Macroeconomic, financial and institutional determinants of banking crisis - the money market pressure index approach.pdf

1. UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
MACROECONOMIC, FINANCIAL AND INSTITUTIONAL
DETERMINANTS OF BANKING CRISIS:
THE MONEY MARKET PRESSURE INDEX APPROACH
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
CHAU THE VINH
Academic Supervisor:
Assoc. Prof. NGUYEN TRONG HOAI
HO CHI MINH CITY, December2014

2. i
CERTIFICATION
“I certify that the substance of this thesis has not already been submitted for any
degree and has not been currently submitted for any other degree.
I certify that to the best of my knowledge and help received in preparing this thesis
and all used sources have acknowledged in this dissertation”.
CHAU THE VINH
Date: 31st
December 2014

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ACKNOWLEDGEMENT
Upon completing this thesis, I have received a great deal of encouragement and
support from many people.
First of all, I would like to express my deepest gratitude towards Assoc. Prof.
Nguyen Trong Hoai, my esteemed academic supervisor, for his patient guidance,
encouragement and valuable critiques for my research work.
Also, I would like to thank Dr. Truong Dang Thuy for his guidance and advice in
econometric techniques, Dr. Pham Khanh Nam for his encouragement and valuable
advice in the starting phase of my thesis research design.
My gratefulness is also extended to all of my lecturers and staffs of the Vietnam-
Netherlands Program for their assistance during my first days in this programme.
Besides, I would love to thank my parents and my families for their ceaseless
encouragement and support during my study period. Moreover, my special thanks
to my C.E.O – Mr. Nguyen Huu Tram, who understands and gives me approval for
my long personal leave to finalize my thesis on time. Without them, I would not
have opportunities and incentives to have my thesis finished.
Finally, I would like to thank all my friends and other people who have had any
help and support for my thesis but are not above-mentioned.

4. iii
ABSTRACT
The thesis estimates a logit regression model by fixed effect with a combination of
some macroeconomic and financial indicators from the work of Hagen and Ho
(2007) and Worldwide Governance Indicators (WGI) from the updated database of
Kaufmann (2013) as explanatory variables for binary dependent variable banking
crises generated from the approach of money market pressure index (Hagen and Ho,
2007). The monthly panel dataset, which is available in full range and easy of
approach from International Financial Statistics CD-ROM (2011), of 18 countries
from Latin America and Asian over the scope of 2001 – 2010is applied. Some
specific lag lengths of indicators are also applied according to the suggestion of
“flexibility in forecast horizon” of Drehmann et al. (2011).
The crisis phenomenon of banking system seems to be well-described in light of the
present of depreciation, former year crisis, high real interest rate in prior of 36
months, growth of credit to GDP in prior 12 months. Moreover, impact of inflation
seems to support the school of thought that it is negative effect to crisis.
Simultaneously, growth rate of bank deposits to GDP is likely useful to prevent
banking systems from profitability risks exposure that leads to banking crisis
probability. However, unfortunately, the indicators of growth of monetary base and
growth of M2 to reserves give incorrect expected sign and negligible effect on
banking crisis. Furthermore, the included institutional variables from WGI give
insignificant statistic meaning. Hence, another set of institutional indicators such as
that from International Country Risk Guide (ICRG) should be considered in future
analysis to test for the relationship between Government health and banking crisis
probability.
Despite, on one hand, there should be a more adequate research to be examined in
the future, this thesis attempts to contribute so-called new updates information on
the would-be banking crisis determinants. Nevertheless, on the other hand, there is
likely no proper explanation on the tranquil periods of banking system. Hence, it is

5. iv
suggested that thereshould be some assessment ofsuch time of banking system,
which over a long time has beenneglected (Kauko, 2014).
Key words: banking crisis, tranquiltime, determinants, institutional indicators, fixed
effect logitregression.

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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION .................................................................................................1
1.1. Problem statement.........................................................................................................1
1.2. Research objective ........................................................................................................3
1.3. Research question..........................................................................................................3
1.4. Structure of the thesis....................................................................................................3
CHAPTER 2: LITERATURE REVIEW ......................................................................................5
2.1. Defining banking crisis .................................................................................................5
2.2. Trends of banking crises researchtogether with crises mechanism...............................7
2.2.1. The first trend............................................................................................................8
2.2.2. The second trend .....................................................................................................10
2.2.3. The third trend.........................................................................................................14
2.3. Money Market Pressure (MMP) Index (Hagen and Ho, 2007)...................................19
2.4. Chapter summary ........................................................................................................21
CHAPTER 3: METHODOLOGY, MODEL SPECIFICATION AND DATA..........................28
3.1. Model selection...........................................................................................................28
3.2. Model specification.....................................................................................................31
3.2.1. Macroeconomic indicators......................................................................................33
3.2.2. Financial indicators .................................................................................................34
3.2.3. Institutional indicators.............................................................................................36
3.2.4. Use of lagged terms.................................................................................................37
3.3. Estimation strategies and relevant model diagnostics.................................................40
3.3.1. Calculation of MMP for banking crisis assessment ................................................40
3.3.2. Model estimation steps and diagnostics..................................................................41
3.4. Data scope and sources ...............................................................................................43
3.5. Conceptual framework................................................................................................46
3.6. Research Process.........................................................................................................47
CHAPTER 4: RESUTLS AND FINDINGS...............................................................................48
4.1. Descriptive statistics of explanatory indicators...........................................................48
4.2. Statistical tests for model ............................................................................................51
4.2.1. Model specification test ..........................................................................................51
4.1.2. Goodness of fit test..................................................................................................51
4.1.3. Test for multicollinearity.........................................................................................51

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4.3. Coefficients interpretation...........................................................................................53
4.3.1. Macroeconomic indicators......................................................................................53
4.3.2. Financial indicators .................................................................................................55
4.3.3. Institutional indicators.............................................................................................57
CHAPTER 5: CONCLUSION, POLICY RECOMMENDATION AND LIMITATION..........58
5.1. Conclusion ..................................................................................................................58
5.2. Policy recommendation...............................................................................................58
5.3. Limitation of the research ...........................................................................................60
REFERENCES............................................................................................................................61
APPENDICES ............................................................................................................................65
Table 2.1 Summary of literature reviewed..............................................................................22
Figure 2.1 Mechanisms of banking crisis................................................................................27
Table 3.1 Data for MMP index calculation.............................................................................44
Table 3.2 Data and sources of explanatory variables..............................................................45
Table 4.1 Banking crisis dates retrieved from MMP index ....................................................65
Table 4.2 Summary statistics of variables used in the regression...........................................49
Table 4.3a The correlation on the sample observations..........................................................50
Table 4.3b The correlation on the sample observations..........................................................50
Table 4.4Linktest for specification error of logit model .........................................................66
Table 4.5 Goodness of fit test of model ..................................................................................67
Tabel 4.6 Full model multicollinearity test result ...................................................................67
Table 4.7 Dropping significantly high correlated variables GE, RL: .....................................68
Table 4.8 Dropping high correlated variables GE, RL and CC ..............................................68
Table 4.9 Using interactive term of GE and RL .....................................................................69
Table 4.10 Full model .............................................................................................................69
Table 4.11 Restricted model without GE, RL, CC..................................................................70
Table 4.12 Fixed effect model with lags.................................................................................70
Table 4.13 Random effect model with lags.............................................................................71
Table 4.14 Simple logit model with lags ................................................................................72
Table 4.15Comparison of lagged terms of indicators in simple logit, FEM and REM...........73

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ABBREVIATION
MMP: Money Market Pressure
WGI: World Governance Indicator
WB: World Bank
IMF: International Monetary Fund
IFS: International Financial Statistics
ICRG: International Country Risks Guide
FEM: Fixed Effect Model
REM: Random Effect Model
BC: Banking Crisis

