding back provisions for bad debts as a percentage of total assets. At
the country level, we use the growth rate of GDP in order to control for business cycle
effects.
9
Just as ∆CoV aR, we multiply this variable by −1, such that a higher VaR indicates higher individual
risk.
14
3.5
Descriptive Statistics
We present descriptive statistics of bank characteristics in Table 2 and of country characteristics in Table 3.
Table 2: Descriptive statistics of bank characteristics
Variable
CDS
CDS (winsorized)
−∆CoV aR
−∆CoV aR (winsorized)
TBTS (Total liabilities/GDP)
TBTS (Total assets/GDP)
−V aR
−V aR (winsorized)
Leverage
ROAA
Earning Power
ln(Total assets)
Mean Std. Dev.
110.002
146.655
100.910
100.224
1.456
1.158
1.367
0.901
0.568
0.728
0.592
0.751
6.607
4.492
6.231
3.146
22.654
12.816
0.359
2.363
1.098
1.676
19.88
1.184
Min.
3.296
7.500
0.005
0.087
0.004
0.005
0.705
1.885
3.971
-71.733
-20.349
15.674
Max.
2217.381
406.670
8.590
3.303
4.848
4.961
42.271
13.902
115.879
2.942
21.639
22.103
N
4263
4263
4263
4263
4263
4263
4263
4263
4263
4211
4211
4263
All variables with daily frequency are winzorized at the 5%-level before calculating
monthly averages.
The average CDS price is 110.00 basis points, the maximum value being more than 2200
basis points, realized by the Irish bank Anglo Irish Bank in December 2010. Furthermore,
the mean of −∆CoV aR is 1.46, which is of the same order of magnitude as in Adrian
and Brunnermeier (2011). In order to deal with the extreme outliers of some banks in
this data with daily frequency, we use a 5% winsorization before calculating monthly
averages. This has the advantage of maintaining the information that some observations
are extreme without allowing for an unduly strong impact of individual observations. The
average ratio of liabilities to home country’s GDP is 56.80%, while the largest institution
in relative terms exceeds its home country’s GDP by more than 380%, which was attained
by the Swiss bank UBS in 2007. The bank-specific risk of an individual institution, −V aR,
has a sample mean of 6.61, with the Irish bank Bank of Ireland showing the highest level
of individual risk in November 2008. In order to deal with those outliers, we also winsorize
this variable at the 5% level. The lowest return on average assets of -71.73 comes from
the Icelandic bank Glitnir in the aftermaths of its failure. Moreover, the huge leverage
ratio of 115.88 refers to the Belgian bank Dexia in 2008.
The average ratio of domestic credit provided by the banking sector in relation to GDP is
15
Table 3: Descriptive statistics of country characteristics
Variable
Mean Std. Dev.
Domestic credit / GDP 172.556
64.221
Debt ratio
63.091
32.453
Reserves / GDP
11.836
19.305
Bond yield
3.945
1.099
GDP growth
1.847
3.551
Min.
Max.
N
57.590 331.766 4232
18.029 174.979 3824
0.315 109.378 4263
0.930
8.449 3850
-7.241 14.763 4263
172.56%, with the maximum value of 331.77% in Japan in 2010 and the minimum value
of 57.59% in India in 2005. Moreover, we find in our dataset an average debt ratio of
63.09%, with Japan being the most indebted country in 2010 with a proportion of central
government debt to GDP of 174.98%. Australia has the minimum debt ratio of 18.03%
in 2008. The average yield for a government bond with a maturity of ten years amounts
to about 4% with a maximum of 8.45%, the yield of Ireland in December 2010.
4
Estimation Results in Baseline Specification
Table 4 presents the regression results from our baseline specification. The regression
results show that a higher systemic importance, as measured by an increase in −∆CoV aR,
significantly decreases CDS spreads in all specifications. In contrast, the effect of bank
size is always insignificant when controlling for systemic risk. There is no evidence of a
nonlinear size effect either: the coefficient of the squared term is insignificant and has
an unexpected sign (see column 2 of Table 4). An explanation of this finding is that it
is not bank size as such, but its interaction with the home country’s debt capacity that
determines CDS spreads. When we additionally include an interaction term of T BT S
and a country’s debt ratio, we find that bank size has no significant effect on bank CDS
spreads in countries with a zero debt level (see the coefficient of T BT S). However, the
effect increases in a country’s debt ratio, consistent with the idea that countries with
higher debt ratios are less able to bail out financial institutions. The overall effect of
relative bank size becomes positive already at moderate debt ratios (between 9.5% in
specification (3) up to 18.8% in specification (5)). The significant coefficient of the debt
ratio in specification (3) indicates that the government debt ratio positively affects the
CDS price for mean-sized banks, with its effect rising in a bank’s ratio of liabilities to
16
GDP.10 The first result is not robust across specifications, but the increasing effect of
debt (which is just the counterpart of the increasing effect of bank size) is found in all
specifications.
Summing up, these results strongly support Hypothesis 1: The more a financial institution
contributes to systemic risk, the more likely is a bail-out in order to prevent negative
repercussion effects on the rest of the financial system, and the lower are the bank’s
CDS spreads. However, a bail-out becomes less likely for large banks even at moderate
sovereign debt levels, and the effect strengthens when the debt ratio increases, confirming
Hypothesis 3. In contrast, there is no evidence of a nonlinear size effect when controlling
for systemic risk, as predicted by Hypothesis 2. There is no bank size above which
banks become too big to be saved, but this level depends on a country’s debt capacity.
Another interesting result concerns substitutability. In countries with a higher ratio of
domestic credit over GDP, bank CDS spreads tend to be lower. This is consistent with
Hypothesis 4 that bail-out probabilities are higher in countries where the banking sector
is more important.
The remaining coefficients at the country level are in line with our expectations. We find
that the CDS price is significantly higher in countries with worse refinancing conditions,
indicated by a higher government bond yield. GDP growth and reserves (foreign exchange
plus gold) have the expected negative sign, but are insignificant. At the bank level, we
find no effect of the value at risk (although it has the expected sign), which may stem
from the high correlation with ∆CoV aR. The coefficients of the balance sheet ratios are
also insignificant.
