Download Cross-Country Empirical Studies of Systemic Bank

Cross-Country Empirical
Studies of Systemic Bank
Distress (1980-2015): A
Survey
Abstract
PG-Mokhtari, Fatemeh (120007498)
9-28-2016
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Contents
Page
Introduction………………………………………………………………………………………………………….2
Theory
A. Panic Based Banking crisis
B. Fundamental based Banking crisis
1. Institutional factors
2. Macroeconomic factors
Studies of the Determinants of Banking Crises
Empirical testing
Using Econometrics models of Banking Crises as Early Warning System
Effect of banking crisis
Econometrics Model
Sample
Data
Results
Conclusion
Appendix
References
[1]
Introduction
Post World War II, majority of economies across the world were experiencing economic
growth, low inflation, universal controls on international capital flows and in many free
market orientated nations, regulations of central banks assisted in controlling the quantity
and price of capital, all contributing to economic and financial stability. This consistency
continued through the 1970s, even with the oil shocks and the breakdown of the Breton
Wood system, the banking sector in majority of the countries remained secure possibly due
to low and sometime negative real interest rates and the persistency of regulations. In the
early 1980s Latin America and few other developing countries (LDCs) experienced financial
crises including extensive bank distress. These events took place after introducing a more
contained monetary policy, interest rates rose sharply and credit market liberalization, and
as a result of external shocks and reckless fiscal and exchange rate policies (all related to
macroeconomic fundamentals).
In the course of the same period, financial liberalization, liberal deposit insurance and
ineffective regulations contributed to American bank managers increased risk taking
behaviour that resulted in the United States savings and loans (S&L) crisis costing the federal
government $105 billion to resolve the crisis. Kane (1989) stated, by the end of 1999 U.S. tax
payers have paid approximately $124–132.1 billion, carrying the fiscal cost burden, resulting
in a small macroeconomics effects. In the 1990s, financial crises caused by the banking
sector become more frequent and with more severe macroeconomic costs. Scandinavian
countries experienced a similar crisis as to twin crises, as the banking crises were caused by
currency devaluation and fall in asset prices, resulting in economic slowdown. Moreover,
Japan’s banking sector became insolvent as a result of burst of the asset price bubble. Hoshi
and Kashyap (2004) stated that after forty years of rapid economic growth, due to moderate
regulation and relaxed monetary policy the process of balance sheet repair for banks were
extended over more than a decade while they performed poorly. Japan’s banking crises has
lasted over two decades as of 2010 and it has yet to fully recover.
The other prominent crisis of that impacted the macroeconomic stability was the tequila
crisis, which began in Mexico. Given that the Mexican government finances appeared to be
healthy, the currency devaluation as a result of large dollar denominated debt, political
shocks and fragile banking system, caused a financial meltdown. This was expensive for the
government as the cost of bailouts reached 20 percent of the GDP, and contrary to Mexican
government’s rescue, the economic state of the country has not fully recovered yet. This is
[2]
similar to the crisis that the East Asian countries experienced during the same period. While
these nations were experiencing strong economic growth and healthy public finances, burst
of the credit bubble, sudden halt in foreign capital inflows and loss of depositors’ confidence
in banks, made these economies extremely weak.
The most recent global financial crisis of 2007-2008, surprised the financial sectors in United
States, United Kingdom and Europe, causing these nations to experience economic
recessions for the years to follow. After the event, some economists stated that the financial
crisis could have been predicted but no one in the financial sector paid attention to warning
signals. Thus, the paper that I will be focusing on is an IMF working paper by A. DemirgucKunt and E. Detragiache (2005) titled ‘Cross-Country Empirical Studies of Systemic Bank
Distress: A Survey’. In this paper the authors study the determinants of banking crises, using
the two basic methodology of signals approach and the multivariate logit model. The use of
these methods for providing early warnings and the economic effects of banking crises are
then reviewed.
Using their Multivariate Logit Approach I will be looking at different
variables as determinants of 2008 financial crisis in United States, United Kingdom, Euro
Area and Greece.
Since the 18th century, there has been numerous occasions where majority of countries
within the world, being developed, developing or emerging economies have experienced
banking crisis on one or more occasions. Looking at diverse literatures, different authors
have found various number of banking crisis occurring, depending on what indicators, period
of study and the sample of countries in question were. Looking at the last twenty five years
for example, Caprio and Klingebiel (1996) documented 69 crisis in developing countries and
emerging economies. Similarly, Bordo et al. (2001) reported only one banking crisis in the
quarter of a century after 1945 but 19 since then, given he studied 21 countries in total.
However, Demirguc-Kunt and Detragiache (2005) reported 82 crisis in their paper, given they
looked at all the countries in the world for the same period. The most recent update is in
Laeven and Valencia (2012) paper, where they have identified 147 systemic banking crises
from 1970 to 2011.
There are few factors that contributed to this increase in frequency of banking crisis. As
Bordo et al. (2001) stated, the rise in the regularity of financial crisis could be the result of
financial globalization, where it caused a rise in capital flows between industrial countries,
and a more significant increase in capital flow between developing and industrial countries.
While in some developing economies, surge in capital flows has been associated with
[3]
economic growth, in others have experienced periods of collapse in growth and a notable
financial crisis that has resulted in substantial macroeconomic and social costs. As of 1996,
Honohan (2000) reported an estimated total cost of banking crisis to be over $500 billion, of
which over $200 billion was the share of five East Asian crisis countries including Korea, $250
billion for the rest of the affected countries including Brazil and Mexico. In terms of social
costs, these costs have been partially financed by the depositors and creditors of failed
banks, but the majority have been paid by the governments of these countries, resulting in
increase in social costs ad government resources are being shifted away from other sectors
of the economy. Given the frequency, social and economic costs of banking crisis, many
economists have looked at the possible cause(s) and factors that could indicate banking
crisis since the 19th century, but none could predict the financial and banking crisis of 20072008 that affected majority of countries across the globe, and causing economic recessions
in its wake. This paper will look at economic fundamentals during the past 35 years to see if
they could have send warning signals about these systemic banking crisis.