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CHAPTER 1: INTRODUCTION
1.1. Problem statement
Banking crisis in nowadays economies is not a new issue or even an old one that
has been given awareness to, discussed and researched from many angles and
perspectives by applying many approaches from simple to complicate. There have
been three trends of banking system crisis researches from its first trend of
qualitative description by Friedman and Schwartz (1963) about US crisis over its
past decades to the second trend in which econometric analysis with panel data were
employed according to relatively enough banking crises observations and to the
third trend since the 2007 “global financial turmoil”. The trends of banking crisis
research contribute most of important indicators related to macroeconomics and
banking sectors such as reserves, current account, real exchange rate (Kaminsky et
al, 1998). Despite the fact that the logistic regression approach focused more on
quantitative economics model, it has seemed to be an important tool for anticipating
the crisis signals and timing as well as significant indicators. However, there was
also some noise that could affect the effectiveness of this model. Hence, it led to the
rise of further studies in terms of developing new method and other new critical
variables.
As suggested, there have been many criteria to help researchers with banking crisis
identification. Amongst, money market pressure index from the work of Hagen and
Ho (2007), who expanded the literature of Eichengreen, Rose, and Wyplosz (1995,
1996a, 1996b) for currency crisis, stands out to be convenient for understanding and
data collecting but still provide good judgment value for banking crisis symptom.
Such index observed the periods that banking systems experience its liquidity
problem by considering simultaneously the phenomenon of both high central bank
reserves demand and fluctuations of short-term real interest rate. Originally, the
index provides the criterion to indicate whether there is a crisis or not under the
scope analyzed.
Banks relevant data, to some extent, seems to be difficult to obtain precisely due to

10. 2
their sensitiveness. Given those difficulties, the research will make use of
macroeconomic indicators as suggested in a survey that emphasized “the analysis of
macroeconomic variables is of some help for banking supervisors in order to fully
assess banks’ health” (Quagliariello, 2008). In accordance with both suggestion
from Quagliariello (2008) and Hagen and Ho (2007), some available
macroeconomic and financial variables such as inflation, growth of monetary base,
depreciation, real interest rate, growth of private credit over GDP, growth of
deposits over GDP and growth of M2 over reserves are examined. In recent years,
there has been the use of institutional signals (Kaufmann et al, 2008) to predict for
the probability of vulnerability and crisis occurrence besides quantitative economic
indicators to enhance the limitation of the model by Kaminsky et al (1998).
Moreover, being motivated by the work of Breuer et al. (2006) on institutional
variables and currency crisis, this research will take this idea together with the
combination with six updated world governance indicators (Kaufmann, 2013)
namely voice and accountability, government effectiveness, political stability, rule
of law, regulatory quality, control of corruption to assess the role of “health” of
Government in the relationship with crisis time of the banking systems. Last but not
least, the 12-month lagged term of banking crisis included into the regression model
(Falcetti and Tudela, 2006) also give significant assessment.
Nevertheless, it seems that most of relevant researches tend to try to explain the
reasons for a banking crisis occurrence but not that why banking crisis does not take
place in some situation over some period in some country. The attempt to
understand or even forecast the crisis is important on one hand. But, on the other
hand, future researches should be carried out with the tranquil time of the banking
system, i.e. the “non-crisis” situation, still has its important role which seems to be
belittled or even no need to be explained (Kauko, 2014).
Although there have been researches and studies on banking crisis, it seems that
there are likely few works considering simultaneously the health of Government,
macroeconomic and financial background in a same model. Thus, the contribution

11. 3
of this thesis is to employ a combination of MMP index approach with updated data
from IMF – IFS over the year scope of 2001-2010 to analyze the somewhat overall
banking crisis phenomenon under the impacts of the macro-economy environment,
the financial situation and institutional indicators. The rationale of such approach is
that there may be more useful findings will be figured out for banking crisis
analyses as well as more awareness will be taken into account from the perspectives
of authorities’ management for banking sector, in particularly, and for the economy
in general.
1.2. Research objective
This thesis, whose attempt is to contribute an updated research on benign periods of
banking systems through the analysis of banking crisis, will focus on the objectives
which try to identify factors of macroeconomics, finance and institutions that are
useful for explaining the occurrence of banking systems crisis.
1.3. Research question
Which are the macroeconomic, financial and institutional indicators that provide
awareness for the crisis time of banking system?
1.4. Structure of the thesis
After the finish of Chapter 1 about thesis introduction, the rest of this thesis will be
categorized as following chapters:
Chapter 2 introduces banking crisis definition, relevant literature reviews of trends
of banking crisis researches, money market pressure index which will be applied for
banking crisis dependent variable identification.
Chapter 3 states the methodology, model choice and specification and data scope
used. This chapter also gives readers clear arguments on explanatory variables used,
suggested statistical diagnostics of significance of model and variables.
Simultaneously, data scope and sources together with model conceptual framework
and analytical framework are also declared.
Chapter 4 interprets results and findings of thesis regression model.

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Chapter 5 concludes with policy recommendation, thesis limitation and further
research suggestion.

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CHAPTER 2: LITERATURE REVIEW
This section demonstrates the defining work of banking crisis and choice of the
author for the appropriate definition from the perspective of understandability and
data availability. Simultaneously, the research history of banking crises over time
are also introduced and discussed in terms of approaching methods applied,
particular researchers, and dataset collections.
Henceforth, this chapter includes four parts which will be introduced one by one in
order from the first part of banking crisis definition to the second part of the
introduction of three trends of banking crisis analyses. The third part of this section
gives detailed explanation and discussion on money market pressure index used by
Hagen and Ho (2007)and the last part will concludes all related literature of this
chapter.
2.1. Defining banking crisis
Banking crisis by the definition of IMF (1998) is the situation that “bank runs and
widespread failures induce banks to suspend the convertibility of their liabilities, or
which compels the government to intervene in the banking system on a large scale”.
In another work of Demirgtic-Kunt and Detragiache (1998), the concept of banking
crisis was defined as event method whose conditions are that one or the entire
following phenomenon holds:
1) The existence of at least 10% of the ratio of non-performing assets over total
assets in the banking system.
2) Cost of the rescue packages reached at least 2% of GDP.
3) Extensive nationalization of banks due to banking sector problems.
4) Governmental regulation of deposit guarantee, large-scale bank runs, long
holidays of banks, deposit freeze.
However, this definition of banking crisis has some drawbacks. Firstly, the cost of
rescue packages from the Government were unclear until after a crisis occurred
leading to late identify of this crisis. Long banks holidays, nationalization of banks
seem to happen after the entire economy was hit by crisis. Secondly, it is difficult to

14. 6
determine the extent to which Government did intervene to help banks facing with
crises. Thirdly, the intervention of authorities may be early or late, hence, the
accurate dates are often uncertain (Caprio and Klingebiel, 1996a). Finally, the event
method only classifies the crises when there are enough severities to accelerate
market events. Consequently, crises identification based on the events of policy
responses are biased in the nature of biased event selection. This, with no doubt,
limits the ability for banking crises likely determinants to prove their analytic
values.
With the attempt to contribute an alternative identification for banking crisis, the
money market pressure index (MMP) was built up in the work of Hagen and Ho
(2007) who were motivated by the ideas of Eichengreen (1995) on currency crises
analyses. Henceforth, the banking crisis is defined as “periods in which there is
excessive demand for liquidity in the money market” (Hagen and Ho, 2007). The
rationale for this index to be born comes from the traditional assumption that the
short-term interest rate, i.e. the opportunity cost for banking sector to hold reserves,
has a negative relationship with its reserves demand for central bank. The
hypothesis that “banking crisis is characterized by a sharp increase in the banking
sector's aggregate demand for central bank reserves” (Hagen and Ho, 2007, p.1039)
can be analyzed through three reasons:
- Banks confront with increasing non-performing loans and/or significant
decline in bank loans quality leading to illiquidity, hence, a rising in demand
of reserves to retain liquidity.
- When sudden withdrawals occur, there will be a pressure for banks to deal
with interbank market and central bank to be refinanced.
- Government bonds and other more guaranteed assets are favored by financial
institutions rather than lending to those in troubled leading to “a drying up of
inter-bank lending”.
With the attempt to react to the increasing demand for reserves, central bank, who is
the last lender, will enact two basic policies on either bank reserves targeting or

