BUILDING AN EARLY WARNING SYSTEM FOR ISLAMIC BANKING CRISIS IN INDONESIA: SIGNAL APPROACH MODEL

BUILDING AN EARLY WARNING SYSTEM FOR ISLAMIC BANKING CRISIS IN INDONESIA: SIGNAL APPROACH MODEL DIMAS Bagus Wiranata Kusuma 1, ABU Asif 2 3 International Islamic University Malaysia, Selangor, Malaysia 4 International Islamic University Malaysia, Selangor, Malaysia dimas_economist@yahoo.com ABSTRACT Background Islamic Banking is increasingly growing and has enjoyed a steady and consistent horizontal contribution in economic growth. However, given the operation of current Islamic banks are connected with conventional banks, it might trigger crises once the conventional banks are trapped into crises. Purpose of Study - This paper tries to focus on three essential issues, namely first, to identify crises periods or period of unusual volatility on Islamic banks, and second, to evaluate the alternative filtering mechanism which leads to the minimum type I error by exercising four leading indicators, and third, to estimate the out-sample data by using in-sample results estimation in order to testify the reliability of EWS predictability towards the onset of crises. Data and Methodology We develop an Islamic Banking Sector Fragility (IBSF 2 ) Index and employ four leading indicators, namely M 2 /Reserve growth, domestic credit growth, real effective exchange rate, and inflation rate. To establish an early warning system mechanism, we adopt an extraction signal approach, four different threshold levels, and three kinds of signal horizons to generate a signal. The data spans from March 2004 to December 2006 and these data are included as in-sample observations. Differently, the out-of-sample observations are incorporated since January 1 Candidate Doctor of Economics, Kulliyyah of Economics and Management Sciences, IIUM (+60-102906105). 2 Candidate Master of Islamic Banking Finance, IIUM. 3 Department of Economics, International Islamic University Malaysia (IIUM). 4 Department of Institute Islamic Banking and Finance (IIBF), IIU-Malaysia. 1

2007 to June 2012 by employing the similar variables used for in-sample observation. Results According to IBSF 2 index, the Islamic Banking in Indonesia during period of observation shows a decreasing and falling direction in the IBSF 2 index, which are below -0.5. It suggests that Islamic banking posits fragility condition to systemic crises. Additionally, the four employed leading indicators are leading to be best in minimizing type I error, if utilize the threshold developed by Garcia and use 24 months signal horizon. The out-of-sample analysis shows that the findings support the use of longer forecast horizons to perform EWS analysis and the model analytically could improve the quality of the estimation and hence that of the forecasting ability. Conclusion IBSF 2 is able to figure out the development process of Islamic banking crises in Indonesia over March 2004 to June 2012. By utilizing signal generating mechanism, the study is able to expose some signals and detect crises phenomena through employing four leading indicators, either in-sample or out-of-sample period. However, the extended methods and leading indicators are strongly encouraged to enhancing a surveillance mechanism on Islamic Banking in Indonesia. Keywords: Islamic Banking, Signal Approach, Crises, Indonesia JEL: E44, F15, G01 1. INTRODUCTION Being a growing industry, Islamic banking has enjoyed a steady and consistent horizontal contribution for boosting economic growth in some emerging economies, such as Indonesia and Malaysia (Madjid, and Kassim, 2009; Kusuma, 2011). In fact, being able to endure the implication of the global financial crisis and remain relatively positive 5 in the midst of the crises, it has raised the profile of Islamic banking and concomitantly underscored its capacity to bring stability to the global financial system. Hence, the presence of its stability and current positive trend has 5 According to Bank Indonesia report 2009, during the global financial crises (2008-2009), Islamic Banking main indicators showed some positive trends, namely (1) Islamic commercial banks, Islamic Rural Banks, and Number of offices increased I, 8, and 272 units, respectively, (2) total assets, deposit, and financing raised roughly 33.37%, 41.84%, and 22.74%, respectively. 2

encouraged Muslim scholars and practitioners to then justify that Islamic Banking has the potential possibility to become an alternative model for global financial system. However, like the other financial institutions, the operations of Islamic banks face some risks associated with, for example, credit risk, liquidity risk, and exchange rate risk. For Indonesian case, such mentioned risks become embedded elements due to some reasons, namely: - The dual system has implied some connected relationships between the development in conventional banking and Islamic banking. This is reflected by, for instance, the depositor consumer behavior in Islamic banks which is yet influenced by the offered interest rate by conventional counterparts. - The current practices of Islamic Banks are not totally in accordance with the spirit of operational ground in establishing the Islamic Banking due to dominantly operates under the debt and trade-based transactions, instead of promoting equity-based contracts. Above mentioned situation, in fact, might create a triggering factor to instability in which depositors wanted to transfer not share their risks fully to the equity holders. Eventually, the failure in managing such risks, assumed that depositors are rational and emotionally motivated by profitoriented, it would eventually promote displaced commercial risk and later might trigger vulnerability into crisis in Islamic banking. Therefore, a higher exposure into banking crisis calls for an increasing awareness of the most closely watched market indicators, or building an early warning system (EWS) model. Given connected relationship between Islamic and conventional banking, there could be similar costly borne by Islamic banking once they trapped into crisis. Caprio and Klingebiel (1996b) reveal that there have been more than 65 developing country episodes during 1980-95 when the banking system s capital was completely or nearly exhausted. In addition, the public-sector bailout costs of resolving banking crises 6 in developing countries during this period have been estimated at around $250 billion 7. In more than a dozen of these banking crises, the public sector resolution costs amounted to 10 percent or more of the country s GDP 8. In the case of Asian, specifically, the cost of bank recapitulation for the country most affected, 6 Actual banking crises are the product of expectations-driven shocks or other events related to a change in underlying fundamentals (Jiang, 2007,p5) 7 This figure is net of the estimated amount of loans that were eventually repaid. See Honohan (1997) 8 See Goldstein (1997) for a list of these severe banking crises 3

namely around 58% of GDP for Indonesia, 30% of GDP for Thailand, 16% of GDP for South Korea, and 10% of GDP for Malaysia (World Bank, 2000). But, generally, Haggarth and Sapporta (2001) estimate that cumulative output losses from banking and twin crises 9 were very much greater in OECD countries (28.2% GDP) than in emerging market economies (13.9%). Having said that banking crisis is costly and might create systemic risk 10 in economic system, an implementation of EWS for Islamic banking crisis is very important and calls for existence. There is increasing demand for predicting the performance of Islamic banks due to the vital importance of prior information on any problem that may face any Islamic bank before it materializes. EWS for Islamic banks will save on the costs of banks band performance or failure to depositors, owners, and the society. Thus, Al- Osaimy and Bamakhramah (2004) argue that the rationale for a EWS comes from the following reasons: - Identifying the possible causes of bad performance. - Facilitating the surveillance of banks and reducing its costs. - Proper timing of examining problem banks and scheduling the remedial procedures. Inspired by Kibritcioglu (2003), this paper attempts to contribute to the EWS in Islamic Banking literature by developing a model to monitor and predict impeding banking sector problem. In addition, Kaminski, Lizondo, and Reinhart (1998) developed some leading indicators which this paper uses as predictor of crises. Then, an extraction signal approach is utilized to assess the behavior of single variable in explaining the symptom of banking crisis. More specifically, against this background, the paper has three specific objectives; the first one identifies crises periods, or periods of unusual volatility, based on monthly information for the period from March, 2004 to June, 2012. The second section evaluates the alternative filtering mechanism which leads to the minimum type I error by employing four 9 Defined as cases where a currency crisis occurs within the period 2 years before and after the banking crisis. 10 It is an environment whereas panic and instability was existence so that an emergency lender should not be doubted by the market participants. The central bank should pronounce its willingness to lend and its support should be visible. Decisions to provide support should be part of a general crisis management strategy and should be made jointly by the monetary, the supervisory, and the fiscal authorities. The repayment terms may be relaxed to accommodate the implementation of the bank restructuring strategy. Emergency liquidity support in these circumstances should be explicitly guaranteed by the government and any loss thus incurred should be fully compensated by the budget (He, 2000, p5). 4

