Determinants of banking crisis in developing countries

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 DETERMINANTS OF BANKING CRISIS IN DEVELOPING COUNTRIES :\ A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Development Economics By LuO'ng Duy Quang Academic supervisor: Dr Nguy~n Van Phuc • ~.- > ~ ~-'./ :~:.·~ - Ho Chi Minh city, March 2010 - - 1- I ACKNOWLEDGEMENT To be able to complete this thesis, I have been received a great support from many people First of all, I would like to thank my supervisor, Dr Nguyen Van Phuc for his valuable guidance, comments, advice and encouragement during my completion of this thesis I would like to express my deep appreciation and sincere thanks to Assoc Prof Le Bao Lam for his valuable orientations in the first days of searching further education I am grateful to my all teachers and staffs of the Vietnam-Netherlands Program, particularly, Assoc Prof Nguyen Trong Hoai for his assistance during the first days I study in this program Many thanks are respectfully sent to my manager, Mr Nguyen Tu Han, and my colleagues for their encouragement and support during my writing thesis duration Finally, I am indebted to my family, especially my parent and others who give me great encouragement and support for my study -2- TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem statement: 1.2 Research objective: 1.3 Research question~ 1.4 Research hypothesis: 1.5 Structure of the thesis: CHAPTER II: LITERATURE REVIEW 2.1 Key Definition: 2.2 Banking crisis theory: 2.3 Empirical Studies; 14 2.4 Chapter remarks: 17 CHAPTER III: MODEL SPECIFICATION AND DATA 3.1 Model choice: l8 3.2 Theoretical model and model specification: 20 3.2.1 Growth~ 21 3.2.2 Real short-term interest rate: 21 3.2.3 Exchange rate~ 22 3.2.4 Inflation: 23 3.2.5 Terms ofTrade; 23 3.2.6 Speculative attack: 22 3.2.7 Deposit Insurance: 22 3.2.8 Liberalization~ 22 3.3 Estimation strategy and statistical tests of the model: 24 3.4 Data sources: .26 3.5 Data filler process; 27 CHAPTER IV: REGRESSION RESULTS AND TESTING HYPOTHESIS 4.1 Accessing the best unbiased model: 28 4.2 Significance of explanatory variables: 33 4.3 Minimization of banking crisis frequency: 36 CHAPTER V: WELL-KNOWN CRISES AND PREDICTION POWER OF THE MODEL -3- 5.1 Thai Banking Crisis~···················································································································································39 5.2 Uruguay's crisis: Victim of contagious effect 40 5.3 Implementing the best unbiased model to Thailand and Uruguay cases: failure to explain the crisis originating in balance of payment crisis 43 CHAPTER VI: CONCLUSION AND POLICY RECOMMENDATION LIST OF TABLES Table 1: Regression results ofbanking crisis determinants: the panel eliminating observations following end year of crisis 31 Table 2: Regression results of banking crisis determinants: the panel eliminating observations following first year of crisis 33 Table 3a: Average marginal effects of determinants case for those countries without deposit insurance system -37 Table 3b: Average marginal effects of determinants case for those countries with deposit insurance system 37 Table 4: Banking indicators before the crisis erupt (December 31,2001 } 41 LIST OF FIGURE Figure 1: Breakdown structure of liability side of Uruguay banking system before crisis erupt 41 Figure 2: Peso/US$ exchange rate and evolution of US$ reserves -43 Figure 3: Predicted probability of banking crisis in Uruguay and Thailand with specification 5a 44 -4- CHAPTER I: INTRODUCTION 1.1 Problem statement: The banking crises that erupted in US in 2007 are the latest in a series of such episodes that have been experienced by economies in various regions of the world in recent years In the 1990s, banking crises have occurred in Europe (the 1992-93 crises in the European Monetary System's exchange rate mechanism), Latin America (the middle of 1990s), as well as in East Asia (the 1997-98 crises in Indonesia, Korea, Malaysia, the Philippines, and Thailand) These crises have been costly in varying degrees both in lost output and in the fiscal expense to rescue fmancial sectors Through fmancial system, their significant spillovers spreads internationally and in a number of situations international financial assistance have required to mitigate their severity, costs and spillovers