15. 7
short-term interest rate targeting. In the first scenario, short-term interest rate will
increase. For the latter, an injection of reserves into the banking system through the
mechanism of OMO or discount window lending must be carried out. As a result,
the existence of either the symptom of drastically increasing of short-term interest
rate or the amount of reserves of central bank, or even both, denoting money market
is under high pressure. Thus, with a convincing reasoning, the index of money
market pressure may capture the vulnerabilities of banking sector and be defined as
“the weighted average of changes in the ratio of reserves to bank deposits and
changes in the short-term real interest rate. The weights are the sample standard
deviations of the two components” (Hagen and Ho, 2007). The index can be
described by the equation herewith:
Where denotes reserves to bank deposits ratio which will, when money market
confronts high tension, increase in the case of injecting reserves from central bank
to banking system or in the case there are withdrawals of depositors.r denotes short-
term real interest rate, and are different terms of and , and are the
standard deviations of the two components respectively.
The judgment for banking crisis (BC) will be shown below:
{
After the defining work of banking crisis are finished, the following parts of this
chapter introduce the three research trends of banking crises to provide readers with
an overall picture of crisis empirical researches existing so far. The last part of this
chapter is a detailed review of methodology and results of the study of Hagen and
Ho (2007) as a conjunction for the Chapter 3.
2.2. Trends of banking crises researchtogether with crises mechanism
Going through the history of banking system fragility, from the first popularly cited
qualitative description of US crisis of Friedman and Schwartz (1963) to the so-
called seemingly first banking crisis database of Caprio and Klingebiel (1996a,

16. 8
1996b) and the widely cited works of Demirguc-Kunt and Detragiache (1998) and
Kaminsky and Reinhart (1999), banking panics or banking crises, on the whole,
were and have been caused by somewhat similar factors such as the health
economy and/or Government, the fragility of banking system itself, some contagion
effect from the outside world/ economies, etc…Given those similarities in
mechanism(s), each period has its own approaching method to the assessment of
specific banking system distress based on the availability of data, techniques and
even support from statistical software packages. The following words will introduce
in details the existing trends together with their relevant approach and the
mechanism, if any, with the intention to provide readers with an overview of
banking crises research and analysis. Some arguments on approaching methods are
also discussed in this section following the categorized suggestion of Kauko (2014).
2.2.1. The first trend
Description of specific historic events is mainstream of the first trend of banking
crisis analyses. The below words introduce some authors of this trend.
Friedman and Schwartz (1963) in the work of “Monetary history of the United
States, 1867–1960” mentioned about bank run over the observations of an increase
of short-term interest rate and a decline in the ratio deposit over currency. As cited
by Waldo (1985), bank tends to guaranty its withdrawal by selling long-term
securities prematurely leading to a rise in yield of short-term assets. In addition,
with losses by the tradeoff between withdrawal readiness and the selling of
securities before maturity, bank has no choice but default some of its deposits
making the depositors rush to shift their deposits into cash to somewhat self-protect
themselves against risks of bank-run. Moreover, the banking crisis in October 1930
supported for this point of view that some banks experienced failures making the
public, on one hand, attempt to convert their deposits into cash. On the other hand,
this effect spread out to the whole banking system all over the country generating a
collapse of the US banking system in December 1930. Not long after that, the

17. 9
period from March to June 1931, the second wave of crisis occurred more severe
because the banking system had been unhealthy during the former crisis.
Herrala (2011) contributes a description on Finnish crisis within the scope of 1865
– 1998 from the perspective of profitability of bank by using case studies of banks
in Finland. The study shows that observations made by Herrala give evidence that
series of event triggering banking crisis in Finland seem to go in line with other
former studies using either data of others countries or international. The study
conducts a definition of banking crisis under the condition of incidental occurrence
of negative profitability of banking sector. By using available statistical data at the
time being, the study has made an attempt to figure critical characteristics of
banking crisis and the crisis cycles which may deteriorate financial status of
banking sector. For the purpose of comparison, the study, then, take advantages of
those findings from studies of international banking crises. Indicators affecting the
advance phase of banking crisis cycle are sought by analysis of the periods whose
features are similar to those indicating typical case of banking crisis cycle when
financial conditions of banking sector are still healthy. In addition to the main
explanatory factor of bank profits over total assets, the study includes some other
statistical descriptive factors such as growth of real GDP, investment, inflation,
volume export change, stock money, exchange rate, interest rate, total assets
change, portion of bank deposits over loans, etc…
Gorton (1988)introduced econometric evidence on determinants of banking panics
in US before WW1, i.e. U.S. National Banking Era (1863-1914), by the analysis of
banking panics and the depositors’ behaviors. Moral hazard, i.e. the role of agency,
issue was also mentioned. The research emphasized that the banking panics might
be caused by the changing in perceptions for risks of depositors. Some indicators
were taken into account such as deposits ratio, liabilities.
Such econometric based researches made a link between the first trend and the
second trend which will be introduced below.

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2.2.2. The second trend
In the condition of relatively adequate information of observations of banking crises
and relevant useful data, econometric researches have been deployed together with
panel data. In this trend, banking crises were likely explained by the use of
macroeconomic and financial factors. Usually, researchers use the samples of panel
data with many countries over very long period of time, but the analyses seem to
focus on developed countries. In addition, the crisis here only captured two
extremes of the situation whether there is crisis or not, this is the so-called
dichotomy nature as discussed in some papers of this trend.
Being a highly attracted issue, banking crises phenomenon of this second trend
obtained an important contribution from Caprio and Klingebiel (1996a, 1996b)
whose work has been considered to be the first banking crisis database with crisis
dates, countries and some economic explanatory variables together with
observations on policy measures. The focus of this research was on the insolvency
of banks in the relation with readiness of more data could be collected such as GDP,
inflation, monetary growth, fiscal balances, trade balances, real deposit rate,
financial deepening, real credit/GDP, etc…from 69 countries over the period of late
1970-1996. In-depth interviews with experts in this field were carried out to obtain
episodes of such crisis. However, the work of Caprio and Klingebiel(1996a, 1996b)
advised that it should be improved by more bank performance indicators which are
difficult to achieve (even in nowadays banking systems) and development
indicators which may contribute to the precision of crises occurrence predicting for
individual banks, on one hand, and for the whole system, on the other hand. In
addition, the political economy researches for the phenomenon of bank insolvencies
were suggested to be a useful tool for Governments.
Besides, in the trend of econometrically oriented analyses, the twin crisis was
introduced as the simultaneous occurrence of both currency crisis and banking crisis
based on the signal-to-noise approach to judge for the situation of crisis or not, i.e.
reach the alarm signal or not, in accordance to “the threshold values on an indicator-

19. 11
by-indicator basis” (Kaminsky and Reinhart , 1999). Consequently, the thresholds
must be selected in the sense that could minimize the signal-to-noise ratio. 16
indicators from financial sector, external sector, real sector and fiscal sector were
employed in this analysis of banking crisis individually and twin crises as a whole.
However, there existed some drawbacks of wrong signaling in this method.
Nevertheless, earlier signal are, to common sense, somewhat valuable information
for the authorities. Sample used in the research consists of 20 countries for the
period 1970-mid-1995. This paper aimed to fill this void in the literature and
examine currency and banking crises episodes for a number of industrial and
developing countries including Denmark, Finland, Norway, Spain, Sweden,
Argentina, Bolivia, Brazil, Chile, Colombia, Indonesia ,Israel, Malaysia, Mexico,
Peru, The Philippines, Thailand, Turkey, Uruguay, and Venezuela. This sample
gives also the opportunity to study 76 currency crises and 26 banking crises
following the database in the work of Caprio and Klingebiel (1996). Out of sample
testing was examined with the twin crises in Asia of 1997.
Dermirguc-Kunt and Detragiache (1998) used a large sample of both developed
and developing countries over their scope from 1980 to 1994 with a multivariate
logistic model to figure out the relevant factors of systemic banking crises
occurrence. This research pointed out that the crises seemed to burst under a weak
macroeconomic environment, i.e. high inflation and low growth. In addition, real
interest rate in its high status also contributed to problems in the banking sector, the
same evident finding for the role of vulnerable balance of payment was mentioned.
Some institutional issues such as deposit insurance existence and weak law
enforcement were found to put risks to the banking systems. The study emphasized
the significance of low growth of GDP in the sense that it could make the banking
sector at risk. On one hand, banks are the financial intermediaries, by nature, that
should involve in risk taking manner; hence the vulnerability of outside economic
environment should not be a worrying signal. But, on the other hand, banks would,
to some extent, ignore the credit risk of domestic economy fluctuation and lend