We checked the robustness of our results in various ways. First, we used a milder winsorization for our risk variables ∆CoV aR and V aR (2.5% instead of 5%, see Table A3
in the Appendix). The results are virtually unchanged, with the same coefficients being
significant and similar orders of magnitude of all coefficients. Second, we substitute for
our TBTS measure by using the ratio of total assets (rather than liabilities) to GDP as a
measure of bank size (see Table A4 in the Appendix). The results are again very similar.
As before, we find a highly significant effect of banks’ systemic importance and similar
effects of the TBTS variable and its interaction with the home country’s debt ratio. The
only difference is that, at least in column (1), bank size has a significantly positive effect
10
In order to facilitate the interpretation, we demeaned the variable TBTS in our regressions.
17
Table 4: Baseline regression with bank fixed effects and time fixed effects
VARIABLES
(1)
CDS
(2)
CDS
(3)
CDS
(4)
CDS
(5)
CDS
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
-10.82***
(2.48e-07)
-10.83***
(2.53e-07)
-10.34***
(1.24e-06)
-11.47***
(4.24e-06)
-11.42***
(5.41e-06)
37.64
(0.115)
40.94
(0.294)
-13.47
(0.717)
-28.89
(0.448)
-34.16
(0.383)
-0.851
(0.925)
T BT S · Debtratio
1.422*
(0.0803)
1.625*
(0.0850)
1.813*
(0.0654)
Debtratio
2.030**
(0.0411)
-0.565
(0.702)
-1.009
(0.545)
Bondyield
7.968**
(0.0142)
7.677**
(0.0197)
Domesticcredit/GDP
-1.179*
(0.0896)
-1.307*
(0.0720)
GDP growth
-4.500
(0.181)
-4.657
(0.170)
Reserves/GDP
-0.306
(0.882)
-0.573
(0.766)
−V aR
0.246
(0.634)
0.230
(0.657)
ROAA
-1.209
(0.717)
Leverage
0.864
(0.165)
EarningP ower
1.983
(0.730)
-0.268
(0.653)
−∆CoV aR
T BT S
T BT S 2
18
Constant
-0.436
Observations
4,263
4,263
3,839
3,473
3,417
R-squared
0.446
0.446
0.439
0.469
0.470
76
76
72
66
65
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Number of Banks
Bank FE
Time FE
(0.395)
-0.434
(0.397)
-0.629
(0.243)
-0.409
(0.502)
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects. Standard errors are clustered at the country level
throughout. p-value are given in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. TBTS is demeaned before taking first
differences. Sample sizes of different specifications differ due to data availability.
on CDS spreads, which is in line with a TBTS story. However, once we control for the
home country’s debt level and the TBTS interaction, we find the same interplay between
bank size and sovereign debt as before. The results regarding the other coefficients are
qualitatively identical. Finally, we additionally include the logarithm of total bank assets
in the regressions in order to capture the effect of absolute size (rather than just relative
size). Independent of a bank’s size relative to GDP, market participants may consider a
bank as being too big to fail due to its absolute size. The results presented in Table A5
in the Appendix do not support that view. Absolute size has no significant effect on CDS
spreads. The remaining results are again qualitatively similar to the earlier regressions.
5
Double Wake-Up Call
Within our sample period, one can identify two key events that fundamentally changed
the financial system: the onset of the financial crisis in 2007 and the failure of the US bank
Lehman Brothers in 2008. In order to check for potential structural breaks, we divide our
sample period into three sub-periods: first, a pre-crisis period from 2005 until July 2007;
second, the crisis period from August 2007 11 until shortly before the meltdown of Lehman
Brothers, i. e., until August 2008; finally, the post-Lehman period from September 2008
till the end of our sample. Note that the Lehman failure coincides with the trouble in
the Icelandic banking system. We run two of our baseline regressions (namely those in
columns (4) and (5) of Table 4), interacting all variables with the dummy variables for
each sub-period. The results are shown in Tables 5a, 5b, and 5c.12
In the pre-crisis period (see Table 5a), no bank-specific variable has a significant effect on
bank CDS spreads. Only domestic credit and GDP growth have the expected negative
signs and are statistically significant. This indicates that individual bank risk was not
priced before the crisis, implying that CDS spreads were largely flat.
However, with the beginning of the financial crisis, we find highly significant and large
negative coefficients of banks’ returns (ROAA) and of our measure of systemic importance
(−∆CoV aR), indicating that markets now became aware of banks’ risks and priced in a
11
Most observers date the beginning of the financial crisis on August 9, 2007.
To facilitate comparisons of coefficients, Table A8 in the Appendix shows the same results in one
table. Tables 5b and 5c additionally present test results for a change in coefficients across subperiods.
12
19
Table 5a: Pre-crisis, crisis, and post-Lehman analysis
(1)
CDS
VARIABLES
(2)
CDS
Panel A: Pre-crisis period
coef.
p-value
coef.
p-value
(pre-crisis)−∆CoV aR
-1.387
(0.425)
-0.885
(0.574)
(pre-crisis)T BT S
-3.859
(0.949)
17.98
(0.718)
(pre-crisis)T BT S · Debtratio
-0.941
(0.198)
-0.876
(0.105)
(pre-crisis)Debtratio
-0.544
(0.709)
-0.350
(0.769)
(pre-crisis)Bondyield
0.995
(0.710)
0.707
(0.794)
(pre-crisis)Domesticcredit/GDP
-1.567*** (0.00491)
-1.333*** (0.00738)
(pre-crisis)GDP growth
-8.771*** (0.00594)
-7.788*** (0.00246)
20
(pre-crisis)Reserves/GDP
-3.077
(0.132)
-3.083*
(0.0953)
(pre-crisis)−V aR
-0.0260
(0.866)
0.0412
(0.805)
(pre-crisis)ROAA
14.59
(0.125)
(pre-crisis)Leverage
0.731
(0.263)
(pre-crisis)EarningP ower
-11.99
(0.180)
OLS regressions in first differences for equation 7 with bank fixed effects and time
fixed effects and interacting all variables with a time dummy. (pre-crisis) indicates
a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy, which
equals 1 for the period August 2007 to August 2008. (post-Lehman) indicates a
Dummy, which equals 1 after September 2008. Panel A shows the pre-crisis period,
Table 5b and Table 5c show the results of the same regression for the crisis and the
post-Lehman period. Table A8 summarizes Tables 5a, 5b, and 5c without showing
differences to previous periods. Standard errors are clustered at the country level
throughout. p-value in parentheses. ***, **, * indicate significance at the 1%, 5%,
and 10% levels. TBTS is demeaned before taking first differences. Sample sizes for
different specifications differ due to data availability.