to other countries In the wake of these recent crises, issue of what determinants affecting to occurrence of banking crisis has been a hot topic for economists in both developed and developing countries around the world Accessing this question is quite significant because it does not merely help authorities have confident base for their policies making process but it also is necessary to build up early warning system so that the crisis can be prevented beforehand However, understanding determinants of banking crisis is not an easy task Financial innovations and the increased integration of global financial markets are driving forces that make us harder to deal with this issue Such factors push financial system to evolve rapidly and generate new risks for financial system One critical thing is that development of fmancial market seems so complicated and exceeds our knowledge and prediction What we can as crisis comes is waiting for crisis wave and witnessing its effects on our life Thus, widely spreading influence of financial crisis, especially in banking sector, and its possible consequences obviously show important role of researches about determinants of banking crisis Even though this issue is not new, it always deserves to pursue - 5- This paper, thus, will focus on examining theoretical paths that lead to occurrence of banking crisis From that place, early warning system is developed to deal with crisis However, because of data limitation, the scope of study narrows in developing countries during period 1974-2002 1.2 Research objectives: Some objectives of the thesis are to identify: (i) Determinants of banking crisis (ii) Threshold values of determinants which minimize banking crisis occurrence probability (iii) Suggest policies recommendation to prevent banking crisis 1.3 Research question: (i) What are determinants of banking crisis? (ii) How determinants affect on probability of banking crisis occurrence? (iii) How is marginal effect of each variable on probability of banking crisis evaluated? 1.4 Research hypothesis: (i) Key determinants such as real interest rate, deposit insurance, and exchange rate would have positive effects on probability of banking crisis (ii) Margial effect of each variable on probability of banking crisis is helpful to reduce risk of crisis 1.5 Structure of the thesis: The thesis includes chapters: Chapter 1: The introduction of the thesis Chapter 2: Literature review Fundamental components of banking cns1s such as key defmition, conceptual framework, theories and empirical studies will be discussed in this chapter Firstly, defmition of banking crisis is clarified Then, theoretical part will review the relationship between dependent variable {banking crisis) and explanatory -6- variables (fmancial variables, institutional variables, macro-economic variables) Empirical work is the last one mentioned in this chapter Chapter 3: Model specification and data The top priority of this chapter is to develop necessary steps to obtain the best unbiased model for data analysis steps later on To fulfill this purpose, advantages and disadvantages of potential models is initially analyzed Next, strategy to obtain model specification from originally conceptual one will be presented Other important issues relating to data such as data filler process, data sources are also mentioned in this chapter Chapter 4: Regression result and testing hypothesis In chapter 4, second objective of this paper will be fulfilled The chapter begins with analytic steps to access the best unbiased model Then, hypotheses relating to sources of banking crisis are tested to provide basis of policy recommendation The last part of the chapter will investigate marginal effects of the most significant variables on probability of banking crisis From that place, policymaker could proceed to maximize effectiveness of macro-policies on frequency of the crises Chapter 5: Well-known crises and prediction accuracy of model In the first part of chapter 5, two shrinking cases of banking crisis (Thailand and Uruguay) in Latin America and Asia will be reviewed For convenient purpose, events are arranged in chronology with two obvious parts: prior to the crisis and afterwards the crisis Then, prediction power of model will be discussed after analyzing results and information collected from prediction model