20. 12
overseas. This activity of banking sector in developed countries benefited some
developing countries but put much pressure on the authorities to improve the
institutional regulation on banking systems if they do not want to see the banking
sector fragility caused by the volatility from the expansion of cross-border banking
activities. There has been a debate for the role of financial liberalization in banking
system stability. The study also showed some weak evidence for the likelihood of
banking crises under the condition of controlled real interest rate in financial
liberalization periods. However, this study faced with some drawbacks related to
estimation model, the tradeoffs between the macroeconomic, institutional
explanatory indicators and the financial factors, i.e. financial markets indicators,
which might capture the banking system more entirely. Some suggestions for
further studies on banking structural indicators, such as “degree of capitalization of
banks, the degree of concentration and the structure of competition of the market for
credit, the liquidity of the interbank market and of the bond market, the ownership
structure of the banks (public versus private), and the quality of regulatory
supervision”, were also stated.
Broad new and old samples of banking crisis over different countries have been
combined in some researches. However, once again, these analyses on focused on
developed countries.
Bordo and Meissner (2012) submitted an analysis with a 14 advanced countries
over the scope of 1880-2008 to study the linkage between credit booms, inequality
and housing policy to banking crises. Credit booms, whose explanatory factors still
have not yet firmly indicated, are likely to contribute obvious evidence to banking
crises. By applying a logit model with and without countries fixed effect, the
research found positive evidence between credit booms and banking instability, lag
term of credit booms indicator was also taken into the model for testing. Although
the lag term of one year gave low probability of banking crisis, surprisingly, the
finding showed that there is a significant positive relationship that banking crisis
occurrence proceeded by a rise in real credit growth with its lag terms in prior to

21. 13
two to five years. Moreover, the research suggested that some factors such as
increasing of real income and fall in interest rate may be important to analyze credit
booms. However, according to the existing dataset, the research could not find
much valuable signal from the standpoint that a rise in income inequality and
housing redistributive policy contribute to the probability of financial crises.
Schularick and Taylor (2012) analyzed the role of “hitherto unknown” credit
expansion in nowadays economy around the world. The motivation of this is that a
stable relationship between money and credit found after the Great Depression and
World War II still keeps hold to crisis today. In addition, there is likelihood that,
after the 1930s, complex macroeconomic environment and financial policies such as
increasing of fiat money, role of banks as the lender of last resort have been
triggering the credit to expand. Moreover, the financial system with its particular
structural changes over a long period in the past has given credit an important role
in the macro-economy as a whole. To this extent, the stated unlucky progress has
been making credit become more essential ever. However, the raise of such credit
has been debated to play no construction role for the monetary policy. Nevertheless,
from the perspective of lessons learnt from histories from both researchers and
policymakers, the risk of this so-called credit accumulation was ignored. There
exists the likelihood that credit booms may contribute more risks to financial crises
in the future. One could give some criticisms that this is not a perfect factor to
predict the financial crises under some explanation that expansion of credit
contributes to the real economic growth; that some failures in terms of
operations/regulations within the financial systems have decrease the role of credit
expansion. Although there are many debates about the predicting power of credit
booms, the historic lessons of credit expansion and financial fragility still has its
value for more deep research in the future. The role of credit in macro-economy
should be examined.
Jorda et al. (2011) analyzed the financial fragility in the relationship with external
imbalanced situations of the economy such as deteriorations of the current account,

22. 14
growth of loans, volatility of interest rate, inflation, growth of GDP, etc…by
applying the logistic country fixed effect model over the wide scope of 140 years
across 14 developed countries. A combination of descriptive statistics of financial
fragility explanatory factors and that of econometrically oriented logit model were
also discussed. The mechanism of macro-economic indicators was found out that
growth of loans played an important role in accelerating crises from both national
and global perspectives. Deteriorations of current account seemed evidently
contribute to the run-up to crises for not only global but individual countries as
well. Natural interest rate being under strong suppression gave signal to the phase of
run-up to crises especially in the cases of four global crises over 140 years of
analysis. (i.e. 1890, 1907, 1930-1931 and 2007-2008). Real interest rate and
inflation also gave similar predicting signal to this trend. The conclusion of this
research indicated that the built-up phase of crises should be paid more attention by
policymakers by observing and/or analyzing the activities of external
macroeconomic imbalances. Moreover, the research emphasized that the credit
growth and current account intertwined significantly nowadays. Hence, these
factors might be good predictors for financial instability from both the viewpoint of
clear historic event and recent observations.
2.2.3. The third trend
Cross-country analyses have been emerged since the 2007 global financial turmoil
and the subprime Lehman crisis. Mainstreams of this trend are those analyses on the
impact from the perspectives of financial sector, real economy to the employing of
variety explanatory indicators on banking system fragilities. Each of the research
aspect will be reviewed hereinafter separately.
Financial sector perspective
Since the conjunction contribution of Gorton (1988) between the first and second
trend of banking crises analyses, there have been raising the motivation for
economists to study more on factors from both macro-economic and banking
sectors with the hope that their forecasting power for banking system fragilities will

23. 15
be improved. Kauko (2012) gave an analysis on the relationship of deficit current
account to banking vulnerabilities with main concentration in banking sector.
Although the research did not deal with crisis occurrence probability, it focused on
the so-called direct factor causing crisis, namely the deterioration of credit quality.
Non-performing loans of individual bank, on one hand, and of the whole system, on
the other hand, are attractive signal as they contribute to the losses of banks
profitability, i.e. one among the indicators judging health of banks. Over high credit
growth may occur in prior to banking crises. In addition, from the standpoint of
financial stability, a proxy for problematic credit growth is foreign debt. In more
details, the study analyzed how and to which extent the macro-economic
environment reflected in the quality of credit in recent crises. A dependent variable,
indicated by the relative amount of non-performing loans of 2009 from IMF, was
applied together with a cross-section of 34 advanced countries according to the
classification of IMF. The outcome showed that credit growth in the combination
with current account deficit act as an important predictor for financial crises.
In the scenario of the well-known Global Crisis accelerated by 2007 financial crisis
in the US, the study of Aizenman and Pasricha (2012) seeks to understand the
dynamics of the spillover effect of this crisis to the rest countries of the world and
the crisis relevant factors. The scope of analysis spread over 107 countries together
with their financial crises episodes from 2008-2009. In addition, a wide set of
indicators were taken into account by using the ordinary least square regression.
There are six common variables that existed through all the regression models,
namely per capita real GDP, international reserves-to-GDP ratio, an interaction term
between international reserves-to-GDP ratio and a dummy variable indicating
whether the country was a recipient of a swap line by the Federal Reserve, the
European Central Bank, or the People’s Bank of China, trade-to-GDP ratio, a
dummy variable for whether the country was a commodity exporter, and de jure
restrictions on capital flows measured by the Chinn-Ito index. Moreover, many
other indicators were also applied such as those factors of external exposure,

24. 16
institutions, financial development, banking sector health and competition, etc… the
concepts of internal and external financial stress were also distinguished
respectively as pressure of capital outflows and pressure inducing declines in stock
markets and expansions in central banks’ balance sheets. The research showed that
these two types of stresses shared some common factors. Countries with greater de
facto openness saw larger shocks, and countries with more competitive banking
systems were less vulnerable if their banking systems were also better capitalized or
better supervised. In addition, countries with higher international reserves saw
greater external stress, and commodity exporters saw lower internal stress.
Real sector perspective
Berkmen et al. (2012)shared the same concern of the contagion effect of financial
crisis in advanced countries to the rest of other countries around the world. The
research stated that, although the severity of crisis may differ among countries, the
macroeconomic background as well as institutional policies may play a role in
reflecting the vulnerabilities of each financial system during the crisis shock. The
differences between impacts of crisis in emerging and developing countries were
also discussed. Variation in growth rate of the economy stands out to be an
interesting factor to capture the real economy activities. Regarding to
methodologies, this research applied both descriptive statistic evidence and country
cross-sectional regression together with wide range of indicators to meet the aim
that the whole picture of which key indicators matter for shaping the varieties in
growth. The result showed that vulnerabilities of the financial systems seem to have
a significant role in the serious impact of growth. Simultaneously, the result
indicated that a more leveraged system as well as more short-term debts seemed to
generate bigger losses for the country. On the other hand, there is likelihood to
suffer shock for countries whose exchange regimes are pegged because the flexible
regime would be at good help to buffer the shocks. Effective fiscal policies were
also found to be at good help for less severity with shocks. However, the study
might be due to dataset problem, found no significance of other policy variables.