lower default probability of systemically relevant institutions (see Table 5b). In contrast,
sovereign debt problems do not seem to have mattered in this subperiod.
In the last subperiod (see Table 5c), CDS spreads are significantly increasing in banks’
size for countries above some (higher than before, but still moderate) threshold of the debt
ratio. The effect of bank size increases in the home country’s debt ratio. Since this effect
appears only after the large-scale bank bail-outs and the Icelandic events, it seems that
markets only now recognized that banks may not only be TSTF, but also TBTS. We also
find a significant effect of the government bond yield, which further supports the TBTS
hypothesis. Banks in countries with lower funding costs supposedly are better able to bail
out a bank in distress. Moreover, −V aR now has a significantly positive impact on CDS
spreads, and −∆CoV aR has a reduced impact on CDS spreads compared to the crisis
period (although this difference is not statistically significant, as can be seen from the
second and fourth column of Table 5c). This may reflect vanishing bail-out expectations
in the light of rising sovereign debt problems. It may also partly be due to the non-bailout of Lehman Brothers, which put in question the 100% bail-out guarantee for systemic
institutions and raised expectations of a tightening of future banking regulation (such as
the introduction of bank resolution procedures), especially for systemic institutions.
The events can thus be seen as a double wake-up call. While the onset of the crisis in
2007 reminded investors of the risks in banking and of the fact that some banks were more
likely to be bailed out than others, the Icelandic crisis showed that government resources
may not be sufficient to bail out even systemic institutions.
We again checked the robustness of results by using a different level of winsorization and
by defining TBTS on the basis of bank total assets rather than liabilities. The results
are shown in Tables A6 and A7. The results are almost identical to those presented
above. In particular, bank risk was not priced before the crisis, systemic risk and ROAA
become significant in the crisis, and debt problems become important only in the postLehman period. One notable difference is found in Table A6, where −∆CoV aR becomes
insignificant in the post-Lehman period, indicating a reduction of the TSTF problem with
the emergence of sovereign debt problems.
The overall message remains: The two crisis events in August 2007 and September 2008
fundamentally changed the relationship between banks’ CDS spreads and bank- and
country-specific variables, especially those related to the TSTF and TBTS problems.
21
Table 5b: Pre-crisis, crisis, and post-Lehman analysis
Panel B: Crisis period
VARIABLES
(1)
CDS
(2)
CDS
(3)
CDS
(4)
CDS
Difference to pre-crisis period
coef.
(crisis)−∆CoV aR
p-value
coef.
-17.61*** (0.000302) -16.22***
p-value
(0.00114)
Difference to pre-crisis period
coef.
p-value
coef.
-17.28*** (0.000237) -16.39***
p-value
(0.000592)
(crisis)T BT S
95.78
(0.181)
99.64
(0.429)
97.71
(0.131)
79.73
(0.449)
(crisis)T BT S · Debtratio
-0.361
(0.760)
0.580
(0.705)
0.592
(0.666)
1.468
(0.362)
(crisis)Debtratio
3.228
(0.185)
3.772
(0.135)
2.730
(0.210)
3.080
(0.174)
(crisis)Bondyield
-1.567
(0.857)
-2.561
(0.757)
-1.731
(0.852)
-2.438
(0.782)
-1.632***
(0.00163)
-0.0647
(0.921)
-1.657**
(0.0213)
-0.324
(0.674)
(crisis)GDP growth
6.107
(0.389)
14.88*
(0.0510)
5.029
(0.525)
12.82
(0.115)
(crisis)Reserves/GDP
-1.890
(0.381)
1.187
(0.723)
-2.118
(0.376)
0.965
(0.761)
(crisis)−V aR
-1.348
(0.231)
-1.322
(0.240)
-1.434
(0.188)
-1.475
(0.183)
-34.58*
(0.0531)
-49.17**
(0.0219)
(crisis)Leverage
1.166
(0.181)
0.434
(0.749)
(crisis)EarningP ower
13.11
(0.248)
25.11*
(0.0967)
(crisis)Domesticcredit/GDP
22
(crisis)ROAA
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects and interacting all variables with a time
dummy. (pre-crisis) indicates a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy, which equals 1 for the period
August 2007 to August 2008. (post-Lehman) indicates a Dummy, which equals 1 after September 2008. Column (1) and (3) in Panel
B shows the crisis period, Table 5a and Table 5c show the results of the same regression for the pre-crisis and the post-Lehman period.
Column (2) and (4) indicate the difference to the pre-crisis period. Table A8 summarizes Tables 5a, 5b, and 5c without showing differences
to previous periods. Standard errors are clustered at the country level throughout. p-value in parentheses. ***, **, * indicate significance
at the 1%, 5%, and 10% levels. TBTS is demeaned before taking first differences. Sample sizes for different specifications differ due to data
availability.
