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1 Systemic Risk Cycle: Evidence from ASEAN-5 2 3 Febrio Kacaribu1 4 Denny Irawan2 5 6 Abstract 7 8 We examine the cyclicality of systemic risk under the influence of business cycle dynamics by 9 using quarterly data of 73 listed in ASEAN-5 countries. The data spans from 2001Q1 to 10 2017Q2. We use Systemic Risk Index (SRISK) by Brownlees and Engle (2017), which represents 11 the capital shortfall of a in the time of a crisis, as the measure of systemic risk. We find 12 evidence for a countercyclical relationship between SRISK and the business cycle. There is a 13 strong evidence that the leverage ratio has a negative impact on the systemic risk dynamics. 14 15 Key words: Business Cycle, Banks Leverage, Macroprudential Policy 16 17 JEL classification numbers: E32, G21, E58, G28

1 Department of Economics, Universitas 2 Arndt-Corden Department of Economics, The Australian National University 2

18 1. Introduction 19 The importance of systemic risk in the financial system has attracted a lot of attention since 20 the financial crisis of 2008. In the sphere of the macro-finance strand of literature, the crisis 21 has stimulated many studies to explain the new center of attention in the banking risk, the 22 systemic risk. By definition, systemic risk can be inferred as a potential of a financial institution 23 to be undercapitalized once the financial system is in the crisis (Engle et al, 2014). 24 Series of studies have been conducted to formulate measurement to quantify the 25 systemic risk. Up to recently, there are three most popular systemic measurements in the 26 literatures, they are: (i) Marginal Expected Shortfall (MES) by Acharya et al (2016), which is 27 based on Acharya et al (2012), (ii) Systemic Risk Index (SRISK) Measure by Brownlees and 28 Engle (2017), which is based on MES, and (iii) CoVaR or Conditional Value at Risk developed 29 by Adrian and Brunnermeier (2016). In addition to that, Lopez-Espinosa et al (2012) proposed 30 an extended version of CoVaR to be able to capture asymmetric response of the banks toward 31 shocks. Zedda and Cannas (2017) attempted to develop a systemic risk measurement with 32 Leave-One-Out (LOO) approach, which measure how system-wide risk would change if one 33 financial institution is excluded. Meanwhile, Varotto and Zhao (2018) developed a hybrid 34 systemic risk measurement called as “rSYR”. These studies, however, only focus on how to 35 define and measure systemic risk. Meanwhile, the world financial system is hardly to be free 36 from the imminent threat of the systemic risk. Thus, along the process of refining ways to 37 define and measure systemic risk, it is also important to put effort to explain – and then 38 control – the behavior of systemic risk. This condition motivates our study to explain the main 39 determinant of the systemic risk. In this study, we focus our analysis on the ASEAN-5 market. 40 We borrow a theoretical framework from Adrian and Shin (2013). The framework has 41 provided insight on two most important determinants of the probability of default, which are 42 leverage and the business cycle. Leverage is an aspect which can be controlled by banks. 43 Although leveraging is an inherent part of banking activities, many financial crises have shown 44 that excessive leverage by the banks could deteriorate the banks’ financial soundness and, 45 thus, put the financial system in danger. BASEL III provides clear guideline for bank supervisors 46 and regulators across the globe to ensure the banks’ leveraging activities to be at the utmost 47 prudence (BIS, 2017). The business cycle is impossible to be controlled by the banks. It is an 48 inherent macroeconomic feature of modern economy. This fact thus provides a strong ground 3

49 for this study to put attention toward how systemic risk could be influenced by the fluctuation 50 of the business cycle. 51 The business cycle is defined as the fluctuation of real aggregate economic activity 52 (Burns and Mitchell (1946); in Jacobs (1998)). At practical level, the business cycle is 53 represented in many literatures as the fluctuation of the gross domestic product (GDP). 54 However, as the GDP is measured quarterly, many studies also implement the Industrial 55 Production Index (IPI) as an alternative representation of the business cycle. Departing from 56 the definition of the business cycle and systemic risk, we define cyclicality of systemic risk as 57 the behavior of systemic risk along the business cycle fluctuation. This definition has specific 58 concern on how systemic risk may build up as compounded by the dynamics of the business 59 cycle. 60 This study attempts to examine the cyclicality of systemic risk toward the business and 61 financial cycle fluctuation. Several works have been done to address this problem. Liu (2016) 62 examines the dynamics of CoVaR of US Bank-Holding-Company (BHCs) using regime switching 63 approach. The result shows that CoVaR is pro-cyclical toward macroeconomic variables. In 64 line with that, Lenoci (2017) also finds that SRISK is pro-cyclical by examining US BHC data. 65 Tasca and Battiston (2011) also exhibit procyclicality phenomenon by simulating the 66 cyclicality of Value-at-Risk in hypothetical setting. 67 Several studies outline the role of deposit insurance in explaining the dynamics of 68 systemic risk. Calomiris and Jaremski (2016) point out the role of deposit insurance in 69 increasing banks’ risk-taking tendency and thus positively contribute toward increased 70 systemic risk. In support for this study, Acharya et al (2011) argued that deposit insurance 71 premium setting should be carefully considered so it could play role as a control tool to reduce 72 the banks’ moral hazard. Liu and Staum (2010) strongly argue that systemic risk components 73 of a bank should be considered when calculating the deposit insurance premium. An empirical 74 study by Kusairi et al (2018) exhibits that implementation deposit insurance scheme in ASEAN 75 countries induces banks to take more risk. 76 Our study offers three contributions. First, the base measurement employed in this 77 study is based on market information, i.e. the nature of volatility of the stock market through 78 the different economic cycle. Second, in the context of macroprudential management, this 79 study addresses one of the most important questions on how to make the financial system 80 more resilient. By examining the cyclicality of systemic risk, we can understand its dynamics 4