Theory
A systemic banking crisis occurs more than one bank in a country faces solvency or illiquidity
issue during the same period of time. Since the first banking crisis in the 18th century,
economists have formed different views about the factors leading to these crisis. Upon
which is the view that banking crisis is caused by deteriorating economic fundamentals,
mainly a fall in asset qualities, which downplays the role of arbitrary changes in agents’
beliefs, first mentioned by Diamond and Dybvig (1983). This means that systemic crisis could
be a result of the exposure of the banking sector to an external shock. common risk or due
to failure of
bank(s) that spreads through the sector as a result of high interbank
relationship, causing financial institutions and corporation not to able to complete their
contractual obligations since they have been facing high number of defaults. Thus, due to
financial institutions and intermediaries’ interrelationship, cross exposure and the nondiscriminatory reaction of the market participants, a failure of one individual would spread
through the system. An example of such an event could be the exposure to broad asset
classes such as real estate or equity, which impacts large economic sectors and is profitable
during an economic boom. This results in a sharp increase in non-performing loans to asset
ratio which can put a lot of downward pressure on the banking sector’s capital. This may be
linked to a fall in asset prices (specifically equity and real estate), slowdown of capital flows
and/or sharp increase in real interest rate. As a result, there is a sharp rise in non-performing
loans and thus all or most of the aggregate banking system capital is exhausted. These
[4]
changes could be the result of unsustainable macroeconomic policies (including large
current account deficits and unmanageable public debt as seen in the case of Greece),
excessive credit booms (as seen in mainly in United States and other economies), large
capital inflows and balance sheet fragilities within the banking sector. Since these
fundamentals are easier to observe compared to agents’ expectations and beliefs, it is more
rational to use these in forming expectations to some degree, for predicting banking panics.
On the other hand, systemic crisis can be triggered loss in confidence of agents (depositors)
as mentioned by Diamond and Dybvig (1983).
A. Panic Based Banking crisis
On the other hand, there is Diamond and Dybvig (1983) theory where bank runs are a result
of an extreme panic from the public where they lose confidence in the given bank(s). Bank
runs are perceived to be random events, caused by shifts in agents’ beliefs that are
unrelated to the real economy. In their simple model, one bank represented the financial
intermediary sector, and the problem of bank run and the effects of deposit insurance
occurred in a system that does not have currency or risky technologies. They found out that
in a financial system where many banks exist and there is a central bank that acts as a lender
of last resort, a bank run can happen in a response to the change in agents’ expectations of
banks credit worthiness. Since agents cannot directly asses the riskiness of each individual
banks due to existence of asymmetric information, they gather bank specific information
and thus the riskiness is based upon the combined information, hence all banks can be
distinguished as risky by agents and causing a panic as a result. Moreover, if agents’ believe
that the central bank (as the lender of last resort) is not willing to rescue a falling bank,
based on this expectation a bank run could happen, much like the Federal Reserve’s
decisions in 1930s which caused a bank run.
B. Fundamental based crisis
1. Institutional factors
Systemic banking crisis can be very damaging for an economy(s), thus why literatures have
been written on them. As seen by the most recent banking crisis, they can be contagious,
meaning they start in one country (in this case United States) and spread to other countries
very rapidly (over the course of a day, banking crisis in States spread to banks and
economies of most European countries). Moreover, systemic banking crisis can lead the
affected economies into deep recessions and substantial change in their current accounts.
[5]
Honohan (2000) stated that isolated bank failure are inevitable. This is due to very
competitive nature of the financial sector, where small and/or inefficient banks are more
likely to fail, thus expecting no bank runs are unrealistic. Though, widespread systemic
failure that causes chain of bank runs should be avoided as they could have dire
consequences on the economy.
Moreover, historically banks acted as an intermediary between the borrowers and the
lenders and as a result expanded or contracted their lending as a reaction to shift in
economic circumstances. Due to securitization, banking and capital market are now heavily
entangled. The main purpose of securitization was to pass on risk to those who are better at
bearing it and as a result making financial system more robust to default by borrowers. As
seen during the recent financial crisis, risk was focused in the financial intermediary sector
instead of the investors. This is due to banks not selling the risky loans, by which the banks
would have passed on the risk. But instead they issued liabilities backed by the bad loans
and kept the risk of default by borrowers on their balance sheets. Hence, in the face of
default, investors only lost money where as the financial intermediaries could lose their
entire equity as they are leveraged. Over time these vulnerabilities build up within the
financial sector given the interactions between financial sector and the real economy. As a
result, during a boom when asset prices rise, the perceived risk falls and financing form
outside the sector becomes more attractive. Hence, investments in sectors showing most
growth will increase, which makes the financial imbalances becoming disguised by the
booming economic conditions. Given a small shock to the economy in form of a contraction,
the financial sector becomes unstable and unless there are shock absorbing tools in place,
the financial sector can be effected by larger scale than other sectors. Based on this factors,
Borio et al.(2002) believed that the timing of financial crisis are unpredictable, but if one
looked closely at other economic factors such as rapid growth in credit and asset prices or
cumulative appreciation of the real exchange rate could indicate a possibility of financial
instability.
2. Macroeconomic factors
It has been believed that there is a causal relationship between financial sector activities and
economic output, meaning financial sector activity tends to be leading output, and thus
when the banking sector is facing problems, output (GDP) tend to fall. However, Friedman
and Schwartz (1963) highlighted the opposite direction of causality during the times when
banking sector is faced with problems. They state that there are two ways in which systemic
[6]
banking crisis can worsen the economic contraction, one by reducing bank shareholders’
wealth and two by a sharp fall in supply of money. Moreover, this reverse causality was
evident in 1933 in United States, where during the 1929-1939 recession, attempt at boosting
the economy was stalled by the banking panics of 1939, causing the financial sector reaching
its all-time low in 1933 and as did the economy. The main two attributing factors of the
1930’s financial crisis were the loss of confidence of agents in financial institutions mainly
commercial banks and the default of majority of debtors. Loss of confidence, as highlighted
by Diamond and Dybvig (1983) initiated a sudden surge of withdrawals and possibility of
bank runs, causing commercial banks to liquidate their illiquid assets, and thus becoming
insolvent and failing. Also widespread default pf debtors were due to fall in income as a
result of economic downturn, which Bernanke (1983) identified this factor as financial
sector responding to the fall in economic output, which supports the finding in Friedman and
Schwartz (1963) paper. Moreover, Bernanke builds upon their theory and introduces a third
way in which the financial crisis could affect output. He stated that due to 1930 disruption in
financial markets, reduced the effectiveness of financial sectors in providing information
gathering services for intermediation between some borrowers and lenders, and thus the
cost of these intermediations rose, resulting in borrowers finding credit expensive and hart
to acquire. This reduced aggregate demand, resulting in the economy entering a recession.
Bernanke’s paper further supports the reverse causal relationship highlighted in Friedman
and Schwartz (1963) paper.