Table 5c: Pre-crisis, crisis, and post-Lehman analysis
Panel C: Post-Lehman period
VARIABLES
(1)
CDS
(2)
CDS
(3)
CDS
(4)
CDS
Difference to crisis period
(post-Lehman)−∆CoV aR
(post-Lehman)T BT S
(post-Lehman)T BT S · Debtratio
(post-Lehman)Debtratio
(post-Lehman)Bondyield
Difference to crisis period
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
-8.398**
(0.0353)
9.209
(0.171)
-8.341**
(0.0390)
8.934
(0.177)
-99.49
(0.204)
-195.3**
(0.0226)
-112.2
(0.168)
-209.9**
(0.0155)
3.499**
(0.0161)
3.860**
(0.0129)
3.441**
(0.0180)
2.849*
(0.0778)
-0.892
(0.598)
-4.120
(0.182)
-1.242
(0.527)
-3.972
(0.187)
12.21
(0.204)
12.17
(0.238)
10.64*** (0.00524)
10.44*** (0.00669)
23
(post-Lehman)Domesticcredit/GDP
0.323
(0.753)
1.954**
(0.0266)
0.225
(0.809)
1.882**
(0.0174)
(post-Lehman)GDP growth
-3.432
(0.367)
-9.539
(0.224)
-3.575
(0.340)
-8.604
(0.273)
(post-Lehman)Reserves/GDP
1.183
(0.681)
3.074
(0.331)
1.625
(0.600)
3.743
(0.306)
2.173**
(0.0186)
3.521**
(0.0221)
2.135**
(0.0170)
3.569**
(0.0156)
(post-Lehman)ROAA
-0.436
(0.927)
34.14*
(0.0839)
(post-Lehman)Leverage
0.188
(0.838)
-0.977
(0.432)
(post-Lehman)EarningP ower
0.794
(0.913)
-12.32
(0.392)
(post-Lehman)−V aR
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects and interacting all variables with a time
dummy. (pre-crisis) indicates a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy, which equals 1 for the period
August 2007 to August 2008. (post-Lehman) indicates a Dummy, which equals 1 after September 2008. Column (1) and (3) in Panel
C shows the post-Lehman period, Table 5a and Table 5b show the results of the same regression for the pre-crisis and the crisis period.
Column (2) and (4) indicate the difference to the crisis period. Table A8 summarizes Tables 5a, 5b, and 5c without showing differences
to previous periods. Standard errors are clustered at the country level throughout. p-value in parentheses. ***, **, * indicate significance
at the 1%, 5%, and 10% levels. TBTS is subtracted by its mean before taking the first difference. Sample sizes for different specifications
differ due to data availability.
6
Conclusion
This paper has analyzed the effect of systemic relevance as well as size on banks’ CDS
spreads. We argue that bank size is not a satisfactory measure of systemic risk and differentiate the effect associated with bank size from the effect arising from the institution’s
systemic importance. To this end, we control for systemic risk using the ∆CoV aR measure introduced by Adrian and Brunnermeier (2011). Controlling for systemic risk, we
then identify the too-big-to-save phenomenon by the effect of a bank’s size relative to
GDP. We further test whether the too-big-to-save effect depends on the home country’s
fiscal situation.
We find a significant decrease in CDS spreads for more systemically relevant institutions,
which is consistent with the too-systemic-to-fail hypothesis. Markets expect that banks
that are highly systemically relevant are more likely to receive governmental support in
case of financial distress. Moreover, we find no significant effect of bank size in countries
with a zero debt level, once we control for systemic risk. However, the effect of bank
size increases in the home country’s debt ratio, and the overall effect of relative bank
size becomes positive already at moderate debt ratios. This result is consistent with the
too-big-to-save hypothesis. Market participants appear to expect that, in countries with
limited debt capacity, a bail-out is less likely the more funding is needed for it.
We further show that the relevance of TSTF and TBTS changes over time. Before the
financial crisis started in August 2007, we find neither a significant effect of TSTF nor
of TBTS. However, we find a strong and significant effect of systemic relevance after
the onset of the financial crisis, supporting the relevance of the TSTF problem. The
increased awareness of the TSTF problem may have resulted from the large number of
bail-outs (before Lehman), which were justified with the prevention of contagion effects.
In contrast, TBTS mattered only in the post-Lehman period when we find a significant
increase in CDS spreads for larger banks in countries above some moderate threshold of
the home country’s debt ratio.
We interpret this sequence of events as a double wake-up call for investors. With the
advent of the financial crisis in 2007, investors were reminded of the risks in banking and
of the fact that some banks were more likely to be bailed out than others. The Icelandic
crisis then showed that government resources are not always sufficient to bail out systemic
institutions. The recognition of the TBTS problem mitigated the TSTF problem.
24
Hence, our results suggest that banks are not too big to fail, but they may be too systemic
to fail and too big to save. Rather than being constant over time, the relative importance
of these problems depends on economic circumstances. Even small banks may become
too systemic to fail in a financial crisis. And even a small bank may become too big to
save if the bank’s home country is burdened with high levels of debt.
25
References
Acharya, V. V., and T. Yorulmazer (2008): “Information Contagion and Bank
Herding,” Journal of Money, Credit and Banking, 40(1), pp. 215–231.
Adrian, T., and M. K. Brunnermeier (2011): “CoVaR,” Discussion paper.
Allen, F., and D. Gale (2000): “Financial Contagion,” Journal of Political Economy,
108(1), pp. 1–33.
Boyd, J. H., and M. Gertler (1994): “The Role of Large Banks in the Recent U.S.
Banking Crisis,” Federal Reserve Bank of Minneapolis Quarterly Review, 18(1), 2–21.
Brunnermeier, M. K., and L. H. Pedersen (2009): “Market Liquidity and Funding
Liquidity,” The Review of Financial Studies, 22(6), pp. 2201–2238.
Buiter, W., and A. Sibert (2008): “The Icelandic banking crisis and what to do about
it,” CEPR Policy Insight, (26).
Chen, Y. (1999): “Banking Panics: The Role of the First-Come, First-Served Rule and
Information Externalities,” Journal of Political Economy, 107(5), pp. 946–968.
Demirgüç-Kunt, A., and H. Huizinga (2010): “Are banks too big to fail or too big to
save? International evidence from equity prices and CDS spreads,” Discussion paper.