leading indicators. Finally, the third objective is to estimate the out-sample data by using in-sample results estimation in order to testify the reliability of EWS predictability towards the onset of crises. The rest of the paper is structured as follows. In the next section, we provide background information about the development of Islamic Banking in Indonesia. Then, section 3 describes literature review. Section 4 contains the data and details of the framework used in the analysis. Section 5 reveals the results of estimation. Finally, section 6 summarizes the main findings and provides recommendations. 2. BACKGROUND Beyond Banking, that is the brand promoted by Bank Indonesia, particularly, Directorate of Islamic Banking, to attract more public awareness of the benefit shared by Islamic Banks. Thus, Bank Indonesia during 2010-2015 has proposed development strategies to enable its expansion and expose to higher performance in their operational schemes. That attraction strategy for expansion was revealed by creating conducive environment, simplifying licensing procedures, and maintaining sustainable growth of supply and demand. Such policies seem to be effective by looking at Islamic banking network. The table below shows that in 2005, the number of Islamic commercial bank was recorded for 3 banks and 304 offices, but it had grown to 11 banks and 1.529 offices, respectively by June 2012. It also prevailed for Islamic business and Islamic rural bank which was showing increasingly trend. Table 1 Islamic Banking Network 2005 2006 2007 2008 2009 2010 2011 June 2012 Islamic Commercial Banks 3 3 3 5 6 11 11 11 Number of Office 304 349 401 581 711 1215 1401 1529 Islamic Business Unit 19 20 26 27 25 23 24 24 Number of Office 154 183 196 241 287 300 336 470 Islamic Rural Bank 92 105 114 131 138 150 155 156 Number of Office 92 105 185 202 225 286 364 378 Source: Bank Indonesia Website Various Sources. In addition, nowadays, Islamic banks have been providing numerous types of financing to facilitate consumer needs. In 2000, Islamic banks preferred to transact under the sell and buy concept which accounted for 5

around 61% out of financing schemes. While, the equity-based financing recorded around 32.3%. Nevertheless, presently, either customers or bankers have been commencing to realize on what important of equity based contract is. Over the last nine years, in June 2012, the composition mix had changed whereas the trend of sale based contract showed a slackened line (57.6%), and conversely the equity based contract conveyed slightly decrease (28.2%). Table 2 Islamic Financing Based on Type of Contract in Islamic Commercial Bank and Islamic Business Unit Type of Contract 2000 2005 2006 2007 2008 2009 2010 2011 June 2012 Musyarakah 0.025 0.125 0.114 0.158 0.194 0.222 0.215 0.185 0.189 Mudharabah 0.298 0.205 0.199 0.200 0.162 0.141 0.127 0.099 0.093 Murabahah 0.610 0.623 0.617 0.592 0.589 0.561 0.550 0.549 0.576 Salam 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 0 Istishna 0.059 0.018 0.016 0.013 0.009 0.009 0.005 0.003 0.003 Qardh 0.000 0.008 0.000 0.000 0.025 0.039 0.034 0.037 0.044 Others 0.008 0.021 0.053 0.038 0.020 0.027 0.069 0.126 0.094 Source: Bank Indonesia Website Various Sources. In terms of the economic sector financed, the below table provides some facts that over five years, Islamic banks dominantly had financed to two main sectors, namely Trade, restaurants, and hotels, and business services. The former sector accounted for about 11.24% and 1.70, respectively in 2005 and 2010. Meanwhile, the later recorded roughly 29.49% and 30.37%, respectively in 2005 and 2010. It implies that Islamic banks are reluctant and risk-averse to involve in low exposure sector, such as agricultural, forestry, and agricultural facilities which made of 4.5% in 2005 and decreased to 2.72% in 2010. However, interestingly, in June 2012, agriculture, forestry, and agricultural facilities placed the second rank, around 8.98%, after trade, restaurants and hotels around 32.93%. It had increased more than 5% compared with in the year 2010. In addition, trade, hotels, and restaurants had double compared with 2010, 32.93%. It informs that Islamic banking has gradually changed their policies to more close for real sector. 6

Table 3 Islamic Commercial Bank and Islamic Business Unit Financing by Sector in Indonesia (%) Sectors 2005 2006 2007 2008 2009 2010 2011 Agriculture, forestry and agricultural facilities 4.5 3.40 3.00 3.08 2.99 2.55 8.37 8.98 Mining 2.59 1.83 1.83 2.52 2.35 0.22 0.09 0.11 Manufacturing 6.11 4.60 4.91 3.50 3.54 0.58 1.26 1.03 Water, gas and electricity 0.43 0.08 0.59 0.65 1.56 0.04 0.10 0.17 Construction 10.14 8.01 8.48 8.82 7.90 1.58 3.46 3.17 Trade, restaurants and hotels 11.24 14.87 14.86 11.59 11.23 14.85 37.39 32.93 Transport, cargo storage and communication June 2012 8.26 5.70 5.61 7.22 7.52 0.52 1.36 1.44 Business services 29.49 26.69 30.15 30.78 30.69 6.58 9.54 12.44 Social services 7.91 7.12 6.81 6.44 5.57 2.55 8.37 8.98 Others 19.33 27.66 23.76 25.38 29.95 0.02 0.09 0.11 Source: Bank Indonesia Website, Various Sources. In terms of Islamic banks performance, to date, it has recorded an increasing move in their assets, financing, and deposits. In 1992, the assets, financing, and deposits were accounted for only Rp 120.880, Rp 32.560, and Rp 20.800 million, respectively. However, over next two decades, it had tremendously increased to Rp 85.454, Rp 60.912, and Rp 63.912, billion, respectively. Every year, in average, those indicators rise around 30% and it obviously signifies that Islamic banking industry in Indonesia will have been growing rapidly and tending a promising sector to spur the economy. Interestingly, in 2011, the growing of Islamic banking industry was recorded as the highest percentage, around 50% for asset, financing, and deposit schemes. Table 4 Performance of Islamic Banking in Indonesia (in Billion Rupiah) Year Asset Financing Deposit Growth (%) Asset Financing Deposit 1992 120,880 32,560 20,800 1993 166,960 92,000 60,320 38.12 182.56 190.00 1994 246,080 188,800 132,880 47.39 105.22 120.29 1995 394,400 285,920 275,680 60.27 51.44 107.47 1996 515,200 310,480 396,560 30.63 8.59 43.85 1997 586,720 456,160 463,440 13.88 46.92 16.87 1998 479,200 317,040 391,920-18.33-30.50-15.43 7

