Macroeconomic, Financial And Institutional Determinants Of Banking Crisis - The Money Market Pressure Index Approach.pdf

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
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


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 VietnamNetherlands 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.

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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

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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

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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

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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

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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

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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 shortterm 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 socalled seemingly first banking crisis database of Caprio and Klingebiel (1996a,

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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

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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-

10


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

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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

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