Diamond, D. W., and P. H. Dybvig (1983): “Bank Runs, Deposit Insurance, and
Liquidity,” Journal of Political Economy, 91(3), pp. 401–419.
European Central Bank (2008): Recommendations of the Governing Council of the
European Central Bank on government guarantees for bank debt.
Gropp, R., H. Hakenes, and I. Schnabel (2010): “Competition, Risk-shifting, and
Public Bail-out Policies,” Review of Financial Studies, 24(6), pp. 2084–2120.
Hellwig, M. (1998): “Too big to rescued,” cited in: Schweizer Bank Nr. 11 vom November 1998.
Hüpkes, E. H. (2005): ““Too Big to Save” - Towards a Functional Approach to Resolving
Crises in Global Financial Institutions,” in Systemic Financial Crisis: Resolving large
bank insolvencies, pp. 193–215. Douglas Evanoff and George Kaufman, eds.
26
Kaufman, G. G. (2002): “Too big to fail in banking: What remains?,” The Quarterly
Review of Economics and Finance, 42(3), 423–436.
Rime, B. (2005): “Do “too big to fail” expectations boost large banks issuer ratings?,”
Swiss National Bank Working Paper.
Stern, G., and R. Feldman (2004): Too big to fail: the hazards of bank bailouts, G
- Reference, Information and Interdisciplinary Subjects Series. Brookings Institution
Press.
Swiss National Bank (2008): Financial Stability Report.
The Central Bank of Iceland (2009): Financial Stability Report.
Völz, M., and M. Wedow (2011): “Market discipline and too-big-to-fail in the CDS
market: Does banks’ size reduce market discipline?,” Journal of Empirical Finance,
18(2), pp. 195 – 210.
Zhou, C. (2010): “Are Banks Too Big to Fail? Measuring Systemic Importance of
Financial Institutions,” International Journal of Central Banking, 6(34), 205–250.
27
A
Appendix A
Table A1: Description of variable construction and data sources
Variable name
Description
Data source
CDS
Single name 5-year senior CDS, winsorized at 5/95%
Datastream
∆CoV aR
Conditional VaR of market valued total financial assets, see definition in text and Adrian and Brunnermeier (2011), winsorized at 5/95%, multiplied by −1
throughout the paper
Own calculation
TBTS
Total liabilities / current GDP (in %)
BankScope & WDI
TBTS Assets
Total assets / current GDP (in %)
BankScope & WDI
Debt ratio
Central government debt / GDP (in %)
WDI
Bondyield
Yield on a government bond with a maturity of 10
years (in %)
Datastream
Domestic credit / GDP
Domestic credit provided by banking sector / GDP
(in %)
WDI
GDP growth
Annual growth rate of GDP (in %)
WDI
Reserves / GDP
Total reserves (includes gold) (in % of GDP $)
WDI
VaR
Unconditional VaR of market valued total financial assets, winsorized at 5/95%, multiplied by −1
throughout the paper
Own calculation
ROAA
Return on average assets
BankScope
Leverage
Total assets / equity
BankScope
Earning Power
Return on asset performance without deducting provisions for bad debt as percentage of total assets
BankScope
ln(Total assets)
Log of total assets
BankScope
for the VaR and CoVaR calculation:
VIX
Implied volatility index
Chicago Board Options Exchange
Repospread
Difference between the 3-month repo rate and the
3-month bill rate
Bloomberg & Federal Reserve Board
H.15 release
Termchange
Change in 3-month term Treasury bill
Federal
Reserve
Board H.15 release
Yieldchange
Change in the difference of the 10-year Treasury rate
and the 3-month bill rate
Federal
Reserve
Board H.15 release
Creditchange
Change in the credit spread between BAA rated
bonds and Treasury rate (with same maturity of 10
years)
Federal
Reserve
Board H.15 release
28
Details on data construction All data with daily frequency were winsorized at 5/95%
(or 2.5/97.5% in the robustness check) before calculating monthly averages. For all data
with yearly frequency, we use cubic spline interpolation to get monthly observations. The
risk measures ∆CoV aR and V aR are multiplied by −1, such that a larger value indicates a
higher contribution to systemic risk and a larger individual risk, respectively. For one bank
in our sample, Glitnir, we observe a negative value of equity. Since this would correspond
to an infinite leverage, we set the leverage for this bank to the maximum observed leverage
in the remaining sample. For the same bank, we further observe implausibly large negative
values of the return on average asset. Thus, we dropped eight observations with a return
on average assets smaller than −75. In all regression tables, we use the demeaned form
of the TBTS variables (total liabilities / GDP or total assets / GDP).