81 over-time and find the determinants of its cyclicality. Third, this study explores the role, if 82 any, of the deposit insurance scheme in managing the systemic risk. Most ASEAN countries in 83 our dataset have only implemented deposit insurance scheme after 2005, except for The 84 Philippines which have implemented such scheme since 1963. 85 This study is also one of the very few to address systemic risk issue in developing 86 countries, especially in South-East Asia. Most studies observe systemic risk development in 87 the developed countries, which are mostly characterized by relatively deep and mature 88 financial market. The South-East Asian financial market is, with some exception for Singapore, 89 characterized as developing and shallow financial market. 90 On the choice of the proxy for the systemic risk we choose SRISK, which is based on 91 LRMES. SRISK is defined as capital shortfall that a bank will be expected to lose on the 92 condition that the market is in crisis. 93 This paper is organized as follows. Section 1 provides introduction explaining the 94 contextual ground of the study. Section 2 provides brief of the literature ground related to 95 the study. Section 3 gives overview of ASEAN-5 banking and financial system. Section 4 96 explains our basic theoretical framework of how the business and financial cycles may 97 influence the systemic risk in the banks. Section 5 explains our dataset and empirical 98 estimation setting. Section 6 articulates the results of our study and discussion of some 99 impact toward the literature development and the practice the macroprudential 100 management. Section 7 provides conclusion and recommendation. 101 102 2. Literature Review 103 The systemic risk indexes that are involved here, LRMES and SRISK, are based on market 104 information, especially the fluctuation of the company price in term of its market value. The 105 spirit comes from the fact that recent financial crises are mostly caused by the sudden fall of 106 asset price in such a short period. The deterioration of asset price, as standard business cycle 107 theory mentions, is a strong symptom that the economy is in the downturn phase. 108 The work of Schwert (1989) is one of initial attempts to relate macroeconomic 109 fundamentals with asset price. In his work, he models the stock price as the discounted 110 present value of expected future cash flow to stockholders. On the aggregate level, the value 111 of corporate equity depends on the health of the economy. Thus, the uncertainty of future 112 macroeconomic conditions would cause a proportional change in stock return volatility. This 5

113 framework is still in place until recently, as followed by Engle (2014), Subrahmanyam and 114 Titman (2013), and Hamilton and Lin (1996), David (1997) and Veronesi (1999). 115 In the same spirit, Bansal and Yaron (2004) develop Dynamic Capital Asset Pricing 116 Model (DCAPM) to relate macroeconomic condition with asset price volatility, based on the 117 dynamic consumption preference model developed by Epstein and Zin (1989). The model 118 incorporates changes in the conditional volatility of future growth rates to allow time-varying 119 risk premia. The business cycle is represented by the conditional volatility of consumption 120 which directly affects the price-dividend ratio; a rise in economic uncertainty leads to a fall in 121 asset prices. The financial market dislikes economic uncertainty and better long-run growth 122 prospects raises current equity prices. In line with this model are the studies from Ozogus 123 (2009) and Bansal et al (2014). 124 Acharya et al (2012) summarized the development on systemic risk measures, 125 especially measurements which are based on market information, the bank’s stock returns. 126 They underline three most popular systemic risk measurements recently. Those three 127 measurements are: (i) Marginal Expected Shortfall (MES) from Acharya et al (2016), which is 128 based on Acharya et al (2012); (ii) SRISK measure by Brownlees and Engle (2017), which is 129 based on MES; and (iii) CoVaR or Conditional Value at Risk developed by Adrian and 130 Brunnermeier (2016). 131 Those measures are based on two bank-level risk measures, Value-at-Risk (VaR), and 132 Expected Shortfall (ES). Value-at-risk is described as the most value a bank will lose with the