and descriptive evidences of crisis Chapter 6: Conclusion and policy recommendation - 7- CHAPTER 2: LITERATURE REVIEW This chapter initially focuses on clarifying the defmition of banking crisis Then, various theories of banking crisis are introduced in order to get hypotheses on how determinants affect on probability of banking crisis occurrence Finally, the chapter closes with empirical studies of banking crisis that help the readers understand how other researchers have approached their research questions, what dataset they have collected as well as which models and statistical methods they have used 2.1 Key Definition: Banking crisis: Effects of banking crisis is always huge and costly to resolve Despite economies may experience different kinds of crisis, one thing ruled out is that if the collective effects of financial collapse is large enough, the government is forced to intervene Therefore, Ergungor and Thomson (2005), as cited by Caprio and Klingebiel (1996b), suggest that when central bankers think that a particular shock to the financial system could develop into systemic problem, and the monetary authorities begin to respond, banking system is considered as crisis In other words, banking crisis can be defmed in terms of behaviors of central banks Kaminsky and Reinhart ( 1996) share this perspective in his study by clarifying two policies of central bank in crisis period Under this view, banking crisis links closely with two types of events (1) bank runs that lead to the closure, merging, or takeover by the public sector of one or more financial institutions (as in Venezuela in 1993); and (2) if there are no runs, the closure, merging, takeover, or large-scale government assistance of an important financial institution (or group of institutions) that marks the start of a string of similar outcomes for other financial institutions As discussed by Kaminsky and Reinhart (1996), this approach is not without drawbacks It could date the crises too late, because the financial problems usually begin well before a bank is finally closed or merged; it could also date the crises too early because the worst of crisis may come later Moreover, data of banking crisis in - 8- terms of their approach is available at limited level Kaminsky and Reinhart (1996) just list system banking crisis of more than 20 countries This makes it harder for • researchers to expand the scope of study Given data limitation, this paper, thus, follows definition of banking crisis developed by Caprio and Klingebiel (1996b) Accordance with his perspectives, banking crisis is the case in which the net worth of the banking system has been almost or entirely eliminated This creates something contradiction as banking problem in many nations is still to appear when a banking system has positive net worth However, Caprio and Klingebiel (1996b) indicate that the problem would be much easier if defmition of banking crisis above linked with insolvency of banking system More obviously, that is, bad loans are strong enough to "blow out" system's capital Based on data of banking sector in developing countries during 1980s, Caprio and Klingebiel (1996b) suggest that nonperforming loans must account for at least percent of total loans so that loan loss would be sufficient to wipe out banking system Under this definition, Caprio and Klingebiel (1996b) track more than 80 systemic banking crises around the world This dataset is updated through various studies including Caprio and Klingebiel (1996c) and Honohan et al (2005) 2.2 Banking crisis theory: A number of studies have been developed around the world to provide insight view as well as explain logic behind crisis trouble in banking sector 1• One interesting thing is that there is no hope to find full theory in any study The theory is supplemented across study to study For that reason, in order to catch whole picture of banking crisis, it is quite useful to examine various studies This paper, thus, introduces banking crisis theory mainly referring to works of Ergungor and Thomson (2005) and Demirguc-Kunt and Detragrache (1998a) because they are the most updated and easily understandable version At the beginning, the theory starts Eichengreen and Arteta (2000) list more than ten studies about systemic banking crisis, See Eichengreen and Arteta (2000) page 39 - 9- at classic