25. 17
Artha et al. (2011)in another study has put a new brick to the analysis of financial
crisis but from a new perspective of the linkage between labor market, an important
entity of the real economy, and financial crisis to the impact of output losses in the
economy. A set of 56 countries over the scope of 2007-2009 first quarter with the
output loss was examined through the declining of real GDP.Employing a cross-
country model that includes control variables such as trade and capital market
integration, financial development, monetary and fiscal policy, institutional
differences, and population growth, we find that lower hiring cost reduce the output
loss, notably so in high-income countries. However, the duration of the crisis is
longer in case of low dismissal cost, notably so in low-income countries.
Rose and Spiegel (2011)are interested in the big amount of “fundamental” causes
of the global crisis of 2008-2009 and in the likelihood that there may be any linkage
between the “Great Recession” and the actual crisis. Using the cross-sectional
approach based on their former dataset in 2010, the study examined the countries
and/or territories whose real GDP per capita higher than USD 10,000 as well as
those with at least USD 4,000 but their population has to be from one million. Some
standard OLS regressions were also taken into account for this dataset. As
mentioned, a wide set of indicators from many aspects were applied such as 7
factors of cross-country crisis severity, i.e. the GDP growth over times, growth of
consumption, 8 indicators that widely used by other researchers such as exchange
rate regime, current account, growth of trading partner, credit market regulation,
short-term external debts, changes of house price, growth of bank credit and
international reserves, etc…together with 6 different samples of country.
Surprisingly, those large set of indicators and countries sample provided no
significant linkage between the causes and the Great Recession. A conclusion from
this time of study is that causes of crisis may vary from country to country leading
to the fact that the cross-country models could not fit the data well even with in-
sample test and they were not estimated with acceptable accuracy. However, in
another following research, still a wide set of factors were employed using the

26. 18
Multiple Indicator Multiple Cause (MIMIC) model introduced by Goldberg (1972)
with a cross-sectional data from 107 countries to analyze causes of the 2008 global
crisis. The paper emphasized the importance of a wide range of indicators with the
rationale is obtaining as much as possible the explanatory ability of the data,
although those causes specification may be empirically unstructured (Rose and
Spiegel, 2012). Despite the big amount of fundamental indicators from financial
situation, macroeconomic background, institutions, geographic indicators and
regulatory framework, this time of analysis still ended up with pessimistic outcome
that almost none of the indicators seemed to have strong significance in explaining
the cause of crisis in 2008. The paper, then, advised that there still exists some
linkage but the observed data may speak fewer things with the existing econometric
techniques and that the future crises seem difficult to be forecasted precisely.
Frankel and Saravelos (2010) contributed a research motivated by the cross-
country incidence of the 2008-09 financial crisis using some approaches such as
probit model, signals method, combination of qualitative and quantitative method,
etc… along with a dataset consists of 50 annual macroeconomic and financial
variables for 2007 or earlier from the World Bank World Development Indicators
database. This source is augmented by monthly real effective and nominal exchange
rate data from the IMF International Financial Statistics database. Data availability
differs by country, with the most data points available for the level and growth rate
of GDP (122 countries) and the least data available for various measures of short-
term debt (67 countries). High frequency data for foreign exchange rates (156
countries), stock market indices (77 countries), industrial production (58 countries)
and GDP (63 countries) up to the second half of 2009 are sourced from Bloomberg
and Data stream for the financial and real data respectively. The high frequency
data are used to define crisis incidence from the second half of 2008 onwards, as
analyzed in more detail below. All the independent variables are dated from 2007 or
earlier, minimizing endogeneity issues. This paper conducted an extensive review
of the early warning indicators literature, and found a number of variables to be

27. 19
consistently useful in predicting financial crisis incidence across time, country and
crisis in earlier work. These indicators were subsequently included in an empirical
analysis of the 2008-09 crisis. International reserves and real exchange rate
overvaluation, the top two indicators identified in the review, stood out as useful
leading indicators of the current crisis. Reserves were robust to a number of crisis
incidence definitions as well as the inclusion of additional independent variables in
multivariate specifications using an exchange market pressure index as a measure of
crisis incidence. Past exchange rate overvaluation only proved useful for measures
of crisis incidence that defined a crisis in terms of the currency. A number of other
variables appear as potentially useful leading indicators during the current crisis,
though their robustness across different crisis incidence measures and specifications
was not as compelling. Lower past credit growth, larger current accounts/saving
rates, lower external and short-term debt were associated with lower crisis
incidence. There remains fertile ground for further research into the effectiveness of
early warning systems in predicting the 2008-09 crises and beyond. The findings
also highlight the potential economic significance of reserve levels and exchange
rate policy in affecting crisis vulnerability.
2.3. Money Market Pressure (MMP) Index (Hagen and Ho, 2007)
The equation (2) illustrates the criteria for the identification of banking crisis using
MMP index. Specifically, the index is calculated for each country separately and the
periods of banking crises identified when the index satisfies both of the two
conditions: (1) the index exceeds the 98.5 percentile of its sample distribution for
each country taken for computation; (2) the growth rate of the index is at least 5%.
Following the explanation of Hagen and Ho (2007), the first criterion ensures that
only distinctive episodes will be judged as crises; while the second criterion
considers those countries confront no crisis during the scope of sample analysis
since the first condition keeps hold for every sample distribution. Empirical results
indicate that, the first criterion once relaxed will lead to the probability that too
many crises are identified, while the tightened counterpart will lead to missing true

28. 20
crisis. In addition, the mentioned percentile has been changed to other values which
resulted in the decline of explanatory power of the regression model with lower
percentile value of 95, while no significant change in the case of higher percentile
value of 99.5. With same explanation, the tightened condition of the second
criterion makes some true crises episodes missing. Overall, the crisis defining here
is country specific, one may criticized the definition should be applied for the whole
countries under consideration by pooling all the data and applying the only
calculation. However, as a matter of fact, the magnitudes of fluctuation of MMP
index may vary among countries, the pooled data for computing may lead to
missing of true crises for countries whose fluctuations of the index are relatively
low. After all, in terms of the first criterion, the percentile is preferred to the
multiple standard deviations due to the “non-normal” nature of distributions of the
MMP index.
Nevertheless, there seemingly exist some drawbacks of this definition of banking
crises: (1) banking crises are believed to occur in modern world due to asset-driven
rather than liability-driven mechanism, however, the increase in demand for
reserves caused by the deteriorations of bank assets is out of the scope of study of
this thesis; (2) the index is not suitable in the case of interest rate controlled by
some central banks in some countries, yet one advantage is that the MMP index is
independent with the interest rate flexibility provided that managements on interest
rate of central bank rely on market measures; (3) the MMP index can only indicate
the starting of the crises but not when the crises end as “identifying the end of a
banking crisis is one of the more difficult unsolved problems in the empirical crisis
literature" (Kaminsky and Reinhart, 2000), but the identification of when the crisis
starts or ends is beyond the thesis.
The sample taken into analysis spanned over the period of 1980-2001 with 47
countries. The reasoning of choice for countries sample is based on the availability
of data from IMF, but the study excluded Argentina and Brazil out of the
consideration due to their abnormal inflation and interest rate. According to which