1999 692,800 342,560 528,080 44.57 8.05 34.74 2000 1,790,168 1,271,162 1,028,923 158.40 271.08 94.84 2001 2,718,770 2,049,793 1,806,366 51.87 61.25 75.56 2002 4,045,235 3,276,650 2,917,726 48.79 59.85 61.52 2003 7,858,918 5,530,167 5,724,909 94.28 68.78 96.21 2004 15,325,997 11,489,933 11,862,117 95.01 107.77 107.20 2005 20,879,874 15,231,942 15,582,329 36.24 32.57 31.36 2006 26,722,030 20,444,907 20,672,181 27.98 34.22 32.66 2007 36,537,637 27,944,311 28,011,670 36.73 36.68 35.50 2008 49,555,000 38,199,000 36,852,000 35.63 36.70 31.56 2009 66,090,000 46,886,000 52,271,000 33.37 22.74 41.84 2010 97,519,000 68,181,000 76,036,000 47.55 45.41 45.46 2011 145,467,000 102,655,000 115,415,000 49.16 50.56 51.79 June 2012 155,412,000 117,592,000 119,279,000 6.83 14.55 3.34 Source: Bank Indonesia Website Various Sources. Finally, the reports convey that according to type of usage in Islamic banks financing, the portion for working capital and investment were dominantly noted roughly 80% and 57%, respectively in 2005 and 2012. Nevertheless, these compositions tended to slow down year by year replaced by consumption purposes. As noted, in 2005, the fund goes for consumption was 19.4% and jumped to 42% in 2012. However, Islamic banks overall are yet committed in promoting small medium enterprises (SMEs). It was figured out that almost 70% financing disbursed for SMEs, and it rose year by year to 75.29% in 2010, but slightly went down to 69% in June 2012. Table 5 Financing of Islamic Commercial Bank and Islamic Business Unit Bank Based on Type of Usage Working Capital Investment Type of Usage 2005 2008 2009 2010 2011 Consumption Amount Share Amount Share Amount Share 7,988 52.4% 4,288 28,1% 2,956 19,4% 20,554 53,8% 7,907 20.7% 9,734 25,5% 22,873 48,8% 9,955 21.2% 14,058 30,0% 31,855 46.72% 13,416 19.78% 22,910 33.60% 41,698 40.62% 17,903 17.44% 43,053 41.93% June 2012 46,603 39.63% 20,719 17.61% 50,271 42.75% Total 15,232 38,195 46,886 68,181 102,655 117,592 Source: Bank Indonesia Website Various Sources. 8

Table 6 Financing of Islamic Commercial Bank and Islamic Business Unit Based on Type of Financing Type of Financing Small and Medium Enterprises Non Small and Medium Enterprises 2005 2008 2009 2010 2011 June 2012 10,196 66.94% 5,036 33,06% 27,063 70.85% 11,132 29.15% 35,799 76.35% 11,087 23.65% 52,570 77.10% 15,611 22.89% 71,810 69.95% 30,845 30.05% 81,218 69.07% 36,375 30.93% Total 15,232 38,195 46,886 68,181 102,655 117,592 Source: Bank Indonesia Website Various Sources. 3. LITERATURE REVIEW As far as concerned, the study related with EWS model for Islamic banking crisis is rarely found. So far, there are just two studies which closely deliberate on such issue. The study conducted by Al-Osaimy and Barakhramah (2004) which identifies the possible causes of bad performance, detect potential problem banks, facilitate surveillance of banks as well as reduce its costs and make possible proper timing of examining problem banks as well as scheduling the remedical procedures. The discriminant analysis technique was utilized in this study whereas financial ratios 11, comprising of productivity, efficiency, liquidity risk, and leverage are as explanatory variables, and the profitability rate as dependent variable. Discriminant 12 scores are then extracted and used to distinguish between high performance and low performance groups of banks. The results reveal that all explanatory variables turned to be significant in the discriminant function utilized to test the classification accuracy and prediction reliability of the model. The study later suggests that a EWS of performance of Islamic banks could be improved when allowing for larger disaggregate number of explanatory (discriminant) variables to be included, such as political variables, macroeconomic variables, and managerial variables. Another literature is intended to show some observation regarding a non-exhaustive collection of EWS literature from 1971 to 2011. According to Eray (2011), there is an evolution of interest in EWS model, methodological spectrum, and coverage of economic variables. He adds that 11 It is obtained from 40 letters of various Islamic banks, particularly uses data on balance sheets and income statements during the period 1991-1993. 12 It is a statistical technique used to classify a sample of observation into two or more groups based on a linear composite of input variables. 9

interest in EWS models seemed to increase after mind-1990s, and had its climax between 2001 and 2005. Source: Eray (2011) As far as the methodologies are concerned, generally binary dependent variable family of models seem to have been most popular. Individually, the logit analysis is the first (21 out of 124), while signal extraction analysis and discriminant/factor analysis expose the second place (14 out of 124). 10

Table 7. Popularity of Methodologies in early Warning Studies Source: Eray (2011) Once the EWS is established, the leading indicators are arranged to set as an early warning indicators. Eray (2011) suggests that globalization was envisaged as quite a strong motivation for EWS, and it enables to put aside the firm-level data on financial ratios. The survey indicated data on balance of payment and fiscal performance are the first followed by monetary aggregate and credit data. Then, the exchange rate and interest rate data, followed by domestic economic activity indicators are almost equally important incorporated in EWS. Details are shown below. 11

Table 8. Popularity of Variables in Early Warning Studies Source: Eray (2011) Since we rarely find out the study on EWS for Islamic banking, the paper tries to refer on what has been undertaken under conventional case. Therefore, below, the paper provides some studies related with banking crisis followed by its methodologies and variables. Table 9. Some Selected Literatures Review on Banking Crises Title Year Focus Method Data Coverage Bank Regulation, Property Prices, and EWS for banking crises in OECD countries Comparing EWS for banking crises Reviving the Indonesian Banking Sector? Indonesia s Economic Crisis: Impact on Financial and Corporate Sectors 1997-1999 Currency and Banking Crises : The Early 2010 2008 2000 1999 Banking Crises Banking Crises Banking Sector Currency crisis, Logit Multivariat e logit, signal extraction Descriptive Signal extraction, Real GDP Growth, Real Interest Rate, Inflation, Fiscal Surplus/GDP, M2/Foreign Exchange Reserve, Real Domestic Credit Growth, Liquidity ratio, Un-weighted Capital Adequacy ratio, Real Property Price Growth. Real GDP Growth, Change in Terms of Trade, Depreciation, Real Interest Rate, inflation, Real GDP per Capita, Fiscal Balance/GDP, M2/International Reserves, Private Credit/GDP, Deposit Insurance, Credit Growth A wide set of macroeconomic, stock market, and sector-specific indicators M2 multiplier, domestic credit/gdp, domestic and external liberalization, bank deposits, excess M1 12

warnings of distress Determinant of Banking crises in developing and developed countries EWS for Financial Crises 1998 and banking crisis Banking Crises Systemic banking crisis composite indication Logit Signal extraction, probit Balances, Exports, Imports, terms of trade, real exchange rate, reserves, m2/reserves, RIR differential, world RIR, foreign debt, capital flight, foreign debt, output, domestic RIR, lending/deposit rate ratio, stock prices. Real GDP Growth, Change In Tot, Rate Of Change Of The NER, Nominal Interest Rate Minus The Contemporaneous Rate Of Inflation, Rate Of Change Of The GDP Deflator, Budget Surplus/GDP, M2/Reserves, Domestic Credit To Private Sector/GDP, Bank Liquid Reserves/Assets, Real Domestic Credit Growth, Dummy For An Explicit Deposit Insurance Scheme, Real GDP Per Capita, Index Of The Quality Of Law Enforcement Base Money, M1, M1 Multiplier, M3 Multiplier, Nominal Credit Growth, Real Deposit Rate, Real Lending Rate, Nominal Interest Rate, Spread Between Bank Rate and 91-Day T-Bill Weighted Average (Of High and Low) Call Money Rate, Return On Banking Stocks, Exports, Imports, REER Overvaluation, NEER, Ratio Of Net Current Account Balance To GDP, Terms Of Trade, Foreign Exchange Reserves, Ratio Of M3 and Foreign Exchange Reserves, Short -Term Debt, Short-Term Foreign Debt As A Proportion Of Reserves, Foreign Direct Investment, FDI As A Proportion Of GDP, Real GDP Growth, Industrial Production Index, WPI, Ratio Of Fiscal Deficit To GDP, London Interbank Offer Rate (1 Month), Us 3 - Month Treasury Bill Rate, International Oil 4. DATA AND EMPIRICAL FRAMEWORK APPROACH SELECTION The present paper tries to empirically identify the existence of crisis in Islamic banking by employing an extraction signal approach or nonparametric 13 approach. According to Kibritcioglu (2003), at least three advantages use the non-parametric approach, namely: 13 This term was elaborated by Kaminsky and Reinhart (1996) to evaluate the usefulness of several variables in signaling an impeding crisis. This methodology could be also interpreted as an extension which compares the behavior of variables in periods preceding crisis with that in a control group. This approach involves monitoring the evolution of a number of economic variables whose behavior usually departs from normal in the period preceding a currency crisis. Deviations of these variables from their normal level beyond a certain threshold value are taken as warning signal of a crisis within a specified period 13