Moreover, Allen and Gale (1998) discovered that systemic banking crisis are correlated with
business cycles rather than sunspots (a random event or variable that has no impact on
economic fundamentals) similar to Gorton (1988). In their paper, Allen and Gale introduced
a simple model, where bank runs can be efficient by allowing early and late withdrawers
share risk efficiently and banks can hold efficient portfolios. Their model is similar to the one
of Diamond and Dybvig with two different assumptions, one being risky and illiquid long
terms assets held by banks are perfectly correlated across the banking sector, thus the
impact of business cycles on the value of bank assets capture the uncertainty of asset
returns; and two being that they do not make the assumption of first come first serve. Since
economic fundamentals are easily observable, depositors use these as signals to how the
banks will perform, and if they believe bank’s receipts are going to be low, the possibility of
bank run occurring is very high. In a more complicated model, where Allen and Gale
introduced market for risky assets and high cost bank runs, they found that the right kind of
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intervention by central banks can lead to Pareto efficiency, as Central Banks were formed in
the 18th century in order to create more stability within the financial sector.
This is similar to Gorton (1988) paper examining the national banking era in United States
between 1863 and 1914, where he stated that bank panics are created by the consumption
smoothing behaviour on the part of cash-in-advance constrained agents in forming
conditional expectations, which is correlated to the state of the economy. Some or most
depositors work for firms within the economy, and during a bust where the output is falling,
firms tend to perform poorly. Due to banks holding claims on firms, when firms fail (possible
indicator of recession), depositors observe the riskiness of the economy, and if a bank run
happens, banks will fail. Thus he suggested that banking crisis occurred whenever key
macroeconomic variables (that are linked to the possibility pf recession happening) reached a
critical value.
Moreover, Demirguc-Kunt and Detragiache (1998) studied an international sample of
developing and developed economies for the period of 1980 to 1994. Their study exhibited a
correlation between number of variables connected to the fundamental state pf the
economy and the occurrence of systemic banking crisis. The main variables that attributed
to banking crisis reduces bank’s asset were GDP growth, interest and inflation rate and the
level of outstanding credit within in the banking system. Their results showed that a fall in
economic activity (low GDP growth) reduces banks’ asset prices as national income falls (less
cash saving in the banks) and possibly a reduction in payments of interest earning loans (i.e.
mortgages), high interest and inflation rate encouraging commercial banks to offer high
deposit rates whilst rates on their long term loans are fixed, and high level of outstanding
credit that makes banking system more vulnerable to external shocks; meaning the financial
system is more likely to experience systemic banking crisis. Thus, based on these findings
they concluded that crisis cannot be explained solely by the self-fulfilling beliefs of agents
(bank panics) and that they are related to the state of the economy. Similarly, Calomiris and
Mason (2003) studied the Federal Reserve System (Fed) bank members in order to model
determinants of bank failure. They identified that there is a close relationship between
fundamentals and the possibility of individual bank failures between 1930 and 1933. In their
study, fundamentals included attributes of individual banks as well as the exogenous local,
regional and national economic shocks that effected the banking system’s health.
Furthermore, Kaminsky and Reinhart (1999) found that due to financial globalisation banking
and currency crisis are linked, where one intensifies the occurrence of the other. Currency
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crisis happens as a result of a speculative attack on a country’s domestic currency, where if
successful, the domestic currency shows a large depreciation, resulting in a significant loss of
the country’s foreign reserve as the central bank/the government tries to defend the regime
by selling the foreign reserves and/or increasing interest rate in order to cash flow in to the
country. Developing countries that do financial trading on international scale, their banks
can experience discrepancy between liabilities that are denominated in foreign currencies
and assets that are denominated in domestic currency. Thus, if there is a run on a bank, due
to loss of foreign reserves the currency weakens, creating an opportunity for speculative
attack on the domestic currency. The speculative attack on the currency weakens the bank
further as depreciation leads to increase in the value of the liabilities relative to the value of
assets of the bank. Hence a vicious cycle between the two crises follows, increasing the
possibility of both happening. This resulted in Kaminsky and Reinhart calling these two crises
the twin crises. In their paper the show that the twin crises are the result of deteriorating
economic conditions including below the average economic growth, declining terms of trade,
falling stock prices, overvalued exchange rate and the increasing cost of credit.
Likewise, Reinhart and Rogoff (2008) demonstrated the existence of high correlation
between liberalization of capital accounts and the occurrence of banking crises, which are
evenly spread between developed and developing countries. They discovered that a
continuous flood of capital inflow combined with a bubble in equity and/or housing market,
which is very likely to burst before the crisis, is common cause of banking crisis (similar to
what happened in 2007-2008 in United States). Based on what has transpired thus far,
Goldstein (2012) indicated that macroeconomic fundamentals initiate panics, and panics act
to intensify fundamentals on the economy. Therefore, when it comes to policy making, panic
based approach and fundamental based approach are not inconsistent with each other.
Studies of the Determinants of Banking Crises
Since the 1970s few literatures have been written on using bank’s balance sheet and market
information as early warning signs for individual bank and institution failure. GonzalezHermosillo (1999) used macroeconomic and bank specific data to analyse periods of banking
distress in Colombia, Mexico and different regions of United States. Her results indicated
that before bank failure, there is a sharp decline in non-performing loans and capital asset
ratio. Also Bongini et al (1999) paper focused on individual institution data for the Asian
crises and investigated the impact of Capital Adequacy, Asset Quality, Management,
Earnings and Liquidity (CAMEL) variables, country dummies, bank size and corporate
[9]
connections on bank failures. Their results indicated that even though big financial
institutions are more likely to face distress, they are less likely to be closed. Moreover, they
found that the more interconnected institutions are at higher risk of experiencing distress.
Also, they discovered that individual bank and institution weaknesses (underlying problems)
were a large contributors to systemic banking distress in the face of exogenous shocks in
Asia.
Other literatures including Caprio and Summers (1993) and Stiglitz (1994) researched the
impact of Financial Liberalization on financial fragility. They discovered that banks tend to
take on more risky behaviour as a result of liberalization which offers limited liability and
explicit and implicit guarantees, meaning when faced with troubles bankers do not bear
much of the downside risk. This increases the financial sector fragility further than the
socially acceptable limits. Similarly Demirguc- Kunt and Detragiache (1998) found that
nations with liberalized financial system are more likely to experience systemic banking crisis.
Also their result indicated that if liberalization is not accompanied by sufficient prudential
regulations and effective supervision, then it can increase the risk taking behaviour and thus
make bank crisis more likely.