29. 21
indicators were used, the total observations varied from 697-726 in which there are
34-38 crisis episodes. Thus, percentage of crises over the sample population is
about 5%. The explanatory indicators were chosen from both former literature and
the existence of data. The method of conditional countries fixed effect model were
applied with banking crisis judged by MMP index as regressand, and regressors
include large sets of indicators such as: (1) Macroeconomic variable: Growth rate of
real GDP, depreciation, over-valuation of real exchange, real interest rates, inflation
rates, surplus/GDP, GDP, Dummy for severe recession (Growth <-5%), Dummy for
high inflation (inflation >20%), Growth rate of monetary base, Growth rate of real
domestic credit; (2) Financial variable: Private credit/GDP, Cash/Bank, Changes in
stock market price Share prices; (3) Institutional variables such as: GDP/CAP (1000
dollars/person), Dummy variable for existence of explicit deposit insurance,
Dummy variable for financial liberalization. The outcome shows that slowdown of
real GDP, lower real interest rates, extremely high inflation, large fiscal deficits,
and over-valued exchange rates tend to precede banking crises. The effects of
monetary base growth on the probability of banking crises are found negligible.
2.4. Chapter summary
After all related literatures are introduced this section gives brief summary in Table
2.1 according to the order of structure written above.
The banking crisis definition used in the rest of this thesis is what derived from the
fluctuation of MMP index that observed the vulnerabilities of banking sector
through the calculation of the weighted average of changes in the ratio of reserves
to bank deposits and changes in the short-term real interest rate (Hagen and Ho,
2007).
Figure 2.1 demonstrates the mechanism leads to banking crisis from the
perspectives of macro-economy, finance and institutions. Generally speaking,
banking system under weak macroeconomic environment, vulnerable financial
background of banks and inefficient institutions is likely to be exposure high risk of
banking crisis occurrence.

30. 22
Table 2.1 Summary of literature reviewed
Author(s) Key indicators Methodology Data sample Findings
Friedman
and
Schwartz
(1963)
Bank run,
short-term
interest rate,
bank deposit
Descriptive
analysis
United States
(1867–1960)
- Banks’ healthiness is likely considered
as its withdrawal readiness in the expense
of selling long-term securities prematurely
leading to a rise in yield of short-term
assets. This action results in defaulting
some of banks’ deposits making
depositors rush to shift their deposits into
cash to somewhat self-protect themselves
against risks of bank-run.
- The later wave of banking crisis
occurred more severe because the banking
system had been unhealthy during the
former crisis.
.Herrala
(2011)
Bank
profitability
Descriptive
analysis, case
studies
Finland
(1865-1998)
- Macroeconomic indicators such as real
GDP growth, change of volume in export,
investment, inflation, money stock,
exchange rate, interest rate, change in total
assets and deposits as portion of loans
were analyzed using descriptive statistics
approach to figure out the characteristic of
banking crisis.
- Banks’ profitability over total assets has
been taken into account to analyze for the
deterioration of banks’ financial condition
which may lead to banking crisis cycles.
Gorton
(1988)
Banking panics
and the
depositors’
behaviors
Econometric
evidence
United States
(1863-1914)
- The research emphasized that the
banking panics might be caused by the
changing in perceptions for risks of
depositors. Some indicators were taken
into account such as deposits ratio,
liabilities.
Caprio and
Klingebiel
(1996a,
1996b)
insolvency of
banks, GDP,
inflation,
monetary
growth, fiscal
balances, trade
balances, real
deposit rate,
financial
deepening, real
credit/GDP
In-depth
interviews
69 countries
over the
period of late
1970-1996
- Been considered to be the first banking
crisis database with crisis dates, countries
and some economic explanatory variables
together with observations on policy
measures.
- Weak macroeconomic background and
health of banks tend to lead to banking
crisis.
Kaminsky
and
Reinhart
(1999)
16 indicators
from financial
sector, external
sector, real
sector and fiscal
sector
signal-to-
noise
approach
20 countries
for the period
1970-mid-
1995
- This study gives the opportunity to study
76 currency crises and 26 banking crises
following the database in the work of
Caprio and Klingebiel (1996). Out of
sample testing was examined with the
twin crises in Asia of 1997.
- Volatilities of such indicators from
financial aspect, external and real sector
seem to contribute to the probability of
banking crisis occurrence.
Dermirguc- Growth rate of multivariate a large - The research found that weak

31. 23
Kunt and
Detragiache
(1998)
real GDP,
change in term
of trade,
depreciation,
real interest
rate, inflation,
Government
surplus over
GDP, M2 over
reserves,
private credit
over GDP, bank
cash over
assets, growth
rate of domestic
credit, dummy
for deposit
insurance, GDP
per capita, law
and order
(ICRG)
logistic
model
sample of
45-65
countries in
IFS database
from both
developed
and
developing
countries
over their
scope from
1980 to 1994
macroeconomic background of the
economy such as low growth rate of GDP
tends to trigger banking crises due to the
risk taking nature of banks.
- In addition, banking crises may rise
from problems of maturity transformation
of banking sectors as the consequences of
high fluctuation of nominal interest rates
in accompany with high inflation rate.
Banking sector stability should be benefit
from inflation controlling policies such as
restrictive monetary policies. However,
such activities carried out in the context of
implementing of high inflation controlling
may lead to the likelihood of banking
crises occurrence through high real
interest rate channel. Weak banking
systems should be cautious about inflation
controlling policies and monetary policies.
- Moreover, institutional decision of
deposit insurance scheme seems to
increase the likelihood of banking crisis.
Bordo and
Meissner
(2012)
credit booms,
inequality and
housing policy
to banking
crises
logit model
with and
without
countries
fixed effect
14 advanced
countries
over the
scope of
1880-2008
- Banking system instability, which may
lead to banking crisis, was evidently found
to have a strong positive relationship with
lagged term of two to five years of credit
booms.
- Inequality and housing policy are those
factors of the economy were taken into
account to test for impacts on the
probability of financial crises.
Unfortunately, the results from existing
dataset showed no evidence.
Schularick
and Taylor
(2012)
credit
expansion,
financial crisis
OLS linear
probability,
logit
14 countries
over the
years 1870–
2008
- Credit expansion contributes to real
economic growth. However, there is
likelihood that credit booms may
contribute more risks to financial crises in
the future due to failures in operations
and/or regulations within the financial
system.
- The role of such credit booms from the
perspective of macroeconomics should be
further studied as there have been the
historic lessons of credit expansion and
financial fragility.
Jorda et al.
(2011)
the current
account, growth
of loans,
volatility of
interest rate,
inflation,
growth of GDP
logistic
country fixed
effect model,
descriptive
statistics
140 years
across 14
developed
countries
- The mechanism of macro-economic
indicators to cause banking crisis was
found out that growth of loans played an
important role in accelerating crises from
both national and global perspectives.
- Deteriorations of current account seemed
evidently contribute to the run-up to crises
for not only global but individual
countries as well. Natural interest rate
being under strong suppression gave
signal to the phase of run-up to crises.