- The banking sector fragility (BSF), as one of components in extracting a signal, which is very easily computed and then BSF is very useful to monitor and interpret the developments in the sector by using monthly banking data. - Non-parametric is easily employed within a single-country framework. - It can be used to differentiate between normal or non-systemic and systemic or crisis, based on the fluctuations in the BSF index. Similarly, Ardiningsih (2002), following the approach developed by Santiago Herrera and Conrado Garcia (1999), promotes the use of signal approach (Non-Parametric) for some reasons: - The approach is the simplest approach for EWS. - The approach can be updated monthly. - It has the lowest feasible cost. - It can be used to aggregate the individual leading indicators 14 into a composite index; and the way this index is used as a signaling device. Therefore, the signal approach is a highly attractive feature of the time-series based statistical approach in order to contribute to policy makers efforts towards early detection of approaching banking sector difficulties. STAGES FOR BUILDING EWS THROUGH SIGNAL APPROACH Defining Signal Crisis Let assume, i=a univariate indicator, j= a particular country, S= signal variable, and X= indicator. An indicator variable relating to indicator I and country j is denoted by and the threshold for this indicator is denoted as. Then, a signal variable relating to indicator I and country j is denoted by:. This is of time (in-sample period or test). Based on the track record of the various indicators, it is possible to access their individual and combined ability to predict crisis (out-sample period or test). 14 It refers to (a) the indicators were used to estimate the probability of a crisis, (b) the indicators pre-crisis behavior was systematically compared with its behavior in a control group, or (c) the indicators ability for signaling future crises was systematically assessed in quantitative terms. 14

constructed to be a binary variable where the threshold, a signal is emitted and = (0,1). If the variable crosses =1. This happens when..(1) If the indicator remains within its threshold boundary, it behaves normally and does not issue a signal, so = 0.. (2) In addition, in taking a conclusive remark, we have to notice the directional sign may vary depending on whether the indicators (leading indicators) in questions (1) and (2) are expressed in absolute terms. Thus for a time series of t observations for country j and indicator i, we can obtain a binary time series of signal or no-signal observations. BUILDING EVALUATION CRITERIA Our evaluation of each model is based on six statistic measurements; the sizes of Type I and Type II errors, the noise to signal ratio, and the probability that a crisis occurs given that a signal was produced. However, as mentioned in the beginning, the focus of evaluation criteria is that to look for the lowest type I error which is shown by lowest noise to signal ratio (NTS). A short description of each one and details on the computation as follow: To visualize the different criteria, we borrow four possible scenarios developed by Kaminski-Lizondo, and Reinhart (KLR) (1997) Table 10. Possible Scenarios of Signals and Crisis Crisis No Crisis Signal Issued A B No Signal Issued C D 1. % of observation correctly called =, defined as the probability that all observations correctly bring information about crisis and not crisis. This implies that the higher percentage occurred will lead to best evaluation criteria for a certain threshold chosen, vice versa. B/(B D) 2. noise to signal ratio ; It measures the false signals A/( A C) (size of type II error) as a ratio of the good signals issued (1-size of type 15

I error). The selection rule is to pick the model that minimizes the noise to signal ratio (NTS) for each country. 3. % of crises correctly called = ; defined as the percentage of crises happened, once the signal was issued. Thus, the higher of its percentage would be better off, vice versa. 4. % of false alarm of total alarms =, given that models expose a frequently false signal, an alternative criterion is to select the model that minimizes the probability of false alarm occurring given that a crisis happened. 5. % probability of crisis given an alarm, given that models generate different signals, an alternative criterion is to select the model that maximizes the probability of a crisis occurring given that a signal was issued as alarm. 6. % probability of crisis given no alarm =, given the signal is important; the crisis without signals was extremely reduced or minimized. DETERMINING SIGNALING HORIZON This is the period within which the indicators would be expected to have ability for anticipating crisis. Kaminsky (1997) defines the period a-priori as 24 months. Meanwhile Bussiere and Fratzscher (2002) set for 12 and 18 months. They argued that various time horizon or signaling horizon provides the best achievable trade-off between missing crises and issuing wrong signals. Thus, this paper tries to utilize 12,18, and 24 months signaling horizon in order to get the optimal time horizon. DEFINING SIGNALS AND THRESHOLDS An indicator is said to issue a signal whenever it departs from its mean beyond a given threshold level. Threshold levels are chosen so as to strike a balance between the risks of having many false signals 15 and the risk of missing many crises 16. For each of the indicators, the following procedure was used to obtain the optimal set of country-specific thresholds that were employed in the empirical. This paper exercises some threshold levels 15 It would happen if a signal is issued at the slightest possibility of a crisis 16 It refers to the signal is issued only when the evident is overwhelming 16

which are applied by Kaminski (1997), Park (2001), Garcia (1999), and Lestano (2003), namely 3 standard deviation (SD), 1,1 SD, 1,5 SD, and 1.0 SD. Later, the all mentioned thresholds are used to determine the crisis periods in order to study the behavior of the employed leading indicators prior to their occurrence. The crises periods is defined as a period in which Islamic banking sector fragility (IBSF) > μ + mσ (where μ is the sample mean and σ the standard deviation of the IBSF, and m the threshold levels). DEFINING ISLAMIC BANKING CRISIS Explicitly, a bank is exposed to the risk that the value of its asset and liabilities dynamically change in financial market. Thus, it allegedly opens the door to a form of inherent fragility or instability. Such that, all banks are potentially exposed to different types of economic risks, such as (i) liquidity risk, for instance the presence of massive bank runs, (ii) credit risk, for instance rising non-performing loans, and (iii) exchange-rate risk, for example banks increasing un-hedged foreign currency liabilities. Therefore, a bank s net worth as well as a bank failure basically can be associated with excessive risk-taking of bankers. In fact, some studies have considered that massive bank runs 17 and withdrawals, enormous lending booms 18, and high increases in the foreign liabilities 19 of the 17 In literature, bank runs and liquidity crises is closely connected. It has been explained either to be caused by depositor panics (see, Diamond and Dybvig, 1983, or Von Thadden, 1998) or by the weak fundamentals like a downturn in the business cycles (See, Gorton, 1988, or Allen and Gale, 1998). In addition, Allen and Gale (2004b) have recently demonstrated how aggregate uncertainty can provoke large fluctuations in asset prices and subsequent bank runs on a number of banks in the economy. 18 Honohan (1997) reviews cases of financial crises in 24 developed and emerging markets and considers policy regime changes as an important source of banking crises. He also includes credit growth into his set of early warning indicators. Demirgüç-Kunt and Detragiache (1998) after analysing banking sector crises in 29 countries conclude that credit growth (lagged two years) is highly significant for explaining a crisis. Kaminsky, Lizondo and Reinhart (1998) review 27 empirical studies and note that most of them mention credit growth as one of the indicators of an upcoming banking sector/ balance of payments crisis. Similarly, Ball and Pain (2000), who review the literature on banking crises, conclude that domestic credit growth is consistently found as a significant indicator of an upcoming crisis. Terrones and Mendoza (2004) analyse credit booms in emerging market economies during the period from 1970 to 2002 and come to the conclusion that 75% of the credit booms were associated with a banking crisis, while 85% were associated with a currency crisis. 19 This indicator was alluded by considering the link between currency and banking crises. Kaminski and Reinhart (1999) analyze 76 currency crises and 26 banking crises for 20 countries during 1970 to mid-1995. On of main findings is that financial liberalization often 17