In addition, international shocks and exchange rate regimes are believed to have an
influence on banking crisis. As Mundell (1961) discovered, flexible exchange rate regime
could absorb some of the real shocks to the economy and also may reduce the financial
sector’s tendency to over borrow in foreign currency, thus act as a stabilizer and a
discouraging factor. On the other hand, fixed exchange rate regime may increase the risk of
banking crisis for a nation, as the central bank has limited expansionary tools for the times
the economy is in trouble. However, Eichengreen and Rose (1998) argue that fixed exchange
rate regime discourages bankers’ risk taking behaviour as they know central bank has limited
tools, hence reducing the probability of banking panics.
Empirical testing
a) Signals approach
Kaminsky and Reinhart (1999) were the first ones to apply this method to banking crises,
when they were analysing the twin crises. Previously signals approach was used to identify
defining moments in business cycles. In this method, one looks at different factors that could
signal any change is behaviour. In their paper, Kaminsky and Reinhart looked at fifteen
macroeconomic variables during the two years before and after crisis, and then they
[10]
compared them to the behaviour of those variables during periods of stability. In relation to
banking crisis, they discovered that in the months prior to the crisis there is a high demand
for money and credit in the economy, as monetary growth and interest rates (both deposit
and lending rates) are higher than normal. Among external balance indicators, export
growth appears below the trend while exchange rate is appreciating. Moreover they found
that eight months prior to the peak of the banking crisis, stock prices reach highpoint and
real GDP growth falls below the average. These indicate that a cause of banking crisis could
be cyclical downturn. Thus they discovered that the performance of each significant variable
during the two years before the crisis is different to the behaviour during the times of
economic stability. In their model, Kaminsky and Reinhart observed variables and if they
crossed a certain threshold (such that they minimise the in-sample noise-to-signal ratio for
each individual variable), they would be considered correct signals if they were followed by a
crisis, and false alarms if not. To finish, they compare the performance of each signal to the
associated Type I (probability of missing a crisis), Type II error (probability of a false signal),
the noise-to-signal ratio and the probability of crisis taking place conditional on a signal
being issued.
Kaminsky and Reinhart (1999) discovered that in regards to banking crisis, appreciation of
real exchange rate, equity prices and the money multiplier have the lowest noise-to-signal
ratio and the highest probability of crisis taking place conditional on a signal being issued.
However, these indicators suffer from large Type I error by failing to issue a signal on 73 to
79 percent of observations within the 24 months prior to the crisis. On the hand, these
indicators present a lower incidence of Type II error by issuing a false signal only on 8 to 9
percent of observations within the 24 months preceding to the crisis. The lowest Type I error
belongs to real interest rate by signalling in 30 percent of pre-crisis observations.
Furthermore, they have discovered that banking crisis rather than currency crisis is mostly
associated with changes in the real sector compared to the monetary sector.
The signals approach looks at each possible covariate in isolation, thus fails to produce
aggregate information based on individual indicators. Also, this methodology tends to ignore
information given by the data. For example, by only looking at if the variable has passed a
certain threshold, it fails to take into account the amount by which the variable has passed
the threshold that can be used in determining the sensitivity and the fragility of it.
b) Multivariate Logit Approach
[11]
The multivariate logit approach was developed by Demirguc- Kunt and Detragiache (1998),
where the probability of a banking crisis occurring is assumed to be a function of a vector of
explanatory variables. Then the data is fitted with a logit econometric model and by
maximizing the likelihood function, an estimation of the crisis probability is given. Hence,
subject to the hypothesized functional form, the model makes the best possible use of the
information given by the explanatory variables and produces a summary measure of fragility
(estimated probability of crisis).
In the model the dependent variable takes a value of one if a country is experiencing crisis in
each period, and zero if it is not experiencing crisis. The probability that a crisis will occur at
a particular time in a particular country is hypothesized to be a function of a vector of n
explanatory variables X(i, t). Letting P(i, t) denote the banking crisis dummy variable, β
denote a vector of n unknown coefficients, and F(β'X(i,t)) denote the cumulative probability
distribution function evaluated at β' X (i,t) , the log-likelihood function of the model is:
𝐿𝑛 𝐿 = ∑
{{𝑃(𝑖, 𝑡) 𝑙𝑛[𝐹(𝛽^′ 𝑋(𝑖, 𝑡))] + (1 − 𝑃(𝑖, 𝑡))𝑙𝑛[1 − 𝐹(𝛽^′ 𝑋(𝑖, 𝑡))]}
∑
𝑡=1..𝑇
𝑖=1..𝑛
F, the probability distribution function is assumed to be logistic. Hence, the effect of a
change in an explanatory variable on ln(P(i,t)/(1-P(i,t)) is shown by the estimated coefficients.
Meaning, any increase in the probability depends on the original probability and the initial
values of all independent variables and coefficients.
Demirguc- Kunt and Detragiache (2005) included all the countries in the world from 1980 to
2002, excluding the transitional economies. Using this method and based on their sample,
they discovered that deteriorating economic factors like low GDP growth, high inflation and
real interest rate as well as bank’s exposure to currency crisis and makes a nation more
vulnerable to banking crisis. Also, their results confirmed that developing countries are more
prone to banking crisis.
Using Econometrics models of Banking Crises as Early Warning System
Given the increase in frequency and number of nations suffering from banking crises since
1990s, many literatures have been written variables that can be used as early earning for
banking crises. Amongst which are Sachs et al (1996) and Gavin and Houseman (1995) who
suggested a good indicator of credit boom is credit growth. Honohan (1997) studies a
sample of eighteen crisis and six non-crisis nations in order to see if there are an indicators
for systemic banking crises. He separated the countries who experienced banking crisis into
three groups based on the type of crisis they experienced, macroeconomic, microeconomic
[12]
and/or related to the behaviour of the central governments. He discovered that banking
crises which stem from underlying macroeconomic problems are correlated to high loan to
deposit ratios, high credit growth rate and high foreign borrowing to deposit ratio. Further,
his results show that the banking crises arising from government interventions are
associated from high level of borrowing and central bank lending o the banking systems.it is
interesting to note that crises due to microeconomic issues tend to have no indicators with
abnormal behaviour.
Kaminsky and Reinhart (1999) introduced the signals approach for crisis prediction, which
Kaminsky (1999) and Goldstein et al (2000) developed it further. These papers looked at
several indicators simultaneously that may cross individual thresholds, or alternatively,
indicators get weighted by their signal-to-noise ratio. The problem with this method was
that real exchange rate was outperformed by the best composite indicator where it was
worst at predicting quiet observations.
