How idiosyncratic are banking crises in OECD countries?
Barrell, Ray ; Davis, E. Philip ; Karim, Dilruba 等
Low levels of bank capital and liquidity in combination with
ongoing crises in other countries are shown to increase the probability
of banking crises in OECD countries, Hence global coordination of
regulatory reform is vital for reducing crisis risks.
Keywords: Banking crises: bank regulation
JEL Classifications: C52: E58; G21
I. Introduction
A particular feature of the recent subprime crisis was its
contemporaneous nature as problems arose simultaneously in a number of
countries. Although similar patterns were also observed in past crises,
the empirical literature shows a limited treatment of cross-country
patterns of banking crises. (1) We test for cross-country simultaneity
of banking crises within the OECD using the logit methodology.
2. Background
Early theoretical models of bank failures and banking crises such
as Diamond and Dybvig (1983) assumed bank failures were a form of
'sunspot', arising from random shifts in depositor perceptions
of the likelihood of a run. However, empirical work soon began to show
that crises were not random within a national economy, but tended to
occur during recessions (see for example Gorton, 1988).
There are reasons to expect that a crisis in one country might also
link to an increased probability of a crisis elsewhere, although this
does not necessarily imply causality. Indeed, the transmission mechanism
for such simultaneous crises could be common shocks to each country
arising from elsewhere. These might include the development of new,
unsound, financial instruments adversely affecting banking systems in
several countries, tighter monetary policy in a dominant third country
or a global recession impinging on world trade and hence on solvency of
corporate clients.
Channels of causality can also be envisaged. One is that a
recession caused by a banking crisis in one country generates recessions
and banking crises elsewhere. Causality could also arise from the
behaviour of investors associated with asset market exposures, where
shocks to global asset markets can cause banks not only to reduce their
exposure to these assets but also to other assets which are seen as
having similar characteristics, generating solvency problems as prices
fall. Or causality could be due to information where revelation of
liquidity or solvency problems in one country's banks induces runs
against other countries' banks that appear similar. Bank behaviour
per se can also be at the root of simultaneous crises given
international interbank exposures, which can lead to cross-border
systemic liquidity problems if one bank defaults, since counterparties
may then be unable to service their own obligations.
Despite these points, there is little empirical work assessing
whether banking crises in OECD countries are systemic at an
international level, in other words whether banking crises in one
country tend to increase the probability of a crisis in other countries.
For example, most banking crisis prediction models (as surveyed in Davis
and Karim, 2008) employ purely domestic macroeconomic variables, albeit
often capturing cross-border impacts (e.g. terms of trade, exchange
rates). One exception is Santor (2003) who finds banking crises are more
likely following the occurrence of crises in countries in the same
income group.
Empirical work on investor-driven banking crises is also generally
at a national level, such as that on abnormal bank stock price behaviour
alongside 'bad news' of banks' performance, and
depositors' behaviour in response to bad news (Calomiris and Mason,
2001). One of the few cross-border studies focused on banking crises is
Jayanti and Whyte (1996) who found significant increases in UK and
Canadian banks' CD rates after the 1984 Continental Illinois
failure.
Models of banking crises such as Freixas et al. (2000) typically
focus on banking links and suggest a potential domino effect if one bank
is unable to meet its counterparty obligations, which could be cross
border. However, empirical counterparts to such research are usually at
a country level (e.g. Furfine, 2003) although Gersl (2007) uses BIS data
to study cross-border effects. However, using direct interbank exposures
of banks or sectors ignores other potential causes of cross-border
crisis simultaneity.
To advance understanding of banking crisis incidence, we created a
variable showing the weighted incidence of ongoing crises elsewhere
(WYCRISC) using 2005 GDP weights. Unlike the studies cited above, this
variable is not restricted to one type of cross-border transmission.
3. Methodology and data
We utilise the multinomial logit, the workhorse approach to
predicting crises (Demirguc-Kunt and Detragiache, 2005; Davis and Karim,
2008). There are alternative approaches but these are less tractable
when looking at simultaneous crises. The logit estimates the probability
a banking crisis will occur in a given country with a vector of
explanatory variables [X.sub.it]. The banking crisis dependent variable
[Y.sub.it] is a zero-one dummy which is one at the onset of a banking
crisis, and zero elsewhere. Then we have the equation:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
where [beta] is the vector of unknown coefficients and F([beta]
[X.sub.it]) is the cumulative logistic distribution. The log likelihood
function is:
[Log.sub.e]L = [n.summation over (i=1)] [T.summation over (t=1)]
[([Y.sub.it] [log.sub.e] F([beta]'[X.sub.it])) + (1 - [Y.sub.it)]
[log.sub.e] (1 - F([beta]'[X.sub.it]))] (2)
Coefficients show the direction of the effect on crisis
probability, although its magnitude is conditional on values of other
explanatory variables at time t.