29
Table A2: List of countries and banks in our sample
Australia
1
2
3
4
5
Austria
6
7
Belgium
8
9
10
China
11
Denmark
12
France
13
14
15
16
Germany
17
18
19
20
Iceland
21
22
23
India
24
25
26
Ireland
27
28
29
Italy
30
31
32
33
Japan
34
35
36
37
38
Commonwealth
Macquarie
National Australia Bank
St. George
Westpac Bank
Erste Group Bank
Raiffeisenbank
Ageas Fortis
Dexia
KBC Bank
Bank of China
Danske Bank
BNP Paribas
Credit Agricole
Natixis
Societe Generale
Commerzbank
Deutsche Bank
IKB
Bayerische Hypovereinsbank
Glitnir
Kaupthing
Landsbanki
Bank of India
Icici Bank
Statebank of India
Allied Irish Banks
Anglo Irish Bank
Bank of Ireland
Banca Italease
Intesa Sao Paolo
Banca Monte dei Paschi
Unicredito
Mitsubishi UFJ
Mizuho Bank
Resona
Shinsei Bank
Sumitomo Mitsui
Malaysia
39
Netherlands
40
41
42
Norway
43
Portugal
44
45
Republic of Korea
46
47
48
Singapore
49
50
Spain
51
52
53
Sweden
54
55
56
Switzerland
57
58
United Kingdom
59
60
61
62
63
64
65
USA
66
67
68
69
70
71
72
73
74
75
76
30
Malayan Bank
ING Bank
Rabobank
SNS Bank
DNB Bank
Banco Comercial de Portugues
Banco Espirito Santo
Industrial Bank of Korea
Shinhan Bank
Woori Bank
Oversea Chinese Bank
United Overseas Bank
Banco Sabadell
Banco Santander
Bankinter
Nordea
Svenska
Swedbank
Credit Suisse
UBS
Abbey Bank
Alliance Leicester
Barclays
HBOS
Lloyds
Royal Bank of Scotland
Standard Chartered Bank
Bank of America
Bear Stearns
Capital One Bank
Citigroup
Goldman Sachs
JP Morgan Chase
Merill Lynch
Morgan Stanley
Wachovia
Washington Mutual
Wells Fargo
Table A3: Baseline regression with bank fixed effects and time fixed effects using a 2.5% winsorization for the risk measures
∆CoV aR and V aR
VARIABLES
(1)
CDS
(2)
CDS
(3)
CDS
(4)
CDS
(5)
CDS
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
-9.568***
(3.87e-05)
-9.569***
(3.90e-05)
-8.530***
(8.86e-05)
-9.101***
(0.000419)
-9.088***
(0.000474)
36.88
(0.116)
40.14
(0.296)
-15.35
(0.680)
-30.85
(0.414)
-36.15
(0.352)
-0.840
(0.926)
T BT S · Debtratio
1.424*
(0.0786)
1.629*
(0.0812)
1.819*
(0.0629)
Debtratio
2.021**
(0.0418)
-0.602
(0.683)
-1.065
(0.521)
Bondyield
8.084**
(0.0138)
7.793**
(0.0191)
Domesticcredit/GDP
-1.190*
(0.0870)
-1.316*
(0.0695)
GDP growth
-4.455
(0.182)
-4.604
(0.170)
Reserves/GDP
-0.294
(0.887)
-0.555
(0.774)
−V aR
0.348
(0.558)
0.360
(0.557)
ROAA
-1.252
(0.705)
Leverage
0.858
(0.169)
EarningP ower
2.049
(0.720)
0.274
(0.706)
−∆CoV aR
T BT S
T BT S 2
31
Constant
-0.252
Observations
4,263
4,263
3,839
3,473
3,417
R-squared
0.446
0.446
0.439
0.468
0.469
76
76
72
66
65
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Number of Banks
Bank FE
Time FE
(0.584)
-0.250
(0.587)
-0.324
(0.560)
0.117
(0.872)
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects. Standard errors are clustered at the country level throughout.
p-value are given in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. TBTS is demeaned before taking first differences. Sample sizes of
different specifications differ due to data availability.
Table A4: Baseline regression with bank fixed effects and time fixed effects using the ratio of bank total assets to GDP as a measure
of bank’s size
(1)
CDS
(2)
CDS
(3)
CDS
(4)
CDS
(5)
CDS
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
-10.82***
(2.45e-07)
-10.82***
(2.48e-07)
-10.32***
(1.24e-06)
-11.46***
(4.32e-06)
-11.41***
(5.42e-06)
38.38*
(0.0918)
45.34
(0.254)
-12.71
(0.729)
-26.36
(0.476)
-31.76
(0.400)
-1.784
(0.839)
T BT S asset · Debtratio
1.351*
(0.0920)
1.462
(0.104)
1.696*
(0.0611)
Debtratio
1.995**
(0.0435)
-0.520
(0.724)
-0.997
(0.549)
Bondyield
8.004**
(0.0136)
7.691**
(0.0195)
Domesticcredit/GDP
-1.168*
(0.0910)
-1.318*
(0.0706)
GDP growth
-4.460
(0.185)
-4.608
(0.174)
Reserves/GDP
-0.326
(0.875)
-0.597
(0.758)
−V aR
0.250
(0.628)
0.231
(0.656)
ROAA
-0.497
(0.882)
Leverage
0.921
(0.141)
EarningP ower
1.835
(0.750)
-0.261
(0.659)
−∆CoV aR
T BT S assets
T BT S assets2
32
Constant
-0.431
Observations
4,263
4,263
3,839
3,473
3,417
R-squared
0.446
0.446
0.439
0.469
0.470
76
76
72
66
65
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Number of Banks
Bank FE
Time FE
(0.401)
-0.427
(0.408)
-0.609
(0.259)
-0.394
(0.517)
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects. Standard errors are clustered at the country level
throughout. p-value in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. TBTS is subtracted by its mean before taking
the first difference. Sample sizes for different specifications differ due to data availability.
Table A5: Baseline regression with bank fixed effects and time fixed effects including total assets
VARIABLES
(1)
CDS
coef.
−∆CoV aR
(2)
CDS
p-value
coef.
(3)
CDS
p-value
coef.
(4)
CDS
p-value
coef.
-10.77*** (2.66e-07) -10.31*** (1.40e-06) -11.48*** (4.50e-06) -11.46***
T BT S
125.2
(0.116)
T BT S 2
-13.02
(0.323)
p-value
(5.70e-06)
4.531
(0.910)
-8.702
(0.811)
-9.020
(0.806)
T BT S · Debtratio
1.646*
(0.0845)
1.876*
(0.0807)
2.179*
(0.0534)
Debtratio
1.943*
(0.0500)
-0.588
(0.685)
-0.959
(0.556)
-46.57
(0.267)
-50.95
(0.232)
-65.25
(0.133)
Bondyield
8.007**
(0.0132)
7.706**
(0.0184)
Domesticcredit/GDP
-1.192*
(0.0850)
-1.338*
(0.0654)
GDP growth
-4.376
(0.166)
-4.545
(0.155)
Reserves/GDP
-0.885
(0.653)
-1.385
(0.456)
−V aR
0.262
(0.612)
0.250
(0.628)
ROAA
-1.708
(0.606)
Leverage
1.010*
(0.0693)
EarningP ower
2.229
(0.701)
0.0858
(0.904)
ln(T otalassets)
-71.89
(0.149)
33
Constant
0.0955
Observations
4,263
3,839
3,473
3,417
R-squared
0.447
0.440
0.469
0.471
76
72
66
65
Number of Banks
(0.870)
-0.316
(0.539)
-0.138
(0.853)
Bank FE
YES
YES
YES
YES
Time FE
YES
YES
YES
YES
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects. Standard errors are clustered at the
country level throughout. p-value in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. TBTS is subtracted
by its mean before taking the first difference. Total assets equals the first difference of the log of total assets. Sample sizes for different
specifications differ due to data availability.