Demirguc- Kunt and Detragiache (2000) use the Multivariate Logit approach that produces
lower in-sample Type I and Type II errors compared to the signals approach, resulting in
more accurate early warning system. Demirguc- Kunt and Detragiache then used forecasts of
the explanatory variables (provided by professional forecasters and international institutions)
and estimated coefficients from the multivariate logit model to construct out-of-sample
forecast of crisis probabilities. Then they use two different monitoring frameworks. In the
first, they use forecast probability of a crisis as the measure of fragility. Given this probability
is high enough, taking action involves a trading-off the cost of taking action when there is no
crisis against the costs of not taking action when there is crisis. Thus the first monitor takes
into account that the optimal trigger for action depends on the cost of making a mistake as
well as the in-sample predictive power of the model. The second monitoring framework only
rates the fragility of the banking system. Thus, based on different rating different actions can
take place. Moreover, they applied these monitoring frameworks to six crisis episodes
(Jamaica, Indonesia, Korea, Malaysia, Philippines, and Thailand). They discovered that both
forecast an actual data signalled a high vulnerability in case of Jamaica only. For the Asian
countries, even though there were signs of fragility, stable economic growth and exchange
rate offset the high real interest rate and credit growth, thus portraying a reassuring picture
(see table 8 in appendix for these probabilities).
[13]
Due to systemic banking crisis evaluations being at its early stages, as of the monitoring and
forecasting tools used for it, the in-sample prediction accuracy cannot be replicated out-ofsample, hence the tools have had limited success.
Effect of Banking Crisis
There are number of studies where they have looked at the repercussions of banking crisis
as well the causes of it. Lindgren et al (1996) paper summarizes several case studies,
concluding that bank fragility and crisis has negative impact on output growth. Similarly,
both output and private credit growth decrease below trend during the years around the
systemic banking crisis.
Recent study by Dell’Ariccia et al (2005) presents new finding on the credit crunch
hypothesis (impact of contraction in lending by financial institutions on the economy). They
use the “difference-in-difference” method used by Rajan and Zingales (1998) in order to
study the impact of finance on growth. Their sample includes a panel data on countries and
industry level data. After controlling for all country specific, time specific and industry
specific shocks, they find that more financially dependent sectors are effected by banking
crisis more than other sectors, which supports the credit crunch hypothesis. In terms of the
size and magnitude of banking crisis effect on these sectors, they found that more financially
dependent sectors lose about one percentage point of growth for each year during the crisis
compared to the less financial sectors.
Furthermore, there have been few cross country empirical analysis to examine the impact of
intervention policies on the cost of banking crisis. The problem with these studies is that
gathering data on intervention policies for a large enough sample is very difficult. Also it is
even more challenging to find and capture complex dimensions like timing, sequence and
specific modalities of bank related interventions using quantitative measures. Honohan and
Klingebiel (2003) used a sample of 40 countries and estimates of fiscal costs related to their
adapted policies. In their database, they divided their sample into five categories based on
five different policy interventions. They discovered that more generous bailouts had the
highest cost for the government.
Econometrics Models
Based on my sample, I will be using the Demirguc- Kunt and Detragiache Multivariate Logit
regression. I Demirguc- Kunt and Detragiache Multivariate Logit regression with the loglikelihood function of:
[14]
𝐿𝑛 𝐿 = ∑
∑
𝑡=1..𝑇
{{𝑃(𝑖, 𝑡) 𝑙𝑛[𝐹(𝛽^′ 𝑋(𝑖, 𝑡))] + (1 − 𝑃(𝑖, 𝑡))𝑙𝑛[1 − 𝐹(𝛽^′ 𝑋(𝑖, 𝑡))]}
𝑖=1..𝑛
𝐹 = 𝑙𝑜𝑔𝑖𝑡[𝑃(𝑖, 𝑡)] = ln (
𝑃(𝑖, 𝑡)
)
1 − 𝑃(𝑖, 𝑡)
Demirguc- Kunt and Detragiache Multivariate Logit regression, they estimate the model
without country fixed effects as in their (1998) paper, in order to use the non-crisis countries
as controls. However in their 2005 paper, by clustering the errors by country, they allow for
error terms to be correlated within each country. Thus the logit regression is as follow:
𝑙𝑜𝑔𝑖𝑡[𝑃(𝑖, 𝑡)] = 𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 +…+𝛽𝑛 𝑋𝑛
In my multivariate logit regression, I account for country fixed effects, but not year fixed
effects, the same as Demirguc- Kunt and Detragiache (2005)
Demirguc- Kunt and Detragiache have few key elements in their model. One being that they
have excluded the years of the crisis happening. The reason being that crisis effect the
behaviour of some of the explanatory variables. For example, during crisis the real interest
rate may fall due to expansionary monetary policy placed for the banking sector rescue
operations. Thus by excluding the years of crisis unfolding, the behaviours that are observed
in their sample belong to the explanatory variables leading to the crisis.
The other key element is the way that Demirguc- Kunt and Detragiache created the dummy
variable for banking crisis. First, they excluded transitional economies as they believed the
problems these nations face were of special nature. Following, they had to distinguish
between what counted as fragilities and crisis and more specifically systemic crisis and
localized crisis. Thus, for a period of distress to be categorised as systemic crisis, one of these
four conditions had to be satisfied:

Banking sector’s ratio of non-performing assets (NPA) to total assets exceeding 10
percent

Large scale nationalization of banks due to banking sector’s problems

The cost of bank rescue operations being at least 2 percent of the GDP

Occurrence of widespread bank runs or the government taking extreme measures
like deposit freeze and/or bank holidays in response to the banking crisis.
Sample
[15]
Demirguc- Kunt and Detragiache (2005) included all the countries in the world from 1980 to
2002, excluding the transitional economies. However, I created a sample that includes only
United States, United Kingdom, Greece and Euro Area. The reason for this being the most
recent financial crisis that had a great impact on these nations.
In August 2007, BNP Paribas announcing that it was ceasing activity in three hedge funds
that specialised in US mortgage debt, signalling that a vast amount of these securities were
worth much less than previously thought, thus causing the United States market experiences
a crash in sub-prime mortgages, causing the American financial institutions and banks to
make big losses. After almost a year, in 2008 the financial crisis came to a head by U.S
government allowing the Lehman Brothers to go bankrupt. Due to the nature of financial
markets (financial liberalization has caused markets and the institutions to be
interdependent) the problems with these mortgage backed securities soon spread to
European markets and institutions and beyond. As a results some banks within these
countries experienced bank runs, these economies experienced recession all to a different
degree that lasted differently for each. Moreover, all these nations experienced
deteriorating economic conditions as a result. I selected these countries because of how the
experienced the systemic banking crisis. Next section will have more explanation on how the
bank crises period were selected in each country.