banking sector are among the most important factors of impeding or triggering banking crises, including in Islamic banks. Therefore, the present paper tries to develop Islamic Banking Crisis by relying upon on the banking sector indicators, but will be connected with the present practices of Islamic banks, namely (1) bank runs and liquidity risk in Islamic banks, (ii) lending boom, Non-performing loans, and credit risk in Islamic banks, (iii) Banks un-hedged foreign liabilities, devaluation, and exchange-rate risk. 1. Bank Runs and Liquidity Risk Saver s massive run on deposits may tremendously trigger the accelerating process of bank runs, even in Islamic banks. Furthermore, Kaminsky and Reinhart (1999) argue that recent banking problem worldwide rise from the asset side namely increases in non-performing loans (NPL), instead of liability side, namely bank runs. 2. Lending Boom, NPL, and Credit Risk Kibritcioglu (2003) mentioned that a lending boom on assets side of a bank s balance sheet is likely to be caused by the bank s poor, or overoptimistic, evaluation regarding the investor s credit applications. Moreover, assumed a bank is allowed to credit risky projects, and then un-bankable creditors might contribute to an increased NPL if the creditors are connected with the bank. In addition, the existence of deposit insurance may increasingly encourage bankers to take excessive risk (moral hazard problem) by easing the credit flows that planned. These considerations might then imply to the judgment that credit booms easily may be linked to banking crises. 3. Banks Un-hedged Foreign Liabilities, Devaluation, and Exchange-Rate Risk Exchange rate risk presents as the existence of financial liberalization and integration across region. It enables foreign bank to invest and flow their loanable funds easily and freely into domestic banks. For instance, the large presence of foreign banks in the EU10 explains only the supply of foreign currency loans, but not the local demand. Most of the borrowers were attracted by lower interest rates on foreign denominated loans, and were aware of the currency risk involved in taking such a loan 20. In turn, the large proportion of loans denominated in foreign precedes banking crises. Similarly, as liability of un-hedged fund could trigger banking currency crises, hence it might promote banking crises later. 20 The un-hedged currency risk born by households and firms with no income in foreign currencies means that if the value of the local currency falls against the euro, Swiss franc or yen in which the loan was taken, the outstanding debt repayments will rise overnight by the same proportion. Large currency swings can thus bankrupt many households and firms. 18

currencies again points to the broader deficiency of the pre-crisis EU regulatory framework that ignored potential macro-prudential problem. CONSTRUCTION OF A MONTHLY ISLAMIC BANKING SECTOR FRAGILITY INDEX A monthly Islamic banking sector fragility (IBSF) index is constructed and used to decide whether a national Islamic banking sector was in crisis at a particular point in time. Because of data availability and we wanted to focus on the relative importance of bank runs and credit risk in banking crises, an IBSF index comprises of two main leading indicators of banking crises, namely (i) Islamic bank deposits (DEP), and (ii) domestic credit (DC). In this regards, specifically, Islamic bank deposits (DEP) are calculated as a monthly depositor funds composition 21 of Islamic Commercial Banks, Islamic Business Unit, and Islamic Rural Bank. Meanwhile, domestic credit (DC) is obtained from a monthly financing of commercial bank, Islamic Business Unit, and Islamic Rural Bank. All index data are expressed in natural logarithm. The data used in the analysis are monthly sourced from Islamic Banking Statistics Bank Indonesia (various issues), and International Financial Statistic (IFS). The observations cover three years, from 2004 to 2006. The data used are considered as in-sample observations. In addition, the year spanning from 2007 to 2012 is to measure out-sample period. Additionally, these two indicators are proxies or indirect indicators of changes in the liquidity risk and credit risk. Thus, the fluctuations in these indicators are supposed to represent the changes in the fragility of banking sector in Indonesia. Therefore, this paper proposes IBSF 2 (general index) to measure the fragility of banks to crisis, namely: Where :..(1) (2) Moreover, countries with high proportion of foreign currency loans cannot let their currencies devalue without many bankrupting households and firms, which is a serious constraint on macro-economic policies used to counter the crisis. 21 It comprises of demand deposits (Wadia), saving deposits (Wadia and Mudharaba), and Time Deposits (Mudharaba 1, 3,6,12,>12 months). 19

.(3) In equation (1), the IBSF 2 index is defined as an average of standardized 22 values of DC and DEP, where μ and σ stand for the arithmetic average and standard deviation of two variables, respectively. In equation (2) and (3), LDC and LDEP represent banking system s domestic financing and total deposits on Islamic banks in logarithm form, respectively. DC and DEP are simply the corresponding annual changes in each and every these two variables. By using 12-month percent changes in the monthly data instead of using monthly changes, we try to avoid any seasonality, which may be appeared into the data. Then, to justify the degree of severity in Islamic banking crises, this paper adopts a concept developed by Kibritcioglu (2003), namely every fall in the IBSF 2 index, does not necessarily imply that a Islamic banking system is moving into a deep systemic crisis or severe banking crises. Therefore, we differentiate the crisis into three types of fragility value: - Normal banking crisis defined as the IBSF 2 does not deviate significantly from zero. Therefore, there is no reason to expect a severe banking sector problem in the short run, (-0.1>IBSF 2t <0.1 ) - While, an Islamic banking system is supposed to be in a medium fragility period, if the value of the IBSF2 index is between 0 and - 0.5 (-0.5<IBSF 2t <0). - If, however, the value of the IBSF 2 index is lower than -0.5, we assume that the relevant banking sector is highly fragile to systemic crisis (-0.5>IBSF 2t ). Phase 1 Table 11 Changes in the IBSF2 Index and the Five Phases of A Hypothetical Banking Crisis Banks Behavior Excessively risk taking Direction of the Change in the IBSF Index Increases significantly above zero Banking Fragility Banking crisis Falls* (optimistic, or boom Probability of Approaching The probability starts to 22 By using the standardized values of DC and DEP, we try to equalize the variance of the two components, and thus avoid the possibility that any one of two components dominates the IBSF2 index. 20

Phase 2 Phase 3 Phase 4 Phase 5 Generally Risk Avoiding Risk Avoiding Risk avoiding Gradually they start to take risk again Suddenly begin to decrease Falls below zero, but it is still above - 0.5 Falls below - 0.5 Increase towards zero phase) Starts to increase Increases significantly (medium fragility) Continues to increase (high fragility) It falls again (recovery period) increase* It increases the probability of panic crisis System is approaching the borderline to crisis Most probably, a severe crisis occurs in this place Crisis runs away if the IBSF is very close or equal to zero again *Although increases in the BSF index imply a fall in fragility in the short run, it actually must be interpreted as an alarming indicator for impending crisis, if the increase in the index is significant and continues for a while. Hence, the probability of crisis starts to increase in this initial phase, since banks take excessive risks during that period of time. Source: Kibritcioglu (2003) 21

Figure 2 Time Path of the BSF 2 Index and Five Phases of A Hypothetical Banking Crisis Source: Kibritcioglu (2003) BUILDING LEADING INDICATORS OF CRISIS As mentioned earlier that the Indonesian Islamic banking operate side by side with the conventional ones. It implies, a shock which is ascribed from conventional banks might influence and trigger instability towards Islamic banks. Moreover, conventional shares in Indonesian economy yet accounted for a dominant portion instead of Islamic banks. On that reason, incorporating conventional variables as leading indicators crisis could bring useful and informative decision on the onset of crises. This paper adopts from Herrera-Garcia s model which employed four leading indicators, namely: 1. M 2 /Reserve growth; Following the literature (Demirgiic-Kunt 1997) uses this variable to capture the vulnerability of the economy to sudden capital outflows triggered by a run on the currency. Greater M 2 to reserve ratio is expected to raise the likelihood of banking crises. 2. Credit Growth, namely the total of credit growth approved by several banks; Bank Pemerintah (National Banks), Bank Pembangunan Daerah (Regional Development Banks), Bank Swasta Nasional (National Private Banks), Bank Asing (Foreign Banks), and Bank Campuran (Mix 22

