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

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

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

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 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

Vietnam-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

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

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

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

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

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

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

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

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

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

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

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

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 term real interest rate, and are different terms of and , and are the standard deviations of the two components respectively

short-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,

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

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

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-

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

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

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,

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

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,

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

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 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

in-Multiple Indicator in-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 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

short-of the early warning indicators literature, and found a number short-of variables to be

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

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

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

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

Table 2.1 Summary of literature reviewed

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

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

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

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

signal-to-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

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

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

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

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

Real interest rate and inflation also gave similar predicting signal to this trend Kauko

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

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

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

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

cross 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

sectional data from 107 countries to analyze causes of the

cross-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

- 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

47 countries over the

The outcome shows that slowdown of real GDP, lower real interest rates, extremely

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

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

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

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 jthregressor

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

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:

=

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 (

) = + + + + + + + + + + +

+ + + +

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