view, next Demirguc-Kunt and Detragrache (1998a), finally Ergungor and Thomson (2005) In the classic view, systemic banking crisis is recognized as a result of series of macro-economy instability events Under this view, insolvency problems at one bank probably cause runs by depositors on other banks in the system If there is no protection or guarantee by monetary authorities, the fear of bank insolvency would spread through the banking system, and hence, liquidity pressure would lead a systemic collapse The occurrence of bank run needs to satisfy three conditions First of all, asymmetric information must exist so that depositors can not distinguish healthy banks Consequently, a run on weaker banks may transmit itself to healthy ones Secondly, sequential withdrawals must be paid at par until the bank is closed The final condition is that lack of precautionary plans for providing liquidity to the bank that faces the runs Considered from that viewpoint, systemic banking crisis is depositors' rational respond against information shock Ergungor and Thomson (2005) argue that the classic view fairly explains well the logic inside banking crises, but this theory is less useful to explain source of the banking crises of the last 20 years In their research about determinants of banking crisis, Demirguc-Kunt and Detragrache (1998a) contribute to banking crisis theory by focusing on role of bank system in economy as well as problems on its balance sheet The theory shows that banks are financial intermediaries whose responsibility is to provide liquidity for real economy The bank serves its intermediary role by mobilizing short-term deposits from savers and lending that money, usually in long-term loans, to borrowers Therefore, as value of bank's asset does not match value of liabilities side, the bank gets into trouble A decline in value of asset probably comes from deterioration of borrowers' balance sheet This leads to borrower being unable or unwilling to pay back money (credit risk) The credit risk can be mitigated in many ways including diversification loan portfolio by making loan toward various risk sectors, or requiring loan collateral Actually, diversification loan portfolio is unable - 10- Bancode Montevideo-Caja Obrera, the third largest private bank in Uruguay Following the months of massive withdrawals, foreign exchange reserves were really low (see the figure below) In December 2001, total reserve declined to 650 million, nearly 80 percent compared to period before the crisis (Luis and Sophie, 2005) Under the pressure of external debt obligation, the Uruguay central bank determined to float its currency and implemented 5-day holidays in July 2002 Figure 2: Peso/US$ Exchange Rate and Evolution of US$ Reserves (January to December 2002) - - - •Official Intemational ReSMVes (Left Axis) Exchaoge Rate (Right Axis) 30 3,000 2,500 eE = rg 2,000 '" - ' 1,500 1,000 \ 500 Ju Pel> Mar Apr May 1• ' -· ·- - Jul All& Sap Oct Nov 10 Dec Source: Luis and Sophie 2005, page 10 5.3 Implementing the best unbiased model to Thailand and Uruguay cases: failure to explain the crisis originating in balance of payment crisis To examine the prediction power of the model, the author uses specification 5a to access crisis probability in Thailand and Uruguay from 1996 to 2002 The figure below shows that prediction of crisis probability for Thailand is more stable than those of Uruguay As can be seen, the prediction probability in Thailand reaches the peak of 0.3 in 1997 (the beginning year of Thailand crisis) and decline slowly in following years while the prediction values in Uruguay fluctuate slightly before climb to the top of0.469 in 2001( the beginning year ofUruguay crisis) -43- Figure 3: Predicted Probability Of Banking Crisis in Uruguay And Thailand With Specification Sa 0.6 0.5 -Uruguay crisis 2001- 2000 ~ 0.4 !i5 e Q -ThaiLand crisis 1997- 0.3 2000 0.2 0.1 1996 1997 1998 1999 2000 2001 2002 years Source: Author's calculation What we see in the figure tells us that the model obviously makes better prediction on Uruguay crisis than Thailand If using a fifty percent cut-off(the wellknown threshold) to determine whether banking crisis occurs Of not, the model completely fails to explain Thai crisis This result is totally a big surprise As described in previous section, Thailand economy has begun to show its potential