We start with a general model and utilise traditional variables,
namely credit growth (DCG), M2/FX reserves (M2RES), fiscal balances
(BB), GDP growth (YG), real interest rates (RIR) and inflation (INFL),
as in Demirguc-Kunt and Detragiache (2005) as well as banks'
unweighted capital adequacy (LEV), the banks' broad
liquidity/assets ratio (LIQ) and the change in real house prices (RHPG)
as in Barrell et al. (2010). The latter paper estimated determinants of
banking crises in fourteen OECD countries over 1980-2006, and found LEV,
LIQ and RHPG dominate traditional crisis indicators. Bank concentration
and structure-of-supervision variables (Beck et al., 2006) were not
discriminators for OECD crises. This analysis forms the basis of our
work here. Our initial estimation dataset includes twelve systemic and
non systemic crises (2) drawn from existing datasets, which are derived
consistently according to relevant criteria. (3) We use narrow liquidity
(NLIQ) comprising cash, central bank and government-issued assets
instead of the broader LIQ measure including private sector securities,
because the latter were unreliable as a source of bank liquidity in
2007-9.
4. Results
We estimated our general model over 1980-2006 and assessed whether
the errors are normally distributed before introducing a contemporaneous
cross-country variable. Non-normality could signal an omitted common
factor or crisis driver that could in turn be correlated with a
contemporaneous variable. We would then have to utilise either the
Pesaran (2004a) Common Correlated Effects approach or the simultaneous
logit approach. Both of these would have required country-by country
regressions, difficult in this case owing to the shortage of crises.
We used Pesaran's (2004b) test for cross-section dependence to
investigate cross-equation correlations ([[rho].sub.ij]) between errors
and test for normality. He shows that the correlation coefficients are
distributed as a standard normal variate CD where N is the cross-section
dimension and T is the time dimension
CD = (2T/(N(N - 1)) ** (1/2) * ([[summation].sub.i=1]
[[summation].sub.j=1+1,N-1][[rho].sub.ij]) (3)
The test statistic for the general equation before the introduction
of the contemporaneous cross-country variable WYCRISC is 1.03, below the
critical value for non normality of 1.96. Hence we can add
contemporaneous variables without inducing bias on their parameters.
We obtain a final specification by sequential elimination of the
least significant variable at each stage, with a goal of ensuring 5 per
cent significance level of the retained regressors. The lag length for
all variables, except for WYCRISC, was chosen to capture developments in
the economy prior to the crisis. WYCRISC is contemporaneous as it is
designed to capture ongoing crises in other countries.
Table 1 shows the traditional variables are dominated by the
regressors LEV, NLIQ and the variable for simultaneous crises, WYCRISC.
Higher liquidity (NLIQ) and capital adequacy (LEV) ratios reduce crisis
probabilities. Banking crises in other countries increase the chances of
a crisis in the domestic economy. We tested for the joint elimination of
insignificant variables; the F statistic is insignificant with a p-value
of 0.26.
Table 2 shows that using the sample average cutoff of 3.2 per cent,
75 per cent (nine out of twelve) in-sample crisis observations are
called and 64 per cent of no-crisis observations are captured correctly.
(4) Eliminating crises in the largest countries, or European countries
experiencing systemic banking crises, does not affect the results.
Equally, extending the estimation period to include 2007, when there
were crises in the UK and US, leaves the parameters largely unchanged.
The simultaneity variable becomes less significant, although it still
retains a 5 per cent significance level.
We ran additional robustness tests to check the sensitivity of our
results, first towards the change in crisis dates, (5) secondly to
crises duration and consequent endogeneity between the crisis itself and
the explanatory variables in the post-crisis period and finally towards
exclusion of borderline-systemic crises episodes, as defined in footnote
3. Table 3 shows that our final specification remains robust to all the
above changes.