32. 24
Real interest rate and inflation also gave
similar predicting signal to this trend.
Kauko
(2012)
deficit current
account to
banking
vulnerabilities
with main
concentration in
banking sector,
deterioration of
credit quality,
Non-
performing
loans
no banking
crisis
probability
calculation
model, OLS
regression
cross-section
of 34
advanced
countries
according to
the
classification
of IMF 2009
The outcome showed that credit growth in
the combination with current account
deficit act as an important predictor for
financial crises.
Aizenman
and
Pasricha
(2012)
per capita real
GDP,
international
reserves-to-
GDP ratio, an
interaction term
between
international
reserves-to-
GDP ratio and a
recipient of a
swap line
dummy
variable, factors
of external
exposure,
institutions,
financial
development,
banking sector
health and
competition
ordinary least
square
regression
107 countries
together with
their
financial
crises
episodes
from 2008-
2009
- The dynamics of the spillover effect of
this crisis to the rest countries of the world
and the crisis relevant factors. The
research showed that these two types of
stresses shared some common factors.
- Countries with greater de facto openness
saw larger shocks, and countries with
more competitive banking systems were
less vulnerable if their banking systems
were also better capitalized or better
supervised. In addition, countries with
higher international reserves saw greater
external stress, and commodity exporters
saw lower internal stress.
Berkmen et
al. (2012)
wide range of
variables of
Trade linkages,
financial
linkages,
vulnerabilities/
financial
structures,
policy
framework
descriptive
statistic
evidence and
country
cross-
sectional
regression
43 countries
from
Consensus
Forecast, 141
countries
from WEO
database over
the year of
2007, 2008,
2009
The contagion effect of financial crisis in
advanced countries to the rest of other
countries around the world. The research
stated that, although the severity of crisis
may differ among countries, the
macroeconomic background as well as
institutional policies may play a role in
reflecting the vulnerabilities of each
financial system during the crisis shock.
Artha et al.
(2011)
financial crisis
in the linkage
with labor
market
cross-country
model
56 countries
over the
scope of
2007-2009
first quarter
Control variables such as trade and capital
market integration, financial development,
monetary and fiscal policy, institutional
differences, and population growth, we
find that lower hiring cost reduce the
output loss, notably so in high-income
countries. However, the duration of the
crisis is longer in case of low dismissal
cost, notably so in low-income countries.

33. 25
Rose and
Spiegel
(2011)
7 factors of
cross-country
crisis severity,
i.e. the GDP
growth over
times, growth
of consumption,
8 indicators that
widely used by
other
researchers
such as
exchange rate
regime, current
account, growth
of trading
partner, credit
market
regulation,
short-term
external debts,
changes of
house price,
growth of bank
credit and
international
reserves
- - cross-
sectional
approach
- OLS
regressions
6 different
samples of
country
2008-2009
Those large set of indicators and countries
sample provided no significant linkage
between the causes and the Great
Recession. Causes of crisis may vary from
country to country leading to the fact that
the cross-country models could not fit the
data well even with in-sample test and
they were not estimated with acceptable
accuracy.
Rose and
Spiegel
(2012)
indicators from
financial
situation,
macroeconomic
background,
institutions,
geographic
indicators and
regulatory
framework
Multiple
Indicator
Multiple
Cause
(MIMIC)
cross-
sectional data
from 107
countries to
analyze
causes of the
2008 global
crisis
This time of analysis still ended up with
pessimistic outcome that almost none of
the indicators seemed to have strong
significance in explaining the cause of
crisis in 2008
Frankel and
Saravelos
(2010)
50 annual
macroeconomic
and financial
variables for
2007 or earlier
from the World
Bank World
Development
Indicators
database.
- probit
model
- signals
method
- combination
of qualitative
and
quantitative
method
cross-country
incidence of
the 2008-09
financial
crisis
International reserves and real exchange
rate overvaluation, the top two indicators
identified in the review, stood out as
useful leading indicators of the current
crisis. Reserves were robust to a number
of crisis incidence definitions as well as
the inclusion of additional independent
variables in multivariate specifications
using an exchange market pressure index
as a measure of crisis incidence. Past
exchange rate overvaluation only proved
useful for measures of crisis incidence that
defined a crisis in terms of the currency.
Lower past credit growth, larger current
accounts/saving rates, lower external and
short-term debt were associated with
lower crisis incidence.
Hagen and
Ho (2007)
-
Macroeconomic
-
Identification
47 countries
over the
The outcome shows that slowdown of real
GDP, lower real interest rates, extremely

34. 26
variable:
Growth rate of
real GDP,
depreciation,
over-valuation
of real
exchange, real
interest rates,
inflation rates,
surplus/GDP,
GDP, Dummy
for severe
recession
(Growth <-5%),
Dummy for
high inflation
(inflation
>20%), Growth
rate of
monetary base,
Growth rate of
real domestic
credit
- Financial
variable:
Private
credit/GDP,
Cash/Bank,
Changes in
stock market
price Share
prices;
- Institutional
variables such
as: GDP/CAP
(1000
dollars/person),
Dummy
variable for
existence of
explicit deposit
insurance,
Dummy
variable for
financial
liberalization.
of banking
crisis using
MMP index.
Specifically,
the index is
calculated for
each country
separately
and the
periods of
banking
crises
identified
when the
index
satisfies both
of the two
conditions:
(1) the index
exceeds the
98.5
percentile of
its sample
distribution
for each
country taken
for
computation;
(2) the
growth rate
of the index
is at least 5%.
- Conditional
countries
fixed effect
model
period of
1980-2001
high inflation, large fiscal deficits, and
over-valued exchange rates tend to
precede banking crises. The effects of
monetary base growth on the probability
of banking crises are found negligible.
As summary in Table 2.1, many researches have been taken on the probability of
banking crisis explicitly and implicitly using various indicators from many aspects
of a real economy from the macroeconomic environment background to the role of
Government to the whole financial system in which banking sector is an element.
Researches have been indicating that weak environment of macroeconomic generate

35. 27
good channel for increasing the risk of banking crisis. In addition, the role of each
Government is significantly important to contribute to high or low exposure of
banking system to crisis. Mismanagement from in charged authorities may lead to
banking crisis directly or indirectly through the mechanism that makes
macroeconomic background go bad then banking crises occur. Moreover, as bank is
one element of the whole financial system, thus, the more unhealthy the financial
system the higher risk of banking crises. Figure 2.1 briefly shows a visual
mechanism of factors that may turn banking sector to the risk of crisis.
Figure 2.1 Mechanisms of banking crisis
Bad macroeconomic
background
Unhealthy financial
system
Unhealthy Government
Banking crisis
probability
Bad institutional
conditions
Improper
activities of
financialsystem
Weak
macroeconomic
environment

36. 28
CHAPTER 3: METHODOLOGY, MODEL SPECIFICATION
AND DATA
In the previous chapter, trends of banking crisis analyses have been introduced and
discussed with the aims to provide readers with an overlook about banking crisis
relevant techniques and/or types of explanatory variables under consideration of
each trend, each research model. This chapter focuses on the following issues: (1)
model selection, (2) specification of chosen model in terms of discussions of
effective relationship of independent variables on dependent variable, i.e. banking
crisis and (3) the data sources and scope.
3.1. Model selection
The judgment outcome of banking crisis is in binary format, i.e. it takes the value of
“1” if there is a crisis and “0” if there is no crisis. Hence, there are two methods
may often be considered from empirical studies reviewed so far namely probit and
logit regression model. Moreover, it seems that most empirical studies reviewed
above tend to employ logit regression model for their analysis of banking crisis due
to its binary nature, ease of understanding as well as interpretation (Dermirguc-Kunt
and Detragiache, 1998, Hagen and Ho, 2007, Jorda et al., 2011, Bordo and
Meissner, 2012). Being motivated by this trend, the same technique of logistic
regression will be followed in this thesis. The mathematical background of logit
regression is described in the below wordings.
First, those may take a look at the logit model in its mathematic equation below:
= E(Y= 1| ) =
Let = , the formula above can be rewrote as:
(1)
It is easy to recognize that while takes the values from -∞ to +∞, will
receive values ranging between 0 to 1.

37. 29
Supposed that is the probability of banking crisis occurrence, then the state of
non-crisis will be described by the term (1- ) = 1- = (2)
Take (1) divide by (2):
= (3)
After all, take natural log for both sides of equation (3) the logit model will be
obtained as:
= ln ( )= = (4)
Where denotes the coefficients of each explanatory variables separately.
Noted that = ln ( ). Therefore, taking the antilog of the estimated logit, we get
, that is, the odds ratio. In general, if taking the antilog of the j-th slope
coefficient (in case there is more than one regressor in the model), subtract 1 from
it, and multiply the result by 100, the result will be the percent change in the odds
for a unit increase in the jth
regressor.
The formula (4) under the conditions that time and entities are considered together
can be rewritten as follow:
= (5)
Gujarati (2004) indicated that the estimation (5) depends on the assumptions made
for its intercept, slopes of coefficients and the error term. Thus, there are five
assumptions: (1) the intercept and slopes keep hold regardless time and entities (i.e.
countries) while letting the error term tell the difference; (2) slopes unchanged and
intercept varies among countries; (3) slopes unchanged and intercept varies
throughout countries and time; (4) both intercept and slopes varies over countries
and (5) both intercept and slopes varies over countries and time. As a result, the
assumptions illustrate the increasing level of complexity and may coincide with
more reality. For the purpose of analysis, only first two assumptions are discussed
hereinafter.