instability in banking system since early 1990s Under pegged exchange rate regime and belief in protection of IMF and Thai government, Thai market become a fruitful and safe land for international investors to earn profit The crisis erupts when speculators realize that the bahts are highly overvalued Soon, speculative attacks occur, pegged exchange rate regime collapse, high interest is introduced One noticeable thing i$ that the model can not predict Thai crisis in regardless of some macroeconomic conditions during crisis time such as sharp devaluation of exchange rate or growth rate (2 variables highly significant in our model) To address this issue, it is useful to look back marginal effect of exchange rate hi table 3a and 3b, its average marginal effects are quite low, ranging from 1.025E-04 to 1.03E-04 for additional unit change of exchange rate Therefore, prediction power of model is reduced considerably despite speculative attacks on July 1997 led to a surge of exchange rate, moving from 25.61 to 47.65 (nearly 54%) Actually, in recent researches about bank crisis such as Demirguc-Kunt and Detragrache (1998a), Hagen and Ho (2002), using absolute value to evaluate effect of exchange rate on -44- banking sector problem still receives top priority Although this technique can provide empirical evidences for our hypothesis, prediction power of our model is likely to be reduced considerably, especially as handling crises originating from balance of payment crisis -45- CHAPTER 6: CONCLUSION AND POLICY RECOMMENDATION Since early 1980s, the need to identifY determinants of banking crisis becomes more urgent than ever before The systemic banking sector problems emerged frequently around the world and its contagious effects always come up with budgetary burden, output loss as well as long-run depression Various studies about banking crisis show that despite the crisis in banking sector varies across the countries and overtime, some factors play an important role in explaining the logic behind many crises In this study, multivariate logit model is applied to identifY these factors The author finds that banking crisis is more likely to erupt as macro-conditions are weak, especially when economy experience low growth GDP Our regression results also indicate that high rate of inflation increases the risk of banking sector problems As mentioned in theoretical section, inflation increases risk of crisis through nominal interest rate that makes bank system more difficult to serve its maturity transformation function Therefore, restrictive monetary policies are quite necessary in the context of infl~tionary economy One thing should pay more attention is that an inflation stability program is likely to cause volatile rise on real interest rate Empirical evidences previous chapter tells us that increased risk of banking sector could be result of high real interest rate Hence, inflation control program should be designed carefully, particular in balancing between effectiveness and a cost of possible banking crisis In our specifications, a sharp devaluation of exchange rate is also associated with banking volatility The risk of crisis due to a sharp depreciation of domestic currency is particularly high when bank's liability side dominated with foreign deposits In such case, exchange rate risks will double debt obligations' banks with depositors and turn the banks to be insolvent One new findings in this paper is that implementing absolute value to evaluate contribution of exchange rate to risk of banking crisis is seems unsuitable Descriptive statistic evidences show that although -46- exchange rate risk is acknowledged as one of the key factors causing banking fragility in Thai banking system, almost influences of exchange rate on banking crisis are reflected less correctly in Thai case In other words, the model tends to ignore effect of exchange rate when absolute value of exchange rate are low and vice versa Unlike Demirguc-Kunt and Detragrache (1998a), our findings suggest that there is no empirical evidence to confirm real likelihood of explicit insurance and banking fragility As discussed above, scientific studies are inconsistent with each other to explain effect of deposit insurance scheme on probability of banking crisis DemirgucKunt and Detragrache (1998a) find that crises are more likely