Our final preferred specification is given by equation (4) with the
corresponding z-statistics:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
Table 4 shows the increment in the probability of a crisis in one
country induced by a crisis in another country for 2006. Effects feed
through WYCRISC, with a greater effect the larger the weight the country
takes in the average. The systemically important countries (see
'average change') are the US followed by Japan and Germany, UK
and France. The potential importance of a crisis event in the US is
noticeable, as its average induced probability is much greater than its
size would suggest. (6) The remaining large countries are less
influential. The Scandinavian economies and the Netherlands are quite
vulnerable to events elsewhere, and the UK is also shown to be
particularly vulnerable.
The table implies small countries have an incentive to induce large
countries to improve their regulatory framework, and large countries
have an incentive to cooperate--although the US as a dominant player may
require side payments.
Predictions of banking stress for 2007 are reported in table 5 and
are compared to the crisis definition from Borio and Drehmann (2009),
i.e. "countries where the government had to inject capital in more
than one large bank and/or more than one large bank failed". By
end-January 2009 this definition included Belgium, France, Germany, the
UK, the US and the Netherlands.
We show in table 5 that five out of six countries that had a crisis
in 2008 were already showing stress (with the probability exceeding the
sample average) in 2007. Our model suggests that there was a noticeable
risk of a crisis in six more counties including Spain, where
developments in 2010 suggest that there may be one under way.
There is a marked nonlinearity in the model which is revealed in
tables 4 and 5, which depends both on the size and the number of
countries having crises. In particular a crisis in the US produces a
fourfold increase in the probability of crises in the UK, France,
Germany, Belgium and the Netherlands. (7)
Conclusions
The risk of a financial crisis is increased markedly when other
OECD countries have a crisis at the same time. Since the probability of
such crises is in turn increased by low capital adequacy as well as
liquidity, the results provide support not only for tighter national
bank regulation but also for international agreements on harmonisation
of regulation at a suitably stringent level, and in particular for
tighter and more effective regulation in the US.
DOI: 10.1177/0027950111411382
REFERENCES
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Gorton, G. (1988). 'Banking panics and business cycles',
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Jayanti, S. and Whyte, A. (1996), 'Global contagion effects of
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Pesaran, M.H. (2004a), 'Estimation and inference in large
heterogeneous panels with a multifactor error structure', CESIFO
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--(2004b). 'General diagnostic tests for cross section
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Reinhart, M.C. and Rogoff, S.K. (2008), 'Is the 2007 US
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NOTES
(1) There is, however, a great deal of work on cross-border
contagion in asset prices and currency crises, see Edwards and Rigobon
(2002).
(2) Our core dataset includes twelve systemic and borderline
systemic crises in fourteen OECD countries between 1980 and 2006, and we
also look at the group of crises in 2007 and 2008. We take information
concerning systemic banking crises from the IMF Financial Crisis
Episodes database (Laeven and Valencia, 2007) which covers the period
1970-2007, while we collect borderline-systemic crises from the World
Bank database of banking crises (Caprio and Klingebiel, 2003) over the
period 1974-2002. Systemic crisis episodes cover Finland (1991), Japan
(1991), Norway (1990), Sweden (1991), UK (2007) and the US (1988, 2007)
while borderline-systemic crises cover Canada (1983), Denmark (1987),
France (1994), Italy (1990) and the UK (1984, 1991, 1995). Borio and
Drehmann consider there were additional crises in the UK, the US,
Germany, the Netherlands, France and Belgium in 2008.
(3) The IMF criteria are that, in a systemic banking crisis, a
country's corporate and financial sectors experience a large number
of defaults and financial institutions and corporations face great
difficulties repaying contracts on time. As a result. non-performing
loans increase sharply and all or most of the aggregate banking system
capital is exhausted. As a crosscheck on the timing of each crisis, they
examine whether the crisis year coincides with deposit runs. the
introduction of a deposit freeze or blanket guarantee, or extensive
liquidity support or bank interventions. Or alternatively they require
that it becomes apparent that the banking system has a large proportion
of nonperforming loans and that most of its capital has been exhausted.
The selection criteria for the wider range of borderline-systemic crises
from the World Bank are to include failures of individual banks or
groups of banks considered to pose systemic risks and necessitating
government intervention in the institutions concerned. These crises do
not involve the complete exhaustion of the banking system's asset
base, but involve large-scale erosion of that base. Finally, Borio and
Drehmann, looking at 2008 events, use the criterion of countries where
the government had to inject capital in more than one large bank and/or
more than one large bank failed.
(4) Demirguc-Kunt and Detragiache (2005) for their most preferred
equation had 61 per cent of in-sample crisis observations and 69 per
cent of no-crisis observations correct. Hence our work stands up well in
comparison.