Data
Data used in Demirguc- Kunt and Detragiache’s paper are collected from the World Bank,
International Monetary Fund (IMF) reports and Institute for Fiscal Studies (IFS). Similarly, I
gathered data from these sources, and when updating to create my own sample, I used few
additional sources like the Federal Reserve database and reports, Bank of England database,
European Central Bank database and National Audit Office.
In order to distinguish the periods of banking crisis in my sample that included Euro Area (as
a whole and not individual countries), United Kingdom, United States and Greece from 1980
to 2015, I used Demirguc- Kunt and Detragiache (2005) table for Periods of banking Crisis
from 1980 and 2002 (Table 7 in appendix). Moreover, I used the four conditions they had
given in the same paper in order to distinguish the periods of crisis within my sample.
Based on the four conditions, I gathered data on the ratio of non-performing assets to total
assets of banking sectors in each of my sample countries. The table below shows the
percentage of non-performing assets (default loans) in individual years within my sample
[16]
period and for each nation. Euro Area has missing values between 1997 and 2000 as it was
established in 2000. Given the condition that if a country’s non-performing assets to total
asset ratio exceeds 10 percent, then the country is having systemic banking crisis, Euro
Area’s NPA to total asset ratio exceeds the 10 percent threshold from 2013 to 2015.
Similarly Greece’s NPA to total asset ratio exceeds the threshold from 2011 to 2015.
However, both United Kingdom and United States NPA to total assets ratio stays below the
10 percent level during the sample period. Thus, simply based on this condition, only Euro
Area for the period of 2013 to 2015 and Greece for the period of 2011 to 2015 face banking
crisis.
Table 1: ratio of Non-performing assets to total assets
Year
Euro Area (%)
United states (%)
United Kingdom (%)
Greece (%)
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
5.33
4.75
4.16
2.85
2.88
2.7
2.31
2.22
3.08
6.34
7.06
7.83
9.2
11.36
11.93
11.86
1
1
1
1.1
1.3
1.4
1.1
0.8
0.7
0.8
1.4
2.97
5
4.4
3.8
3.3
2.45
1.85
1.52
2.9
3.2
3
2.5
2.6
2.6
2.5
1.9
1
0.9
0.9
1.56
3.51
3.95
3.96
3.59
3.11
1.76
1.44
13.6
15.5
12.3
5.6
7.4
7
7
6.3
5.4
4.6
4.67
6.95
9.12
14.43
23.27
31.9
33.78
34.67
Considering the second condition, the large scale nationalization of banks, United Kingdom
experienced series of bank nationalism in 2008, starting with Northern Rock being
nationalized by the government and was sold to Virgin Money in 2012. Later in 2008,
Bradford and Bingley bank was separated into parts by the British government, the section
in charge of loans and mortgages was nationalised and the commercial bank was sold. At the
end of 2008, HBOS-Lloyds TSB group and the Royal Bank of Scotland (RBS) were partially
[17]
nationalized by the government who owns almost 80 percent of RBS and almost 40 percent
of Lloyds TSB.
In United States, it is believed by some economists that with relation to Citigroup, the
Troubled Asset Relief Program (TARP) acted as a partial nationalization in 2009. However,
this cannot be considered as large scale nationalization and thus does not satisfy the given
condition.
In relation to Greece, the Proton Bank was nationalized on 2011 during the Greek financial
crisis. Given that there are only four Greek banks operating in Greece, this nationalization
does not satisfy the large scale nationalization condition, as it was a small operating bank
established in 2001.
With regards to Euro area, there are 39 Euro area banks operating in the monetary union. In
2008 the Parex bank in Latvia and BPN - Banco Português de Negócios bank in Portugal were
nationalised. In 2009, Anglo Irish Bank was nationalized by the Irish government. Also, in
2011, Proton Bank in Greece and the Snoras Bank in Lithuania were nationalized by their
governments. Even though there have been more than a few bank nationalizations in the
Euro area, it is not large enough to be considered large scale nationalization. Thus based on
the large scale nationalization condition is only satisfied for United Kingdom in 2008, and no
other periods for the other nations.
Moreover, looking at the third condition, the cost of bank rescue operations being at least 2
percent of the GDP, the table below represent the estimated cost of bank rescue packages
as a percentage of GDP for each individual country. The percentages for Euro area is an
average of cost of rescue packages as a percentage of the average of GDP. The table shows
that Euro area and United Kingdom cross the 2 percent threshold for the years 2008 to 2013,
United States for the period of 2008 to 2011 and Greece surprisingly between 2008 and
2010. This means these countries were experiencing banking crisis for those periods. The
years with missing value in the table below, means that there were no new rescue packaged
were introduced from the previous year.
Table 2: cost of bank rescue as a % of GDP
Year
2007
2008
2009
2010
Euro area
5.10%
5.10%
5.10%
United states
4.70%
4.80%
2.70%
[18]
United Kingdom
34.40%
6.30%
6.30%
Greece
24.80%
24.80%
24.80%
2011
2012
2013
2014
5.10%
5.10%
5.10%
-
2.60%
-
6.30%
6.90%
6.90%
-
1.10%
1.10%
1.10%
-
And finally looking at the last category, the occurrence of widespread bank runs or the
government taking extreme measures like deposit freeze and/or bank holidays in response
to the banking crisis, in 2007 Countrywide Financial suffered a bank run and was bought by
the Bank of America in 2008. During the same year Northern Rock suffered a bank run in
United Kingdom. Moreover, in 2008 number of financial institution and banks in United
States, including Bear Stearns, mortgage lender IndyMac Bank, Washington Mutual and
Wachovia bank. Furthermore, in 2015 Greek banks closed due to 2 weeks of public holiday
introduced by the Greek government. Thus, the condition of widespread bank runs and
extreme action by governments as a result is satisfied for United States from 2007 to 2008,
and for Greece in 2015.
Therefore, as stated in Demirguc- Kunt and Detragiache’s paper, at least one of the four
given conditions need to be satisfied in order for an economy to experience systemic baking
crisis, and using their instruction as shown before, the table below represents periods of
banking crisis in my sample.
Table 3: Banking crises dates and durations
Country
Crisis Episode
Euro Area
Greece
United Kingdom
United States
2008-2015
1998-2000, 2008-2015
2008-2014
1980-1992, 2007-2011
The period of 1980 to 1992 for United States was taken from table 7 in appendix, which is
the table given by Demirguc- Kunt and Detragiache in their 2005 paper.