Banks). This indicator is relevant to be incorporated because at micro level, fast expansion of loan portfolios may lead to capacity constraints (to manage risks, gather information, or assess quality of applications) starting to bind and new loans being originated without adequate screening and risk management (Berger and Udell, 2004). At the macro level, expansion may involve strategic competition concerns whereby banks take on more risks or financial institutions become more interconnected and the system, as a whole becomes riskier. Hence, rapid credit growth episodes can decrease loan quality, increase systemic risk, and deteriorate bank soundness. 3. Real Effective Exchange Rate is defined as the indicator which is obtained by deflating the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) by a suitable effective deflator. An important refinement of the Real Effective Exchange Rate is particularly useful in considering comparative changes in a country s real economic circumstances. If the REER for a country shows a downward trend (overvaluation), this could be because of other countries are becoming more productive, vice versa. Such condition eventually implies the nominal interest rate in a particular country is higher than other country. Thus, such overvalue real exchange rate might create an increased possibility of occurrence a crisis. REER is obtained by deflating Nominal Exchange Rate with the ratio between standardized 23 consumer price index (CPI) of US over CPI of Indonesia. 4. Inflation rate (there is consistency of this variable as determinants of banking crises according to Demirguc-Kunt and Detragiache 24 ). This paper computes inflation by taking monthly differentiation between current CPI 25 minus previous CPI over previous CPI. Table 12. Conclusion of the Indicator, its Transformation, its frequency data, and the threshold position Indicator Transformation Data Frequency Threshold Position M 2 /Reserve Growth 1 month change Monthly Over zero line 23 Calculated based on a certain base year period 24 Demirguc-Kunt and Enrica Detragiache (1997).Banking Crises around the World: Are there Common Threads?. 25 The inflation refers to 2002 as the base year or standardized based on 2002=100. 23

Credit Growth 1 month Change Monthly Over zero line Real Effective Level Monthly Over zero line Exchange Rate Inflation rate 1 month change Monthly Over zero line The use of above variables actually has been adopted by Susatyo (2002) 26 for Indonesia case. The results show that the variables chosen above are good variables as leading indicators. This can be seen from their low noise to signal ratio. OUT OF SAMPLE ANALYSIS The establishment of out-of-sample performance is to undertake analysis in time-series Early Warning System (EWS) model. Once the paper estimated in-sample analysis by estimating the data over the January 2004-December 2006 window, then the analysis goes to the estimated parameters which use data spanning from January 2007 to June 2012 in order to compute the probability of having crisis. Either IBF 2 or four selected leading indicators utilize data spanning from January 2007 to June 2012. All data are collected from Bank Indonesia and International Financial Statistic. 5. RESEARCH FINDINGS DETERMINES PERIODS OF UNUSUAL ISLAMIC BANKING CRISIS Table 13. Islamic Banking Index and its Degree of Fragility in Indonesia Period IBSF 2 Degree of Fragility Phase March 2004 1.78128 Normal Islamic Banking Crisis 1 April 2.041717 Normal Islamic Banking Crisis 1 May 2.218567 Normal Islamic Banking Crisis 1 June 2.608528 Normal Islamic Banking Crisis 1 July 3.510441 Normal Islamic Banking Crisis 1 August 3.055141 Normal Islamic Banking Crisis 1 26 Using data period 1990-2000 with monthly basis, the study used 14 variables to identify leading indicators. The variables are as follow (respectively from the best performance as leading indicator): real exchange rate, M2/reserves, inflation, real domestic credit growth, international reserves, real interest rate, stock price, ratio of lending rate to deposit rate, commercial bank deposits, ratio of domestic credit to GDP, export, import, and M2 multiplier. 24

September 2.491701 Normal Islamic Banking Crisis 2 October 2.558671 Normal Islamic Banking Crisis 2 November 2.165326 Normal Islamic Banking Crisis 2 December 2.370654 Normal Islamic Banking Crisis 2 January 2005 1.524318 Normal Islamic Banking Crisis 2 February 1.595123 Normal Islamic Banking Crisis 2 March 1.437884 Normal Islamic Banking Crisis 2 April 0.791846 Normal Islamic Banking Crisis 2 May 0.788939 Normal Islamic Banking Crisis 2 June 0.065511 Normal Islamic Banking Crisis 2 July -0.16369 Medium Islamic Banking Crisis 4 August -0.63108 Systemic Islamic Banking Crisis 4 September -1.23032 Systemic Islamic Banking Crisis 4 October -1.38066 Systemic Islamic Banking Crisis 4 November -1.76705 Systemic Islamic Banking Crisis 4 December -1.80312 Systemic Islamic Banking Crisis 4 January 2006-2.05007 Systemic Islamic Banking Crisis 4 February -2.1322 Systemic Islamic Banking Crisis 4 March -2.41531 Systemic Islamic Banking Crisis 4 April -2.19707 Systemic Islamic Banking Crisis 4 May -2.36165 Systemic Islamic Banking Crisis 4 June -2.25624 Systemic Islamic Banking Crisis 4 July -2.19549 Systemic Islamic Banking Crisis 4 August -2.12047 Systemic Islamic Banking Crisis 4 September -1.60344 Systemic Islamic Banking Crisis 4 October -1.5897 Systemic Islamic Banking Crisis 4 November -1.34315 Systemic Islamic Banking Crisis 4 December -1.76493 Systemic Islamic Banking Crisis 4 Source: Author s Calculation 25

Figure 3. Time Path of the IBSF 2 Index and the Phases in Indonesia Source: Author s Calculation The table and figure above apparently describe that the Islamic banking sector fragility (IBSF 2 ) index is easily used to measure and monitor the changes in the banking sector fragility to crisis. Empirically, this type of index is capable of providing more information about the ups and downs in the Islamic banking sector with respect to certain crisis-years in event-based studies. According to above figures, we can interpret that for initial month in 2004, Islamic banking in Indonesia were excessively risk taking. During early seven months in 2004, the direction of IBSF 2 increases significantly above zero, and expected to fall. In fact, since September 2004, IBSF 2 index suddenly begins to decrease and implies Islamic bank generally are starting to risk avoiding. This index continues to decrease and falls below -0.5 since July 2005. Therefore, since July 2005 to December 2006, IBSF 2 index shows some indications for the upcoming of banking crises or systemic banking crises. Thus, the IBSF 2 index proposed here not only captures crisis time in terms of the defined high fragility periods, but also it empirically posits the whole development process of a banking sector problem, even if it is only a significant unsoundness short of a crisis. 26

Domestic Credit Growth NTS (Noise To Signal Ratio) % Of Obs. Correctly Called % Of Crises Correctly Called % Of False Alarms Of Total Alarms SIGNAL-GENERATING MECHANISM AND DETERMINING LEADING INDICATOR OF CRISIS As stated at the chapter 4, this paper employs three signal horizons, namely 24, 18, and 12 months prior crisis. Then, to signify whether leading indicators issue signal or not, some threshold levels are set, namely 3 SD, 1.1 SD, 1.5 SD, and 1.0 SD. The results are shown below Table 14. Results Signal Matric Indicator of M 2 /Reserve Growth M2/Reserve Kaminski Garcia Park Lestano 24 18 12 24 18 12 24 18 12 24 18 12 NTS (Noise To Signal Ratio) % of Obs. Correctly Called % Of Crises Correctly Called % Of False Alarms of Total Alarms % Prob. of Crisis given an Alarm (Pc) % Prob. of Crisis given No Alarm Source: Author s Calculations - - - 3 10 19 6 15 28 11 15.3 17 99.5 99.5 99.5 88.4 85.8 83.1 88.24 84.6 81 86.7 84.5 83.7 0 0 0 56.6 51.1 43.9 60.8 53.5 42.9 64.7 65.4 64.7 - - - 9.1 27.3 45.5 16.2 37.8 59.5 26.7 34.6 37.7 - - - 90.9 72.7 54.5 83.8 62.2 40.5 73.3 65.4 62.3 0.52 0.52 0.52 11.9 12 12 10.9 10.9 10.9 10 10 10 According to some above evaluation criteria, this paper suggests that variable M 2 /reserve growth will strongly expose as a leading indicator for Islamic banking crises if uses the threshold level developed by Garcia with 24 month signal horizon. It is because of such approach can generate the highest percentage in terms of % of observation correctly called and lowest NTS. Table 15.Results Signal Matric Indicator of Domestic Credit Growth Kaminski Garcia Park Lestano 24 18 12 24 18 12 24 18 12 24 18 12 - - - 0 2,1 10,9 1,8 10,2 23 1,7 6 21 98.4 98.4 98.4 94.2 93.7 92.7 90.6 89.2 88.3 91.5 90.5 89.1 0 0 0 29.4 25 14.3 29.4 20.8 13.6 32 26.1 15 100 100 100 0 20 60 0 44.4 66.7 11.1 33.3 66.7 27

