Transcript Saktinil, Roy_presentation_ASSAl - AUSpace

Causes of Banking Crises:
Deregulation, Credit Booms and
Asset Bubbles, Then and Now
Saktinil Roy (Athabasca University)
David M. Kemme (University of Memphis)
Objectives
• How similar are the global financial crisis
of 2008-2009 and historical banking crises
around the world?
• What factors are most common in the runup to banking crises?
• What factors triggered the global financial
crisis of 2008-2009?
Background and Emerging
Literature
• The collapse of housing bubbles and the
global financial crisis of 2008 naturally
prompted the emergence of a literature that
seeks to identify and explain the factors that
triggered the crisis.
• In the literature as well as in debates in
policy circles, the most commonly cited
causes are:
• Lax regulation and oversight
• Risky financial innovations and excessive risks
taken by financial institutions
Background and Emerging
Literature (contd..)
• Capital and credit flows in the aftermath of the
Asian financial crises in 1997
• Interdependencies in assets that increased the risk
of contagion and reduced the ability of risk
sharing
• The low interest rate policy of the Federal Reserve
and other central banks in early 2000s helped to
inflate the housing bubbles by encouraging easy
availability of mortgage loans
• Increase in risky mortgage debts has been partly
an effect of the rise in income inequality over the
last three decades.
Background and Emerging
Literature (contd…)
• Reinhart and Rogoff (2008, 2009), Krugman
(2007, 2009), Bordo (2008), Gorton (2010),
Almunia et. al (2009), Roubini and Mihm
(2010), and Rajan (2010) are only few of the
authors contributing in this area
• A rekindled interest in banking crises has also
inspired an empirical literature that examines
probable common patterns across banking
crises based on their predictability
Background and Emerging
Literature (contd…)
• Following the literature of early warning systems for
financial crises, Barrell et. al. (2010), Jorda et. al. (2011),
Roy and Kemme (2011) and others have investigated
robust similarities in the run-ups to the global financial
crisis and historical banking crises.
• The present paper also examines robust similarities
across the global financial crisis and historical banking
crises based on their predictability.
• We consider a larger set of crisis indicators, a longer
historical period and a greater number of historical
crisis episodes in advanced countries -- present fresh
results that are not always similar to those in the extant
literature.
Model and Data
• Two most popular approaches to predicting
financial crises:
• i) “Signal method” (Kaminsky et. al. , 1998, 1999, 2000)–
examines individual indicators and issues alarms if they indicate
anomalous behaviour exceeding a threshold;
• ii) Logit/probit models (Berg and Pattillo 1999, Demirguc-Kunt
and Detragiachem,1998; and Davis and Karim, 2008) examine the
joint effects of several indicators on the probability of crisis.
Alarms are issued if the estimated probability of crisis exceeds a
pre-specified threshold; Statistical significance of each indicator
can be tested.
• We examine several specifications of bivariate and
multivariate panel logit models to examine the joint
effects of different combinations of indicators on the
probability of crisis and to better capture fat tails in the
data
Model and Data (contd..)
• We ask, specifically, if the historical crises and
the current global crisis can be predicted to
occur within a period of three years.
• The corresponding modeling strategy requires
that all three observations prior to a crisis
episode are labeled as “pre-crisis” and all
observations prior to these three years are
labeled as “tranquil.”
• For regression purposes, the dependent variable
representing crisis probability is assigned the
actual value one when the observation is “precrisis” and zero when the observation is
“tranquil.”
Model and Data (contd..)
• The two essential criteria for assessing
crisis similarity are:
• Percentage of “pre-crisis” years correctly called
(conditional probability of an alarm given a crisis
within three years)
• True alarms as a percentage of total alarms
(conditional probability of a crisis within three years
given an alarm)
Model and Data (contd..)
• The Choice of Threshold
• The first preference under all conditions: 50%
• No bias towards pre-crisis or tranquil observations
• However, for a rare event, such as banking crisis, only a few
alarms are generated
• Candelon, Dumitrescu and Hurlin (2009) recommend several
alternative criteria for the determination of an optimum threshold
• The minimum bias toward either pre-crisis or tranquil
observations: a threshold that simultaneously and conditionally
maximizes the percentage of pre-crisis observations correctly
called and the percentage of tranquil observations correctly called
in the within sample exercises.
• We apply both 50% and optimum thresholds and compare the
results
Model and Data (contd..)
Crisis indicators
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Current account as a percentage of GDP (CA)
Growth rate of per capita real GDP (GGDP)
Public debt as a percentage of GDP
Real house prices (RHP)
Real share prices (RSP)
Income inequality (IE)
Central bank real interest rate (RIR)
M2/reserves (M2/R)
Bank liquidity (LIQ)
Currency appreciation (APP)
Private sector debt as a percentage of GDP (PVD)
Model and Data (contd..)
• Data: Historical crisis episodes in advanced countries
since World War II --Same as in Reinhart and Rogoff
(2009), depending on the availability of data.
• Due to a large number of missing observations, following
the econometric literature, employ a “choice-based”
sample:
• Make the sample distribution symmetric across historical
crisis experiences.
• Test similarities with within sample and out of sample
prediction exercises.
• However, no suitable quantitative measure to identify a
banking crisis (Reinhart and Rogoff, 2009) – quite unlike
in the case for currency crisis.
• Following Reinhart and Rogoff (2009), identify the
crises by the dates on which they were announced.
Model and Data (contd..)