(5) Alternative crisis dates for the US and Japan are based on
Reinhart and Rogoff (2008).
(6) The increment to the 2006 probability is independent of the
crisis event in the US in the next year.
(7) Note that table 4 is derived using 2006 data and table 5 has
projections for 2007, so the comparison is not exact.
Ray Barrell, * E. Philip Davis, ** Dilruba Karim ** and Iana Liadze
*
* NIESR. E-mail:
[email protected] (corresponding author);
[email protected]. ** Brunel University and NIESR. E- mail:
[email protected];
[email protected]. The authors would
like to thank the ESRC for funding for this work.
Table 1. Sequential elimination of variables
NLIQ(-1) 0.174 -0.138 -0.14 -0.153
(-3.81) (-3.058) (-3.036) -3.289)
LEV(-1) -0.398 -0.479 -0.419 -0.502
(-3.795) (-4.034) (-3.473) (-3.6)
WYCRISC 3.62 2.582 2.831 2.164
(3.112) (2.075) (2.223) (1.584)
RHPG(-3) - 0.08 0.083 0.078
(1.774) (1.938) (1.817)
DCG(-1) - - -0.092 -0.09
(-1.797) (-1.788)
RIR(-1) - - - 0.094
(1.303)
M2RES(-1) - - - -
YG(-1) - - - -
BGB(-1) - - - -
InFL(-1) - - - -
NLIQ(-1) -0.138 -0.145 -0.155 -0.156
(-2.969) (-2.854) (-2.796) (-2.749)
LEV(-1) -0.463 -0.473 -0.462 -0.457
(-3.247) (-3.266) (-3.122) (-2.986)
WYCRISC 2.295 2.161 2.185 2.256
(1.665) (1.52) (1.534) (1.424)
RHPG(-3) 0.078 0.08 0.09 0.088
(1.849) (1.885) (1.906) (1.809)
DCG(-1) -0.086 -0.099 -0.101 -0.1
(-1.695) (-1.612) (-1.629) (-1.581)
RIR(-1) 0.086 0.093 0.0815 0.0689
(1.187) (1.248) (1.029) (0.477)
M2RES(-1) -0.0001 -0.0001 -0.0001 -0.0001
(-0.822) (-0.86) (-0.934) (-0.931)
YG(-1) - 0.065 0.0918 0.091
(0.373) (0.498) (0.494)
BGB(-1) - - -0.0438 -0.046
(-0.451) (-0.463)
InFL(-1) - - - 0.019
(0.104)
Note: Estimation period 1980-2006, z-stat in parenthesis. NLIQ--
narrow liquidity ratio, LEV--unweighted capital adequacy ratio,
WYCRISC--GDP-weighted average of crises, YG--real GDP growth, RPHG--
real house price inflation, BB--budget balance to GDP ratio, DCG--
domestic credit growth, M2RES-M2 to reserves ratio, RIR--real interest
rates, INFL--inflation.
Table 2. Robustness analysis--country elin11natton and changing the
estimation
Final Final panel UK not US not
panel extended included included
NLIQ(-1) -0.174 -0.181 -0.163 -0.188
(-3.81) (-4.194) (-3.305) (-3.828)
LEV(-1) -0.398 -0.324 -0.429 -0.405
(-3.795) (3.502) (3.374) (3.594)
WYCRISC 3.62 2.703 3.391 3.998
(3.112) (2.506 (2.647) (3.3)
% of non- 63.9 59.3
crises called
correctly
% of crises 75.0 71.4
called
correctly
Japan not US and Norway Finland
included Japan not not not
included included included
NLIQ(-1) -0.16 -0.173 -0.185 -0.167
(-3.595) (-3.616) (-3.754) (-3.574)
LEV(-1) -0.399 -0.406 -0.366 -0.377
(-3.842) (-3.629) (3.343) (3.376)
WYCRISC 3.337 3.712 3.355 3.108
(2.713) (2.911) (2.757) (2.533)
% of non-
crises called
correctly
% of crises
called
correctly
Sweden
not
included
NLIQ(-1) -0.167
(-3.679)
LEV(-1) -0.382
(3.597)
WYCRISC 3.0002
(2.456)
% of non-
crises called
correctly
% of crises
called
correctly
Note: Cut-off value for the extended panel is 3.5 per cent as the
number of crisis and non-crisis observations increases in the extended
sample, z-stat in parenthesis.