% Prob. Of Crisis given an Alarm (Pc) % Prob. Of Crisis given No Alarm 0 0 0 100 80 40 100 55.6 40 88.9 66.7 40 0.52 0.52 0.52 5.97 5.97 5,97 5.97 9.31 9.31 8.42 8.42 8.42 Source: Author s Calculation Based on above table, the domestic credit growth is able to be a good leading indicator if the paper utilizes the threshold level developed by Garcia with 24 month signal horizon. It is because of exposing the lowest NTS compared with others threshold levels and signal horizons. Table 16.Results Signal Matric Indicator of Real Effective Exchange Rate Kaminsky Garcia Park Lestano Real effective Exchange Rate 24 18 12 24 18 12 24 18 12 24 18 12 NTS (Noise To Signal Ratio) - - - 0 5.47 12,.7 6.78 15,3 25.7 6.86 13.5 25.7 % Of Obs. Correctly Called 97.4 97.4 97.4 94.4 91.6 88.7 88.1 84.8 81.7 87.7 85 81.4 % Of Crises Correctly Called 0 0 0 66.7 60 50 60.4 51.3 44.1 60 54.5 44.4 % Of False Alarms Of Total Alarms 100 100 100 0 25 50 19.4 41.2 58.3 18.9 35.1 56.8 % Prob. Of Crisis given an Alarm (Pc) 0 0 0 100 75 50 80.6 58.8 41.7 81.1 64,9 43.2 % Prob. Of Crisis given No Alarm 0.53 0.53 0.53 6.35 6.35 6.35 10.4 10.4 10.4 10.9 10.9 10.9 Source: Author s Calculation The table above shows that the REER indicator is able to act as powerful indicator if utilized the Garcia threshold level and 24 month signal horizon. It is because of exposing the lowest NTS. In addition, it has the lowest % false alarm of total alarm, and highest % probability of crisis given an alarm. Table 17.Results Signal Matric Indicator of Inflation Rate Inflation Rate Kaminsky Garcia Park Lestano 24 18 12 24 18 12 24 18 12 24 18 12 NTS (Noise To Signal Ratio) - - - 0 3.12 12.8 0 8.75 38.5 0 6.01 25.8 % Of Obs. Correctly Called 97.4 97.4 97.4 94.3 88.5 90 91.2 88.5 85.2 90.8 88.5 84.4 % Of Crises Correctly Called 0 0 0 57.1 36.7 36.8 47.2 36.7 17.4 52.4 45.9 28.6 % Of False Alarms Of Total Alarms % Prob. Of Crisis given an Alarm (Pc) % Prob. Of Crisis given No Alarm Source: Author s Calculation 100 100 100 0 35.3 56.2 0 35.3 76.5 0 22.7 63.6 0 0 0 100 64.7 43.7 100 64.7 23.5 100 77.3 36.4 0.53 0.53 0.53 6.18 9.5 6.18 9.5 9.5 9.5 10.2 10.2 10.2 28

Similar with three indicators stated earlier, the inflation rate leads to be a strong indicator if uses Garcia threshold level with 24 month horizon period as it has the lowest NTS, highest percentage of crises correctly called as well as % probability of crisis given an alarm. OUT OF SAMPLE RESULTS As mentioned in previous chapter, the in-sample period will be recapitulated to estimate the predicting ability in out-of sample period. According to the in-sample estimated parameters, we obtain that the extraction signal posits the best predicting ability if utilizes the threshold set by Garcia with 24 month horizon period. Hence, the out-sample analysis will be estimated by these results with four selected leading indicators. No 1 2 3 `Category NTS (Noise To Signal Ratio) % Of Obs. Correctly Called % Prob. Of Crisis given No Alarm Source : Author s Calculation TABLE 18 OUT-OF SAMPLE RESULTS REER(Real Effective Exchange Rate) M2/Reserve Credit Growth Inflation 0 0 0 0 0.8628 0.8633 0.9858 0.7028 0.1330 0.1518 0.1031 0.1522 According to table 18, the out-of sample estimation posit several interesting results, namely : 1. All selected leading indicators are able to minimize the type 1 error 27, which is shown by very small amount of number. It indicates that the model is able to maintain the good signal remains dominant over bad signal. Good signal id defined as the ability of crisis preceded by signal or no persistence crisis due to no signal alarm perform. Therefore, we might justify that the model outperforms for capturing and explaining the existing crises in Islamic banking industry. 2. Based on percentage of observation correctly called, we may deduce that all selected variables are able to explain the incoming crises once the signal issued. All leading indicators inform that in average once the signal was issued, the probability of crisis occurs would be around 80 27 It is a term used to denote the precise of technical terms in statistics to describe particular flaws in attesting process, where a true null hypothesis was incorrectly rejected. 29

percent. These results recommend that all variables are strongly enough to be further implemented in order to identify the onset of crises by observing the behavior of signal issued by those four selected indicators. 3. According to percentage probability of crisis given no alarm, we obtain that in average the model is able to reduce the possibility a crisis occur without signals. This implies that the model is credible and strong to define an onset of crisis in a particular period of time. According to table 17, the possibility crises occurred without crises are around 15 percent. Finally, the out-sample period has informed that the findings supports the use of longer forecast horizons to perform EWS analysis. By considering a forecast horizon of 24 months increases artificially the number of ones observed in the sample, which should improve the quality of the estimation and hence that of the forecasting ability. All in all, the non-parametric EWS model which is including the lagged binary crisis indicator has good forecasting abilities not only in-sample but also out-of-sample. These findings vindicate dynamic EWS model, particularly the extraction signal approach ability for crises anticipation. 6. CONCLUSION AND RECOMMENDATION Conclusion Identification of crisis episode, particularly, for Islamic banks is very essential if we try to predict and explain banking crisis empirically. The IBSF 2 which was developed in this study is able to figure out the development process of Islamic banking crises in Indonesia over March 2004 to December 2006. The results convey that Islamic banking is subject to have high fragility periods in the midst of 2005 until the ends of 2006. IBSF 2 index continues to decrease and fall deeply below zero. Therefore, at least, this empirical evidence can be used as reference for policy makers in evolving management framework in avoiding or minimizing the upcoming onset of crisis, both domestically and globally sourced. In addition, by utilizing signal generating mechanism, the study is able to expose some signals and detect crises phenomena through employing four leading indicators which borrows from Herrera and Garcia study (1999). The results find that four leading indicators could be used as best predictor variables for Islamic Banking crisis if the threshold set is using Garcia approach. It is because, all indicators show the lowest noise to signal ratio (NTS) which mean showing the best threshold chosen to 30

minimize type I error (probability of not anticipating a crisis) and reduce substantially cost of not anticipating the extreme risk situations. Meanwhile, this paper also finds that 24 month signal horizon is the best because it would bring the employed indicators to have best ability for anticipating crises. Finally, by exercising the out-of-sample period and employing Garcia approach, the study deduces that all selected leading indicators vindicate the ability for correctly forecasting the crises occurrence with at least 24 months before. The findings confirm the in-sample results that is in average the model is empirically able to (1) correctly identify in which degree the signals are well-defined for telling the crises occurrence, around 80 percent, (2) numerically minimize the non-occurrence of crises in such a case with no signal issued, around 15 percent, (3) reduce the existence of bad signal over good signal (NTS), shown by zero number during the analysis. All in all, EWS model which applying extraction signal brings good forecasting abilities not only in-sample but also out-of-sample. These findings vindicate dynamic EWS model over period of observation. Recommendation The study is considered to be very preliminary work in the area of EWS for Islamic banking crises. This study attempts to develop an in-sample and outsample model for Islamic banking crises by using four selected leading indicators with various threshold levels and signaling horizons. In the future, EWS is very important as one parts of a surveillance mechanism for monitoring banking system operation, including Islamic banking. Therefore, the call for more leading indicators and more complicated methods or approach is highly welcomed. In the case of conventional banking, for instance, more macro and micro prudential leading indicators are exercised. In terms of methodology, the use of logit/probit model, artificial neural network, and Markov Switching model are empirically conducted as surveillance mechanism. Hopefully, those methods and extended leading indicators can be applied in the context of Islamic banking in Indonesia context or international level. 31