Table A6: Pre-crisis, in-crisis, and post-Lehman analysis using 2.5% winsorization for the
risk measures ∆CoV aR and V aR
(1)
∆CDS
(2)
∆CDS
(pre-crisis)−∆CoV aR
(pre-crisis)T BT S
(pre-crisis)T BT S · Debtratio
(pre-crisis)Debtratio
(pre-crisis)Bondyield
(pre-crisis)Domesticcredit/GDP
(pre-crisis)GDP growth
(pre-crisis)Reserves/GDP
(pre-crisis)−V aR
(pre-crisis)ROAA
(pre-crisis)Leverage
(pre-crisis)EarningP ower
-1.664
-4.278
-0.905
-0.653
0.964
-1.587***
-8.684***
-3.018
0.0637
(0.330)
(0.943)
(0.219)
(0.658)
(0.717)
(0.00482)
(0.00668)
(0.148)
(0.618)
-1.120
17.57
-0.835
-0.460
0.679
-1.349***
-7.659***
-3.022
0.117
14.66
0.726
-11.95
(0.463)
(0.725)
(0.127)
(0.704)
(0.800)
(0.00722)
(0.00315)
(0.110)
(0.391)
(0.122)
(0.269)
(0.182)
(crisis)−∆CoV aR
(crisis)T BT S
(crisis)T BT S · Debtratio
(crisis)Debtratio
(crisis)Bondyield
(crisis)Domesticcredit/GDP
(crisis)GDP growth
(crisis)Reserves/GDP
(crisis)−V aR
(crisis)ROAA
(crisis)Leverage
(crisis)EarningP ower
-16.05***
96.07
-0.388
3.199
-1.832
-1.635***
6.116
-1.879
-1.422
(0.000401)
(0.182)
(0.741)
(0.187)
(0.835)
(0.00166)
(0.387)
(0.378)
(0.124)
-16.00***
98.09
0.567
2.696
-1.952
-1.660**
4.991
-2.110
-1.401
-34.61*
1.182
13.10
(0.000258)
(0.130)
(0.678)
(0.213)
(0.835)
(0.0222)
(0.526)
(0.375)
(0.107)
(0.0521)
(0.177)
(0.249)
(post-Lehman)−∆CoV aR
(post-Lehman)T BT S
(post-Lehman)T BT S · Debtratio
(post-Lehman)Debtratio
(post-Lehman)Bondyield
(post-Lehman)Domesticcredit/GDP
(post-Lehman)GDP growth
(post-Lehman)Reserves/GDP
(post-Lehman)−V aR
(post-Lehman)ROAA
(post-Lehman)Leverage
(post-Lehman)EarningP ower
-4.984
-107.0
3.578**
-1.039
10.64***
0.345
-3.190
0.975
2.037*
(0.210)
(0.165)
(0.0128)
(0.541)
(0.00512)
(0.733)
(0.396)
(0.731)
(0.0913)
-4.942
-119.0
3.519**
-1.384
10.46***
0.265
-3.324
1.415
2.008*
-0.449
0.125
0.668
(0.222)
(0.138)
(0.0153)
(0.484)
(0.00656)
(0.773)
(0.369)
(0.641)
(0.0942)
(0.925)
(0.892)
(0.927)
Constant
0.611
(0.412)
0.732
(0.256)
Observations
R-squared
Number of Banks
Bank FE
Time FE
3,473
0.476
66
YES
YES
3,417
0.479
65
YES
YES
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects.
(pre-crisis) indicates a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy,
which equals 1 for the period August 2007 to August 2008. (post-Lehman) indicates a Dummy,
which equals 1 after September 2008. Standard errors are clustered at the country level throughout.
p-value in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. Sample
sizes for different specifications differ due to data availability.