For the out of sample exercise consider a representative
sample of crises:
• USA (2007); Britain (2008); Ireland (2008); Spain (2008)
• All four countries had spectacular prior housing bubbles
and suffered the most severe banking crises – also found in
Reinhart and Rogoff (2008), inter alia.
• To check the robustness of results, we also predict out of
sample the relatively tranquil environments of Australia,
Canada and New Zealand around 2008-2009.
• To further check the robustness of results from logit model
specifications and to identify which indicators primarily
contributed to the global financial crisis, we report Granger
causality test results on a selected set of indicators.
Results from Estimation and
Forecasting
Significance of Coefficients
• In the bivariate specifications : current account, real GDP
growth, real interest rate, income inequality, M2/reserve,
private sector debt, real share prices and real house prices are
significant at 5% or 10% level.
• In the multivariate specifications: real GDP growth, income
inequality, private sector debt, real share prices and real house
prices are significant at 5% or 10% level in most specifications
Results from Estimation and
Forecasting (contd..)
Within Sample Predictions
 Bivariate specifications:
 At 50% threshold, PVD, RSP, and RHP predict 37%-63%
of the pre-crisis years correctly and issue alarms that
are correct 47%-66% of the time.
 At 50% threshold, ALL other indicators fail either
completely or nearly so.
 At optimum threshold, the best performances are by
PVD, RSP, and RHP: 69 – 76% of the pre-crisis years are
correctly called and 52-55% of the alarms are true.
 At optimum threshold, the only other notable
performance is by income inequality (IE): 67% of the
pre-crisis years are correctly called and 42% true
alarms.
Results from Estimation and
Forecasting (contd..)
• Multivariate Specifications
• At 50% threshold, the specification containing only
PVD, RSP and RHP predicts 61% of the pre-crisis years
correctly and generates 75% true alarms.
• At optimum thresholds, the same specification
predicts 81% of the pre-crisis years correctly and
generates 64% true alarms.
• Adding any other variable to this combination of three
variables does not improve prediction performance
significantly.
• The worst performance is by the specification which
has ALL variables except PVD, RSP and RHP.
Results from Estimation and
Forecasting (contd..)
Out of Sample Predictions
• Bivariate specifications
 At 50% threshold, PVD, RSP, and RHP predict 67%-100% of
the pre-crisis years correctly and issue alarms that are true
30%-100% of the time.
 At 50% threshold, ALL other indicators fail either completely
or nearly so.
 At optimum threshold, PVD, RSP, and RHP predict 100% of
the pre-crisis years correctly and issue alarms that are
correct 30%-100% of the time.
 At optimum threshold, notably CA also does very well:
100% of the pre-crisis years are correctly called and 75% of
the alarms are true.
 At optimum threshold all other indicators do much worse.
Results from Estimation and
Forecasting (contd..)
• Multivariate Specifications
• The best performance is by the specification that has
PVD, RSP, RHP and IE.
• At 50% threshold, 100% of the pre-crisis years correctly
called and 86% of the alarms are true.
• At optimum threshold, 100% of the pre-crisis years are
correctly called and 75% of the alarms are true.
• No other specification does nearly as well.
• The worst performance is by the specification that has
ALL indicators except PVD, RSP and RHP.
Results from Estimation and
Forecasting (contd..)
Check Robustness of Results
• Log probability scores, Quadratic probability scores,
Global squared bias, McFadden R-squared, AIC, SIC and
HIC confirm the results.
• Out of sample predictions of the tranquil observations in
the tranquil countries (Canada, Australia and New
Zealand) and crisis countries (US, UK, Ireland and Spain)
are consistent.
Granger Causality Test Results
• For crisis countries, if there is any causality
between CA and RHP or PVD then it is from RHP
or PVD to CA.
• For tranquil countries quite the opposite is true.
• In the US and Ireland, any possible causality
between IE and RHP or PVD is from the former
to the later.
Conclusions
Similarities of banking crises are most robust in terms of prior
stock and real estate bubbles in an era of increasing financial
deregulation where private sector debt continues to expand.
 Lends credence to Shiller’s (2005, 2008) dictum that, often
spurred by “new era” stories of indefinite future increases of
asset prices, the over-optimism of market participants— and
sometimes also of policy makers and economists— sets the
stage for a financial crisis, particularly in a period of regulatory
imprudence.
 A sustained rise in income inequality is the most important
other factor that demonstrates a similarity between the
current global crisis and historical banking crises.

Conclusions (contd.)


Sustained surges in capital inflow, increasing GDP share
of public debt, falling growth rate of real GDP per capita,
the central bank’s low interest rate policy, and even
rising income inequality, when taken independently from
bubbles in equity and housing markets, largely fail to
demonstrate similarities across historical banking crises
and the current global crisis.
The important implication: on most occasions of a
systemic banking crisis— even as catastrophic as to roil
the whole global financial system— there need not be a
prior sustained surge in capital inflow, or the central
bank having slashed the key interest rate for a prolonged
period, or even structural shocks restraining economic
growth.
Conclusions (contd..)
• It also follows that increasing financial deregulation over
three decades that helped to increase private sector debt
and to form a spectacular housing bubble contributed
most to the global financial crisis of 2008.
• The rise in income inequality over the same period was
the next most important factor that contributed to the
crisis.
• Large capital inflows in the aftermath of the Asian
financial crisis might have contributed to the global
financial crisis, but it was a secondary factor.