BIBLIOGRAPHY Al-Osaimy, M.H. and A.S. Bamakhramah. An EarlyWarning System for Islamic Banks Performance, J.KAU: Islamic Econ. 17(1):3-14, 2004. Adiningsih, S., D.N. Setiawati, and Sholihah. Early Warning Systems For macroeconomic Vulnerability in Indonesia, Final Report, EADN Regional Project, 2002. Allen, F Gale, D. Optimal Financial Crises. The Journal of Finance, Papers and Proceedings of the Fifty-Eighth Annual Meeting of the American Finance Association, Chicago, Illinois, January 3 5, 1998. (Aug., 1998) Vol. 53, No. 4, 1245 1284, 1998. Allen, F Gale, D. Financial Fragility, Liquidity, and Asset Prices. Journal of the European Economic Association, Vol. 2, No. 6, (Dec. 2004), 1015 1048, 2004b. Barrell, R., E.P. Davis, D. Karim and I. Liadze. Bank Regulation, Property Prices and Early Warning Systems for Banking Crises in OECD Countries, NIESR Discussion Paper No.330, 2010. Brzoza, Michal, Brzezina, Lending Booms in the New EU member States will Euro Adoption Matter, Working Paper series Np.543; ECB, 2005 Bussiere M. and M. Fratzscher. Towards a New Early Warning System of Financial Crises, ECB Working Paper No. 145, 2002. Candelon, Bertrand, et.al. Currency Crises Early Warning Systems: Why They Should be Dynamic. Draft paper Caprio, G., and D. Klingebiel. Bank Insolvencies: Cross-Country Experiences. Washington, DC: The World Bank, Policy Research Working Paper No. 1620, 1996. Cheang, N. Early Warning System for Financial Crises, Draft paper Davis, E.P. and D. Karim Comparing early warning systems for banking crises, Journal of Financial Stability 4:89 120, 2008. 32

Demirgüç-Kunt, A., and E. Detragiache. The Determinants of Banking Crises: Evidence from Developing and Developed Countries. IMF Staff Papers 45 (1): 81-109.. Formerly printed as: International Monetary Fund, Working Paper No. 97/106, 1997, Washington, DC, 1998. Demirguc-Kunt, Asli and Enrica Detragiache. Banking Crises around the World: Are there Common Threads?, 1997. Demirgüç-Kunt, A. and E. Detragiache. Determinants of Banking Crises in Developing and Developed Countries, Staff Papers - International Monetary Fund 45(1):81-109, 1998. Demirgüç-Kunt, A. and E. Detragiache. Determinants of Banking Crises in Developing and Developed Countries, Staff Papers - International Monetary Fund 45(1):81-109, 1998. Diamond, D W. Dybvig, P H. Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy, Vol. 91, No. 3, (Jun. 1983), 401 419, 1983. Eray, Yucel, A review and Bibliografi of Early Warning Model, MPRA Paper No.32893, 2011. García-Herrero A. Banking Crises in Latin America in the 1990s: Lessons from Argentina, Paraguay, and Venezuela. Washington, DC: International Monetary Fund, Working Paper No. 97/140, 1997. Goldsyein, Morris. Presumptive Indicators/Early warning Signals of Vulnerability to Financial Crises in Emerging Market Economies, Mimeo, Institute for International Economics, 1996. Gorton, G. Banking Panics and Business Cycles. Oxford Economic Papers 40, 4 (Dec.), 751 781, 1988. He, Dong. Emergency Liquidity Support Facilities, IMF Working Paper/WP/00/79, 2000. Herrera, Santiago and Conrado Garcia. User s Guide to an Early Warning System for Macroeconomics Vulnerability in Latin American 33

Countries, Paper presented in the XVII Latin American Meeting of the Econometric Society, August, Cancun, 1999. Hoggarth, Glen and Saporta, Victoria. Costs of Banking System Instability: Some Empirical Evidence. June, Financial Stability Review, 2001. Honohan, P. Banking System Failures in Developing and Transition Countries: Diagnosis and Prediction, BIS Working Paper No. 39, Bank for International Settlements, Basle, 1997. Igan, Deniz, and Marcelo Pinheiro. Credit Growth and Bank Soundness : Fast and Furious? IMF WP/11/278, 2011. Kaminsky, G. Currency and Banking Crises: The Early Warnings of Distress. Presented at the IMF Institute course on An Early Warning System for Financial Crises, May 4, 1999, manuscript, 1999. Kaminsky, G.L. Currency and Banking Crises The early warnings of distress, IMF Working Paper WP/99/178, 1999. Kaminsky, G. and C. Reinhart. Banking and Balance-of- Payments Crises: Models and Evidence. Washington, DC: Board of Governors of the Federal Reserve System, manuscript, 1996. Kaminsky, G.L. and C.M. Reinhart. Twin Crises The Causes of Banking and Balance-Of- Payments Problems, The American Economic Review 89(3):473-500, 1999. Kaminsky, G. and C. Reinhart. The Twin Crises: The Causes of Banking and Balance-of-Payments Problems. American Economic Review, 89 (3): 473 500. Formerly published as: Board of Governors of the Federal Reserve System, International Finance Discussion Papers No. 544, 1996, Washington, DC, 1999. Kaminski, Graciela, Saul Lizondo, and Camen M. Reinhart. Leading Indicators of Currency Crises, MNF Staff Papers, Vom.5 No.1, March 1998,pp.1-48, 1998. Kibritcioglu, Aykut. Monitoring Banking Sector Fragility, The Arab Bank Review Vol.5, No.2 October, 2003. 34

Kusuma, Dimas and Farlian Nugroho. Analisis Sistem Peringatan Dini terhadap Krisis Nilai Mata Uang di Indonesia, Paper Presented in Financial Stability Forum 2008, Bank of Indonesia, 2008. Lestano, Jan Jacobs, and Gerard H. Kuper. Indicators Of Financial Crises do Work!: An Early Warning System for Six Asian Countries, CCSO Working Paper 13, Department Of Economics, University Of Groningen, Netherland, 2003. Majid, Shabri and Salina H. Kassim.. Assessing Contribution of Islamic Finance to Economic Growth : Empirical Evidence from Malaysia, 2010. Park, Won-Am. An Indicators on the Sustainable Development Of Korea, Paper prepared for EADN meeting, Singapore, June 25-27, 2001. Terrones, M, Mendoza, E. Are Credit Booms in Emerging Markets a Concern?, in World Economic Outlook, IMF, Washington, 2004. Topi, Jukka. Bank Runs, Liquidity, and Credit Risk, Discussion Papers : Bank of Finland Research, 2008. Von Thadden, E-L. Intermediated versus Direct Investment: Optimal Liquidity Provision and Dynamic Incentive Compatibility. Journal of Financial Intermediation, 7, 177 197, 1998. 35