38. 30
The case of assumption (1), normally known as pooled regression model, provides
an easy regression method together with high restrictions, therefore, the whole
picture of the relationship between the regressand and regressors may be distorted.
As common sense, people are more interested in the specific nature of each country.
This turns to the second case described by the estimation rewritten from (5) as
below:
= (6)
The estimation (6) with the intercept was rewritten as indicates the changing
over countries for this item. This is the so-called fixed effect model (FEM), in
which its intercept keep changing across countries but still time invariant. The
differences of intercepts indicate some specific features that one country has while
the others may not have. Hence, the choice of FEM seems to be preferable to pooled
regression for the thesis.
In addition, a formal statistical test, i.e. the restricted F-test, is suggested for the
choice between restricted pooled model and FEM.
Though the FEM is easy to be applied straightforwardly, this model is established in
the expense of the degrees of freedom if there are many cross-sectional units
(Gujarati, 2004). One possible question rose for an alternative approach in
considering the unknown information of the error term instead what have been done
with the intercept in FEM. Consequently, random effect model (REM) was built up
as the expression below:
= (6)
Where = + (7) denotes that the intercept now will be followed by a random
error term with zero mean and variance. The aim of REM is that it covers the
idea that individual countries have the same mean value of intercept but the
differences come from the error term.
By substituting (7) into estimation (6), the REM estimation obtained below:
=

39. 31
Another formal test introduced by Hausman (1978) for the choice between FEM
and REM. However, since FEM always gives unbiased estimation, FEM seems to
be most favored in this time of analysis.
3.2. Model specification
As stated about the specification of regression model, Hagen and Ho (2007) and
Gujarati (2003) emphasized that the choice of any variable to be plugged into the
model has to be a combination of both theories and empirical researches. However,
as a matter of fact, the risk of inappropriate structure leading to specification error
may still exist due to some reasons: (1) relevant variable(s) may not be considered
and/or irrelevant variable(s) may be taken into account, (2) inaccurate functional
form. Regarding to the second reason, the thesis did argue and follow the
assumption that logistic regression model is an appropriate model choice for this
time of banking crisis analysis. In terms of the first reason, the thesis tends to
include as many as possible the relevant variables in line with those already applied
by other previous researches and their data availability. Since the aims of the
analysis are to recognize the more determinants the possible to the contribution to
probability of banking crisis occurrence, all variables will be included to test for
their effectsthrough the use of logit model discussed in studies of Dermirguc-Kunt
and Detragiache (1998), Hagen and Ho (2007), Jorda et al. (2011) and Bordo and
Meissner (2012).
Thus, general framework for predicting the occurrence of banking crisis would
comprise the all possible variables that we have. Based on banking crisis theory and
data availability, the model is suggested as following:
= Ln ( ) = + + + +
+ + +
+ + + +
+ + + +

40. 32
Where:
denote the constant item of the regression model and the error term
respectively
to denotes the coefficient of each independent variable in the logit model
separately
denotes banking crisis at prior time of 12 months for country i
denotes inflation of country i at time t
denotes growth rate of monetary base to GDP of country i at time t
denotes the depreciation of domestic currency of country i at time t
denotes the short-term real interest rate at 36 months in advance
denotes the growth rate of domestic credit to GDP in 12 months
earlier
denotes the growth rate of domestic deposits to GDP in prior 6
months
denotes the growth rate of M2 over reserves of country i at time t
denotes voice and accountability
denotes political stability and absence of violence
denotes government effectiveness
denotes regulatory quality
denotes rule of law
denotes control of corruption
The relevant variables used in this thesis are classified into three variable groups
including macroeconomic indicators such as inflation, growth of monetary base,
depreciation and past banking crisis. The consideration of past banking crisis as a
factor of macroeconomic background is due to suggestion that weak
macroeconomic environment may accelerate banking crisis (Berkmen et al., 2012).
The other variable group includes financial indicators capturing such variables as
growth of M2 over reserves, short-term real interest rate, growth of credit over
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41. 33
GDP, and growth of bank deposits over GDP as these factors reflect the health of
financial systems in common sense. The last group of variable is that of institutional
indicators which give assessment for health of Government including Voice and
Accountability, Political Stability and Absence of Violence, Government
Effectiveness, Regulatory Quality, Rule of Law and Control of corruption. The
following wordings are discussions on the effects of each explanatory variable and
its expected sign.
3.2.1. Macroeconomic indicators
 Inflation
Inflation is among those indicators that reflect the somewhat mismanagement of
Government to the economy as a whole (Demirguc-Kunt and Detragrache, 1998a).
Inflation periods may occur simultaneously with high interest rate, the deterioration
of currency as well as the balance sheet of banks. In common sense, the bad effect
of inflation should be taken into account as one of the determinants for studying the
phenomenon of banking crisis. However, some empirical studies show that the
relationship between inflation and banking crisis is not that strong in developed
countries but it is more significant in the emerging countries (Kauko, 2014).
Nevertheless, inflation is likely the most popular factor to be analyzed due to its
ability of capturing the whole macro-economy environment; hence, this item will be
applied in this thesis although its expected sign with the banking crisis left
ambiguous.
 Past banking crisis (lagged 12 months)
As discussed above in chapter 2 about the literature of banking crisis, it is difficult
to indicate the exact periods that the banking system is out of crisis due to late
identification and/or asymmetric information of useful signals of the recovery times.
Thus, the including of the past banking crisis may be at good help, to some extent,
to predict the occurrence of banking crisis at the present time. This idea is motivated
by the work of Falcetti and Tudela (2006) when they considered the similar lagged
term into the model for determinants of banking crisis. The hypothesis here is that
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42. 34
last period of banking crisis may lead to the crisis in present; hence, the expected
sign of this relationship is positive.
 Growth of monetary base
The monetary base is considered to reflect the monetary expansion (Hagen and Ho,
2007). Since this is the source for liquidity of banking system as a whole, the
indicator of growth of monetary base seems to play a somewhat important role in
assessing health of banking system that is likely to reduce the exposure to crisis.
Consequently, a negative relationship is expected for this indicator.
 Depreciation of exchange rate
There is a likelihood that banks often borrow from overseas to lend domestically.
Hence, their profitability may be put to risks once the depreciation of domestic
currency occurs (Demirgü
ç
-Kunt and Detragiache, 1998). Therefore, there are many
countries have been enacting regulations to limit the activity of foreign borrowings
of banks; however, those regulations have been bypassed purposely. Some banks
even provide domestic loans under foreign currency leading to the shift of foreign
exchange risk from them to their borrowers. In this case, any depreciation of the
exchange rate will deteriorate banks profit through the channel of bad loans. As a
matter of fact, the narrative histories cited by Demirgü
ç
-Kunt and Detragiache
(1998) show that loans in foreign currency were causes of banking system problems
in Chile (1981), Mexico (1995), Nordic countries (early 1990s) and Turkey (1994).
Thus, the positive sign is expected for this indicator in the relationship with banking
crisis.
3.2.2. Financial indicators
 Growth of M2 over reserves
The monetary aggregate, to some extent, can reflect the reserves of central bank
whose shortage may likely lead to crises. This relationship is found in the study of
Jorda et al. (2011) that the crisis was preceded by four year high value of ratio of
the money to nominal GDP. However, Drehmann et al. (2011) found a contradict
result that this ratio is not good at predicting crisis. To compromise the empirical
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