in countries with a deposit insurance system take place while Hagen and Ho (2003) not recognize any persuasive evidence in their research This contradiction not only comes from implementing different approaches in their studies, but it also belongs quite much to what determinants they use For instance, Demirguc-Kunt and Detragrache (1998a) use dummy variable and quality of law enforcement to test effect of deposit insurance scheme, but Hagen and Ho (2003) use dummy variable only Moreover, experts also believe that deposit insurance variable may be insignificant as amount of bail-out package are quite low for each depositor Thus, under so many unreliable conditions it seems impossible to address likelihood of deposit insurance and banking fragility Further researches are required to have more confident results Another important determinant expected to have great contribution to volatility of banking sector is liberalization Theory of banking crisis has shown that effect of liberalization program is likely to spread banking sector through at least three channels including financial system opening, interest rate deregulation, and bank loan deregulation Its usual consequences are sharp rise of real interest rate and credit booming in countries where financial liberalization program introduced In our study, both real interest rate and credit booming are used to verify how closely financial liberalization program connects with probability banking crisis Even though there is a -47- strongly empirical evidence to confirm relationship between banking crisis and real interest rate, it can not conclude that financial liberalization program facilitate crisis in banking sectors as real interest rate could be a consequence of group of factors other than financial liberalization programs However, our regression results indicate that (not very strong) liberalizing financial system could generate more risks for credit market In that case, credit booming will deteriorate real sector, and afterwards, spread to banking sector Using absolute value of exchange rate found is unappropriate technique to access banking sector problem, particularly relating to prediction power of model The model tends to ignore crises that originating surge in exchange rate In other words, the prediction probability is high in countries which domestic currency is low to US dollar and vice versa Thus, implementing percent rate is obviously good alternative solution Surely, this is not great finding, but it is worth for noting in further researches There are some shortcomings existing in our study Firstly, our definition of banking crisis is probably not precise Hagen and Ho (2003) show that such definition could define the crisis too late because the crisis may star before it plagues banking system Secondly, our study also ignores some errors in timing crisis periods • Lastly, exploiting annual data to explain banking crisis is obviously a limitation when evidences in Thailand and Uruguay during crisis periods indicate that crisis could be accessed in month This, thus, leaves a question about accurate time of the crisis Dataset of banking crisis episodes used in this study is mainly developed by Caprio and Klingebiel Even though most ofbanking crisis periods in this dataset receive support of many experts, this dataset can not avoid shortcomings as the most useful rule for banking crisis is liquidity in banking system, not experts' opinion -48- APPENDIX: Growth Rate change of real GDP IFS-line 99bvp % Demirgii9-Kunt et al Dummy variable for presence (2006b), of explicit insurance, it takes Garcia (1999), when a full or partial Deposit insurance for depositors is Dummy Ambiguous association of deposit introduced otherwise it insurance (2008) equals Credit Ratio of credit to private growth sector to GDP International Ratio + GDP- IFS line 99B M2 is money + quasi M2/Reser ve M2 to foreign exchange reserves of central bank Ratio + money IFS-line 34+35, reserves come from IFS-line ld.d IFS-line 60 or line Realinterest Nominal interest rate minus inflation rate % + 60L minus inflation rate collected from WDI2007 National Ex Change of exchange rate currency over US + IFS-line ae + IFS- line 64.x dollar Inflation TOT Rate change of consume price index Change in terms of trade % Ratio -49- WDI 2007 SAMPLE COMPOSITION N.o 