Results
In the Table 5 and 6, the variable GROWTH is the growth rate of real GDP, TOTCHANGE is
change in terms of trade, DEPRECIATION is the depreciation rate of the currency against US
dollars (base year 2010), RLINTERST is the real interest rate. INFLATION is the rate of GDP
deflator, RGDP/CAP is the real GDP per capita (constant 2010 in US dollars), FISCAL
BALANCE/GDP is the central government’s budget surplus to GDP (in local currency),
[19]
M2/RESERVES is the ratio of M2 (broad money) to international reserves (in local currency),
PRIVATE/GDP is the ratio of private sector credit to GDP (calculated in local currency then
converted to US dollars), CREDITGRO is the rate of growth of real domestic credit to the
private sector and DEPOSITION which is a dummy variable that equals to one if the country
has explicit deposit insurance and zero otherwise for individual years. The table is divided in
three sections. First, the macroeconomic variables (GROWTH, RLINTERST, INFLATION, FISCAL
BALANCE/GDP, TOTCHANGE, DEPRECIATION), second the banking sector variables
(M2/RESERVES, CREDITGRO) and third being the institutional variables (RGDP/CAP,
DEPOSITION).
Table 5: Demirguc- Kunt and Detragiache (2005) replicated table
GROWTH
TOTCHANGE
DEPRECIATION
RLINTEREST
INFLATION
RGDP/CAP
(1)
(2)
(3)
(4)
(5)
-0.09678***
(0.0259)
0.0005
(0.0061)
-0.0675
(0.3892)
0.0006***
(0.0002)
0.0007**
(0.0003)
-0.0367**
(0.0156)
-0.0991***
(0.0265)
0.0006
(0.0064)
0.0713
(0.3830)
0.0005***
(0.0002)
0.0006**
(0.0003)
-0.0359**
(0.0168)
-0.1175***
(0.0332)
-0.0028
(0.0067)
-0.1233
(0.3946)
0.0006***
(0.0002)
0.0007**
(0.0003)
-0.0544***
(0.0184)
0.0014
(0.0020)
0.0066***
(0.0022)
0.0012***
(0.0005)
0.0041*
(0.0022)
0.5859**
(0.2786)
-0.1035***
(0.0274)
0.0004
(0.0065)
0.0490
(0.3811)
0.0005***
(0.0002)
0.0006**
(0.0003)
-0.0478***
(0.0178)
0.0012*
(0.0007)
0.0010***
(0.0003)
0.0038**
(0.0019)
-0.1115***
(0.0319)
-0.0024
(0.0066)
-0.1037
(0.3918)
0.0005***
(0.0002)
0.0007**
(0.0003)
-0.0414**
(0.0175)
0.0033**
(0.0016)
0.0062***
(0.0021)
0.0016***
(0.0004)
0.0044*
(0.0023)
1612
230.12***
0.08
1356
307.22***
0.09
1356
348.82***
0.10
1612
248.72***
0.08
FISCAL BALANCE/GDP
M2/RESERVES
PRIVATE/GDP
CREDITGROt-2
DEPOSITION
Observations
Chi-sq
Pseudo- R2
1670
216.07***
0.07
0.0013*
(0.0007)
0.0010***
(0.0003)
0.0035*
(0.0019)
0.5131**
(0.2582)
Robust standard errors in parentheses
*** Significant at 1%, ** Significant at 5%, * significant at 10%
As it can be seen in table 5, low GDP growth and high inflation and real interest rates are
significantly correlated with the occurrence of banking crisis. Therefore, during the times of
[20]
economic downturn and loss of monetary control, the crises tend to manifest themselves.
Exposure to real interest rate risk, as seen in 1980s and 1990s where the real interest rate
was high and volatile compared to the previous 20 years, increases the banking fragility and
thus contributes to manifestation of banking crisis. Moreover, as it can be seen in table 5,
exchange rate depreciation and change in terms of trade are not significant. The budget
surplus scaled by GDP (fiscal variable) is only significant in column 3, where the deposit
insurance is omitted. Also it has a positive coefficient, meaning in the absence of deposit
insurance, budget surplus or low budget deficit could assist the occurrence of banking crisis.
Moreover, the coefficient of ratio of broad money to foreign exchange reserve is positive
and significant. This variable measures the currency’s vulnerability to a speculative attack
(run), this suggest that banks’ exposure to currency crisis plays a role in banking crisis as
mentioned by Kaminsky and Reinhart (1999) as the twin crises phenomena. Credit to the
private sector coefficient is positive and significant, suggesting that a banking sector
becomes more vulnerable when it is exposed to a larger number of private sector borrowers
due to mismanaged liberalization. Similarly, the two period lagged credit growth coefficient
is positive and significant in all specifications and since it is lagged, it could capture a credit
boom in the sample.
Furthermore, the coefficient for real GDP per capita is significant under all specifications and
negatively correlated with systemic banking sector problems. This variable measures the
level of development of each nation, meaning developing economies are more exposed to
bank fragility. Also, deposition variable (deposit insurance schemes) acts as risk factor due to
the negative impact of moral hazard being cancelled out by the positive impact of fall in selffulfilling panics.