34
Table A7: Pre-crisis, in-crisis, and post-Lehman analysis using the ratio of bank total
assets to GDP as a measure of bank’s size
(1)
∆CDS
(2)
∆CDS
(pre-crisis)−∆CoV aR
(pre-crisis)T BT S assets
(pre-crisis)T BT S assets · Debtratio
(pre-crisis)Debtratio
(pre-crisis)Bondyield
(pre-crisis)Domesticcredit/GDP
(pre-crisis)GDP growth
(pre-crisis)Reserves/GDP
(pre-crisis)−V aR
(pre-crisis)ROAA
(pre-crisis)Leverage
(pre-crisis)EarningP ower
-1.394
-6.174
-0.801
-0.544
0.966
-1.575***
-8.728***
-3.055
-0.0277
(0.423)
(0.918)
(0.265)
(0.712)
(0.721)
(0.00500)
(0.00549)
(0.130)
(0.858)
-0.891
17.05
-0.785
-0.343
0.667
-1.335***
-7.790***
-3.057*
0.0404
14.50
0.712
-11.95
(0.572)
(0.731)
(0.144)
(0.775)
(0.806)
(0.00706)
(0.00256)
(0.0925)
(0.809)
(0.130)
(0.276)
(0.182)
(crisis)−∆CoV aR
(crisis)T BT S assets
(crisis)T BT S assets · Debtratio
(crisis)Debtratio
(crisis)Bondyield
(crisis)Domesticcredit/GDP
(crisis)GDP growth
(crisis)Reserves/GDP
(crisis)−V aR
(crisis)ROAA
(crisis)Leverage
(crisis)EarningP ower
-17.61***
92.84
-0.408
3.321
-1.410
-1.615***
6.163
-1.895
-1.346
(0.000301)
(0.171)
(0.714)
(0.173)
(0.871)
(0.00171)
(0.384)
(0.380)
(0.232)
-17.26***
94.32
0.590
2.724
-1.604
-1.640**
5.300
-2.108
-1.435
-34.89**
1.217
12.95
(0.000240)
(0.121)
(0.633)
(0.205)
(0.863)
(0.0214)
(0.499)
(0.377)
(0.187)
(0.0500)
(0.156)
(0.255)
(post-Lehman)−∆CoV aR
(post-Lehman)T BT S assets
(post-Lehman)T BT S assets · Debtratio
(post-Lehman)Debtratio
(post-Lehman)Bondyield
(post-Lehman)Domesticcredit/GDP
(post-Lehman)GDP growth
(post-Lehman)Reserves/GDP
(post-Lehman)−V aR
(post-Lehman)ROAA
(post-Lehman)Leverage
(post-Lehman)EarningP ower
-8.303**
-95.44
3.474**
-0.856
10.79***
0.397
-3.311
0.907
2.186**
(0.0372)
(0.221)
(0.0177)
(0.615)
(0.00454)
(0.705)
(0.387)
(0.748)
(0.0177)
-8.306**
-109.3
3.469**
-1.224
10.52***
0.219
-3.510
1.488
2.129**
0.845
0.288
0.478
(0.0394)
(0.176)
(0.0146)
(0.532)
(0.00631)
(0.814)
(0.352)
(0.629)
(0.0174)
(0.857)
(0.754)
(0.947)
Constant
0.608
(0.424)
0.715
(0.269)
Observations
R-squared
Number of Banks
Bank FE
Time FE
3,473
0.476
66
YES
YES
3,417
0.479
65
YES
YES
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects.
(pre-crisis) indicates a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy,
which equals 1 for the period August 2007 to August 2008. (post-Lehman) indicates a Dummy,
which equals 1 after September 2008. Standard errors are clustered at the country level throughout.
p-value in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. Sample
sizes for different specifications differ due to data availability.
35
Table A8: Pre-crisis, in-crisis, and post-Lehman analysis (summary of Tables 5a, 5b, and
5c)
(1)
∆CDS
(2)
∆CDS
(pre-crisis)−∆CoV aR
(pre-crisis)T BT S
(pre-crisis)T BT S · Debtratio
(pre-crisis)Debtratio
(pre-crisis)Bondyield
(pre-crisis)Domesticcredit/GDP
(pre-crisis)GDP growth
(pre-crisis)Reserves/GDP
(pre-crisis)−V aR
(pre-crisis)ROAA
(pre-crisis)Leverage
(pre-crisis)EarningP ower
-1.387
-3.859
-0.941
-0.544
0.995
-1.567***
-8.771***
-3.077
-0.0260
(0.425)
(0.949)
(0.198)
(0.709)
(0.710)
(0.00491)
(0.00594)
(0.132)
(0.866)
-0.885
17.98
-0.876
-0.350
0.707
-1.333***
-7.788***
-3.083*
0.0412
14.59
0.731
-11.99
(0.574)
(0.718)
(0.105)
(0.769)
(0.794)
(0.00738)
(0.00246)
(0.0953)
(0.805)
(0.125)
(0.263)
(0.180)
(crisis)−∆CoV aR
(crisis)T BT S
(crisis)T BT S · Debtratio
(crisis)Debtratio
(crisis)Bondyield
(crisis)Domesticcredit/GDP
(crisis)GDP growth
(crisis)Reserves/GDP
(crisis)−V aR
(crisis)ROAA
(crisis)Leverage
(crisis)EarningP ower
-17.61***
95.78
-0.361
3.228
-1.567
-1.632***
6.107
-1.890
-1.348
(0.000302)
(0.181)
(0.760)
(0.185)
(0.857)
(0.00163)
(0.389)
(0.381)
(0.231)
-17.28***
97.71
0.592
2.730
-1.731
-1.657**
5.029
-2.118
-1.434
-34.58*
1.166
13.11
(0.000237)
(0.131)
(0.666)
(0.210)
(0.852)
(0.0213)
(0.525)
(0.376)
(0.188)
(0.0531)
(0.181)
(0.248)
(post-Lehman)−∆CoV aR
(post-Lehman)T BT S
(post-Lehman)T BT S · Debtratio
(post-Lehman)Debtratio
(post-Lehman)Bondyield
(post-Lehman)Domesticcredit/GDP
(post-Lehman)GDP growth
(post-Lehman)Reserves/GDP
(post-Lehman)−V aR
(post-Lehman)ROAA
(post-Lehman)Leverage
(post-Lehman)EarningP ower
-8.398**
-99.49
3.499**
-0.892
10.64***
0.323
-3.432
1.183
2.173**
(0.0353)
(0.204)
(0.0161)
(0.598)
(0.00524)
(0.753)
(0.367)
(0.681)
(0.0186)
-8.341**
-112.2
3.441**
-1.242
10.44***
0.225
-3.575
1.625
2.135**
-0.436
0.188
0.794
(0.0390)
(0.168)
(0.0180)
(0.527)
(0.00669)
(0.809)
(0.340)
(0.600)
(0.0170)
(0.927)
(0.838)
(0.913)
Constant
0.573
(0.448)
0.700
(0.282)
Observations
R-squared
Number of Banks
Bank FE
Time FE
3,473
0.476
66
YES
YES
3,417
0.479
65
YES
YES
OLS regressions in first differences for equation 7 with bank fixed effects and time fixed effects.
(pre-crisis) indicates a Dummy, which equals 1 before August 2007. (crisis) indicates a Dummy,
which equals 1 for the period August 2007 to August 2008. (post-Lehman) indicates a Dummy,
which equals 1 after September 2008. Standard errors are clustered at the country level throughout.
p-value in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels. Sample
sizes for different specifications differ due to data availability.
36