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Name of Countries Albania Bangladesh Barbados Belize Benin Bhutan Botswana Burkina Faso Burundi Cambodia Cameroon Cl!Pe Verde Central African Rep Chad Chile Colombia Congo, Republic of Costa Rica Cote d'Ivoire Cyprus Dominican Republic Egypt Equatorial Guinea Estonia Ethiopia Fiji Ghana Grenada Guatemala Guinea-Bissau Guyana Honduras Hk India Indonesia Israel Jordan Kazakhstan Kenya Kuwait Lao People's Dem.Rep N.o 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 -50- Name of Countries Latvia Lesotho Libya Malawi Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Niger Nigeria Oman Pakistan Panama Papua New Guinea Paraguay Peru Phil!Qpines Rwanda Saudi Arabia Seneg_al Seychelles Sierra Leone Solomon Sri Lanka Sudan Swaziland Thailand Togo Trinidad and Tobago Tunisia Uganda Uruguay Venezuela, Rep Bol Yemen, Republic of Zambia - AVERAGE MARGINAL EFFECTS OF DETERMINANTS ON PROBABILITY OF BANKING CRISIS OCCURRENCE -·· ·····-· 012 012 010 010 D D D 008 • 008 'b 006 006 004 004 002 002 ,, • 'D 000 -120 -80 -40 40 000 -20 80 -10 10 ••• Dec 20 30 GROWTH REAL_INTEREST_RATE c For countries without deposit insurance • For countries with deposit insurance c For countries without deposit insurance • For countries with deposit insurance 012 000100 010 000095 008 000090 000085 • • •• • 0 % 000080 • '•• 006 ' •• 004 •• 1! 'b 000075 1! 1! .002 00 • 1! .000070; -r -. l 4000 12000 8000 000 -40 Exchange Rate 40 80 120 160 200 INFLATION o For countries with deposit insurance • For countries without deposit insurance 1! For countries without deposit insurance • For coutries with deposit insurance -51 - Macroeconomic variables and prediction probability of banking crises in Thailand and Uruguay Thailand crisis 1997 Growth Rate and Prediction Probability Inflation and Probability Prediction Probability and Exchange Rate 1-inflation -+-probability I 1-exchange rate -+-probability I Real interest rate and prediction probability 1-Growth rate -+-Probability I 1- 0.35 50 / 0.25 1:' 0.2 : 0.15.: ~ 0.1 :; i 0.05 C!l e 0.25 ~ 0.2 ~ ~4 f• j3 ~ 0.15 ~ 0.1 a 0.05 -10 ~~~~+-~-+~+-40 • 'ES 0.25 1:' -+-t t'!H""-t-+""-t-Y 0.2 ~ f u 0.3 ~ ~ 7. ,-0.35 10 - - - - - - - , 0.35 0.3 Real interest rate 0.15 0.1 Oi2 It: I • 0.05 0 199ti 1997 1998 1999 2000 2001 2002 1996 1997 1998 1999 2000 2001 2002 Years Years -15 - ' - - - - - - J 1996 1997 1998 1999 2000 2001 2002 Years Years Uruguay Case (1996-2000) Exchange rate and Prediction Probability w = 0.4 , 0.2 ~ D 0 30 ,.c 25 0.6 0.5 > 0.4 ~ 0.3 -: 0.2 ~ 0.1 D CD !: CD - [ • Real interest rate -+- Probabiltty [ 0.6 0.6 c., 20 c• u00::1Q Real Interest Rate and Prediction Probability \ • GroY.lh rale -+-ProbabUily \ J•lnflation -+ Probability I J• Exchange rate -+ Probability I 30 Growth And Prediction Probability Inflation and Prediction Probability .c.~20 c.!!15 u- , !::1Q =o5 1996199719981999 2000 20012002 1996199719981999 2000 20012002 Years Years 0.5 ~ ""-2 0.4 ;; 0.3-: ';-4 a ~.I) 0.2 ;: C> -8 -10 -12 0.1 -:;;-:;30 -;;; 20 10 -a:: ""' 1996 1997 1998 1999 2000 2001 2002 Years -52- 0.6 0.5 > 0.4 ~ 0.3-: 0.2 """ ~ 0.1 60 -:;; 50 ~ 40 Years REFERENCE • Allegret, J., P., Courbis, B., & Dulbecco, Ph., 2003, Financial Liberalization and Stability of the Financial System in Emerging Markets: The Institutional Dimension of Financial Crises, Review oflntemational Political Economy, Vol 10, No 1, pp 73-92 Calvo, G., A., 1996, Capital Flows and Macroeconomic Management: Tequila Lessons, International Journal of Finance and Economics, Vol1, pp.207-223 Caprio, G & Klingebiel, D., 1999/2003 Episodes of Systemic and Borderline Financial Crises [online] Available at www.banking.mfpa.ru/files/dataset Ol.pdf [accessed june 16 2009] Caprio, G & Klingebiel, D., 1996b, Bank Insolvency: Bad Luck, Bad Policy, or Bad Banking? 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(ii) How determinants affect on probability of banking crisis occurrence? (iii) How is marginal effect of. .. OF TABLES Table 1: Regression results ofbanking crisis determinants: the panel eliminating observations following end year of crisis 31 Table 2: Regression results of banking crisis. .. chapter initially focuses on clarifying the defmition of banking crisis Then, various theories of banking crisis are introduced in order to get hypotheses on how determinants affect on probability of