Table 3. Additional robustness analysis--changing crisis dates,
removing post-crisis observations and elimination of borderline-
systemic crises
Final panel Japanese US crisis
crisis at 1984
at 1992
NLIQ(-1) -0.174 -0.169 -0.17
(-3 81) (-3.755) (-3.774)
LEV(-1) -0.398 -0.371 -0.411
(-3.795) (-3.643) (-3.892)
WYCRISC 3.62 2.925 3.708
(3.112) (2.479) (3.201)
Elimination of Only systemic
post-crisis crises included
observations
NLIQ(-1) -0.167 -0.257
(-3.797) (-3.125)
LEV(-1) -0.383 -0.622
(-3.738) (-3.297)
WYCRISC 3.572 5.938
(3.085) (3.259)
Note: z-stat. in parenthesis, estimation period 1980-2006.
Table 4. Included changes in crisis probabilities in other countries
in 2006
Columns are for countries with a crisis and rows are for
countries affected by spillover
Country i
Country j US UK SP SD NW NL
BG 5.42 0.40 0.23 0.06 O.04 0.11
CN 9.25 0.71 0.42 0.10 0.07 0.19
DK 18.39 1.57 0.92 0.22 0.06 0.43
FN 3.53 0.26 0.15 0.04 0.03 0.07
FR 8.35 0.63 0.37 0.09 0.06 0.17
GE 4.98 0.37 0.21 0.05 0.04 0.10
IT 1.80 0.13 0.07 8.02 0.01 0.03
JP 0.13 0.01 0.01 0.00 0.00 0.00
NL 26.86 2.61 1.54 0.37 0.27 0.00
NW 15.54 1.28 0.75 0.18 0.08 0.35
SD 12.04 0.95 0.56 0.00 0.10 0.26
SP 3.59 0.26 0.00 0.04 0.03 0.07
UK 13.19 0.00 0.62 0.15 0.11 0.29
US 0.00 0.14 0.08 0.02 0.01 0.04
Ave. 8.79 0.67 0.42 0.09 0.07 0.15
Country i
Country j JP IT GE FR FN DK
BG 0.91 0.33 0.54 0.38 0.03 0.03
CN 1.61 0.59 0.95 0.68 0.05 0.06
DK 3.52 1.31 2.11 1.51 0.12 0.00
FN 0.58 0.21 0.34 0.25 0.00 0.02
FR 1.44 0.53 0.85 0.00 0.05 0.05
GE 0.83 0.30 0.00 0.35 0.03 0.03
IT 0.29 0.00 0.17 0.12 0.01 0.01
JP 0.00 0.01 0.01 0.01 0.00 0.00
NL 5.75 2.18 3.48 2.51 0.20 0.22
NW 2.88 1.07 1.72 1.23 0.10 0.11
SD 2.15 0.79 1.28 0.92 0.07 0.08
SP 0.59 0.22 0.35 0.25 0.02 0.02
UK 2.38 0.88 1.42 1.02 0.08 0.09
US 0.31 0.11 0.19 0.13 0.01 0.01
Ave. 1.66 0.61 0.96 0.67 0.05 0.05
Country i
Country j CN BG
BG 0.22 0.08
CN 0.00 0.11
DK 0.88 0.25
FN 0.14 0.04
FR 0.35 0.10
GE 0.20 0.06
IT 0.07 0.02
JP 0.81 0.00
NL 1.47 0.42
NW 0.72 0.20
SD 0.53 0.15
SP 0.14 0.04
UK 0.59 0.17
US 0.08 0.02
Ave. 0.39 0.11
Note: US--United States, UK--United Kingdom, SP--Spain, SD--Sweden,
NW--Norway, NL--Netherlands, JP--Japan, IT--Italy, GE--Germany, FR--
France, FN--Finland, DK--Denmark, CN--Canada, BG--Belgium.
Table 5. Banking stress indicators for 2007 (percentage
probability of crisis
2007 Borio-Drehmann
Belgium 10.47# X
Canada 14.53#
Denmark 31.88#
Finland 5.99#
France 18.69# X
Germany 9.45# X
Italy 3.12
Japan 0.34
Netherlands 47.30# X
Norway 33.24#
Sweden 20.18#
Spain 9.97#
UK 18.89# X
US 0.73 X
Note: Bold figures are predictions above the sample mean.
Note: Bold figures are predictions above the sample mean
is indicated with #.