[21]
Table 6: Updated version using my sample based on Demirguc- Kunt and Detragiache (2005) method
GROWTH
TOTCHANGE
DEPRECIATION
RLINTEREST
INFLATION
RGDP/CAP
(1)
(2)
(3)
(4)
(5)
-0.02759**
(0. 00960)
-0.02886
(0.01136)
-0.00391***
(0.00044)
-0.0655**
(0.010840)
-0. 11771***
(0.01968)
0.00467
(0.000572)
-0.482*
(0.00274)
-0.238
(0.0706)
-0.00166**
(0.000766)
-0.513**
(0.0551)
-0.07103**
(0.01258)
0.000196
(0.00765)
-0.01027***
(0.00157)
0.3662
(0.02603)
-0.001494***
(0.00037)
-0.02148***
(0.0522)
-0.1316***
(0.03292)
0.00652
(0.000159)
-0.00126**
(0.00059)
0.0064
(0.003728)
0.6941***
(0.1871)
0.01853**
(0.02162)
0.002438***
(0.00284)
-0.340*
(0.00183)
0.705
(0.0421)
-0.00371***
(0.000122)
-0.0256***
(0.0592)
-0.3396***
(0.01094)
0.000190
(0.0057915)
-0.0171
(0.0252)
0.7195**
(0.65288)
0.03442**
(0.02184)
-0.826**
(0.00343)
-0.416
(0.0614)
-0.00249**
(0.00111)
-0.043081**
(0.01668)
-0.11681**
(0.02848)
0.000105
(0.000121)
-0.000103
(0.000611)
-0.0895
(0.0654)
0.5773***
(0.50148)
0.0649***
(0.0639)
106
0.8761
106
0.8898
106
0.9211
106
0.8869
FISCAL BALANCE/GDP
M2/RESERVES
PRIVATE/GDP
CREDITGROt-2
DEPOSITION
Observations
Pseudo- R2
110
0.6085
0.00782
(0.0249)
0.1838***
(0.5305)
0.01839**
(0.01596)
0.00141***
(0.00354)
Robust standard errors in parentheses
*** Significant at 1%, ** Significant at 5%, * significant at 10%
Table 6 presents the result from my sample using the same method as Demirguc- Kunt and
Detragiache (2005). As it can be seen low GDP growth significantly correlated with the
occurrence of banking crisis, similar to the result of Demirguc- Kunt and Detragiache (2005),
which supports the idea that during the times of economic downturn the crises tend to
manifest themselves. However, real interest rate and inflation rate in my sample are
negative and significant, whereas in Demirguc- Kunt and Detragiache they are positive and
significant. This means that low inflation and real interest rates are associated with the
occurrence of banking crisis in this sample. This difference could be due to the years leading
up to 2007-2008 financial crisis, where all these nations were experiencing inflation at 2
percent (± 1 percent) target and the real interest rates were below 3 percent, which could
make these economies more susceptible to banking fragilities by reducing the effectiveness
of interest rate cuts in face of banking sector problems.
Moreover, exchange rate depreciation is negative and significantly correlated with the
occurrence of banking crisis, unlike the results in table 5. Negative depreciation
[22]
(appreciation) of Euro and British pound against the U.S dollar suggest possible outflow of
capital from United States, which could indicate banking sector fragility. However, similar to
table 5, change in terms of trade are not significant. The budget surplus scaled by GDP (fiscal
variable) is only significant in column 4, where the deposit insurance is not omitted. Also it
has a negative coefficient, which means that fiscal deficit, even when there is deposit
insurance could increase the occurrence of banking crisis, as the public know that the
government may not be able to fully complete its obligations in terms of paying the deposit
insurance.
Moreover, the coefficient of ratio of broad money to foreign exchange reserve is
insignificant in table 6 unlike the results in table 5. This variable measures the currency’s
vulnerability to a speculative attack (run), but since the three currencies in my sample are
the main currencies for trading globally, banks in my sample nations do not face exposure to
currency crisis. Credit to the private sector coefficient is positive and significant, suggesting
that a banking sector becomes more vulnerable when it is exposed to a larger number of
private sector borrowers due to mismanaged liberalization. Similarly, the two period lagged
credit growth coefficient is positive and significant in all specifications and since it is lagged,
it could capture a credit boom in the sample. Results for these two coefficients are similar to
the ones in table 5.
Furthermore, the coefficient for real GDP per capita is insignificant under all specifications.
Also, deposition variable (deposit insurance schemes) is positive and significant the same as
the results in table 5, which acts as risk factor due to the negative impact of moral hazard
being cancelled out by the positive impact of fall in self-fulfilling panics.
In comparison, table 6 does not report any Chi_sq results as they were missing in the Stata
estimations. This does not mean that there is something necessarily wrong with the model,
Stata has done this in order not to be misleading. Moreover, the Pseudo R- squareds in table
5 are lower than those in table 6, which indicates that the model fits the data used in table 6
better than the data used in table 5.
Conclusion
[23]
[24]
Appendix
Table 7: Banking crises dates and durations by country
Country
Algeria
Argentina
Benin
Bolivia
Brazil
Burkina Faso
Burundi
Cameron
Central African Republic
Chad
Chile
Colombia
Congo, Rep
Congo, Dem. Rep.
Costa Rica
Côte d'Ivoire
Ecuador
El Salvador
Finland
Ghana
Guinea
Guinea-Bissau
Guyana
India
Indonesia
Israel
Italy
Jamaica
Japan
Jordan
Kenya
Korea, Republic of
Lebanon
Liberia
Madagascar
Malaysia
Mali
Mauritania
Mexico
Nepal
Crisis Episodes 1980 - 2002
1990-1992
1980-1982, 1989-1990, 1995, 2001-2002*
1988-1990
1986-1988, 1994-1997**, 2001-2002*
1990, 1994-1999
1988-1994
1994-1997**
1987–1993, 1995–1998
1988–1999
1992
1981–1987
1982–1985, 1999–2000
1992–2002*
1994–2002*
1994–1997**
1988–1991
1995–2002*
1989
1991–1994
1982–1989, 1997–2002*
1985, 1993–1994
1994–1997**
1993–1995
1991–1994**
1992–1995**, 1997–2002*
1983–1984
1990–1995
1996–2000
1992–2002*
1989–1990
1993–1995
1997–2002
1988–1990
1991–1995
1988–1991**
1985–1988, 1997–2001
1987–1989
1984–1993
1982, 1994–1997
1988–1991**
[25]
Niger
1983–1986**
Nigeria
1991–1995
Norway
1987–1993
Panama
1988–1989
Papua New Guinea
1989–1992**
Paraguay
1995–1999
Peru
1983–1990
Philippines
1981–1987, 1998–2002*
Portugal
1986–1989
Senegal
1983–1988
Sierra Leone
1990–1993**
South Africa
1985
Sri Lanka
1989–1993
Swaziland
1995
Sweden
1990–1993
Taiwan, Province of China
1997–1998
Tanzania
1988–1991**
Thailand
1983–1987, 1997–2002*
Tunisia
1991–1995
Turkey
1982, 1991, 1994, 2000–2002*
Uganda
1994–1997**
United States
1980–1992
Uruguay
1981–1985, 2002*
Venezuela
1993–1997
Notes: *The crisis is still ongoing as of 2005.
**The end date for the crisis is not certain, a four-year duration is
Demirguc- Kunt and Detragiache (2000), defines four fragility zones, increasing in the level of
fragility based on Type I and Type II errors. The probability intervals for each zone are: Zone I,
0.000-0.018; Zone II, 0.018-0.036; Zone III, 0.036-0.070; Zone IV, 0.070-1.000.
Table 7
Banking Crisis
Jamaica (1996)
Indonesia (1997)
Korea (1997)
Malaysia (1997)
Philippines (1997)
Thailand (1997)
Estimated Crisis Probability
Based on Actual Data
Based on Forecast Data
11.0
6.0
14.4
2.4
4.4
2.3
3.7
1.8
5.9
3.5
13.8
3.3
[26]
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