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