133 confidence level of 1 − 훼, or formally (푅 < −푉푎푅훼) = 훼. Meanwhile, Expected Shortfall (ES) 134 is the average of the worst 훼-percent return of the left-tail of the bank return distribution. 135 Since 훼 is usually similar both for VaR and ES, then ES can also be interpreted as the expected

136 loss conditional on the loss being greater than the VaR, or 퐸푆훼 = −퐸[푅|푅 ≤ 푉푎푅훼]. 137 Marginal Expected Shortfall (MES), as formally documented on Acharya et al (2016), 138 measures the ES contribution of one bank to total system’s ES when the system is on its worst

휕퐸푆훼 139 훼-percent. It is defined as 푀퐸푆훼 = = −퐸[푟푖|푅 ≤ 푉푎푅훼]. The term 퐸푆훼 stands for 휕푦푖

140 overall Expected Shortfall with the default probability of 훼. Meanwhile, 푦푖 stands for weight 141 of the group. VaR, ES, and MES are commonly computed based on the distribution of daily 142 return during a certain time period. 6

143 Brownlees and Engle (2017) developed a time-varying version of MES, called as Long- 144 Run Marginal Expected Shortfall (LRMES), i.e. by rolling the time-periods when calculating the 145 MES. LRMES is then used to predict SRISK. The step-by-step process of LRMES and SRISK 146 calculation is presented in section 3 and also discussed in detail in Appendix A. 147 There are several literatures that discuss risks in the banking system in ASEAN region. 148 Hamilton et al (2015) measures systemic risk of 201 financial institutions in ASEAN-5 area 149 using Moody’s Analytics Expected Default Frequency (EDF). They use Granger causality to see 150 pairwise connection between each bank in their sample. Ayomi and Hermanto (2013) 151 examined insolvency risk of 30 Indonesian banks with Merton model to identify the 152 probability of default. In addition to that, they also examined the role financial linkage 153 between banks to measure how systemically important each bank compared to the system- 154 wide risk using CoVaR. Manap (2019) measured systemic risk of six listed banks in Malaysia 155 using CoVaR method during 2005-2017. However, since the sample size is too small, it is hard 156 to find general conclusion about the systemic behavior in their study. Meanwhile, Irawan and 157 Kacaribu (2017) examined cyclical relationship between business cycle, financial cycle, and 158 risk cycle inside 150 Indonesian banks during 2005 to 2014. They observe that financial cycle 159 and risk cycle precede the business cycle. 160 161 3. Banking System in ASEAN-5: An Overview 162 Banking system keeps playing important role in the ASEAN-5 countries. Figure 1 give an 163 illustration on the size of banking system compared to total size of the financial system, which 164 is comprised of (i) banking system; (ii) stock market; and (iii) bond market. In general, the 165 banking systems of ASEAN-5 countries in our observation have assets size of about 40-50 166 percent of the total size of the financial system in the recent years. 167 [Figure 1] 168 The business cycle seems to have some influence on the banking system performance in 169 the five countries in our dataset. Figure 2 plotted the countries’ banking system 170 nonperforming loan (NPL) against GDP growth during the period of 2001-2016. The scatter 171 plot shows negative relationship between the NPL and the business cycle. As the economy 172 grows, the share of bad loans (NPL) gets lower. 173 [Figure 2] 7

174 In terms of Capital Adequacy Ratio (CAR), most ASEAN-5 countries banking system is 175 characterized as well-capitalized. This could be inferred from aggregate CAR for each country 176 as described in Figure 3. All five countries could maintain their aggregate CAR around 15 177 percent and above, which is higher than BASEL III minimum requirement of 10.5 percent. 178 [Figure 3] 179 Singapore maintain its CAR at a steady level of 16 percent during 2013-2017. 180 Meanwhile, The Philippines has been experiencing a gradual decline of CAR level from almost 181 20 percent in May 2013 to 15 percent by the end of 2017. On the opposite, Indonesia 182 exhibited an increasing trend, with the CAR at almost 24 percent by the end of 2017. 183 The levels of financial depth in ASEAN-5 countries are quite similar except Thailand. 184 Figure 4 presents the value of stock market transaction as compared to the size of GDP of 185 each country. The Philippines’ is the lowest with the average total amount of transaction 186 below 20 percent of GDP during 2001-2017. Thailand is the country with the most liquid stock 187 market compared to peers with total value of transaction accounted more than 80 percent of 188 GDP on average. Both Malaysia and Indonesia are relatively stable at around 20-40 percent. 189 In general, stock market transaction in ASEAN-5 is showing a declining trend since the global 190 financial crisis 2008. 191 [Figure 4] 192 193 4. Model 194 4.1. Conceptual Framework 195 We borrow conceptual framework developed by Adrian and Shin (2013). They develop a 196 framework to measure the default probability on a certain confidence level 훼, based on the 197 Extreme Value Theorem (EVT). They show that the default probability is determined by two 198 variables: the leverage and the business cycle condition. 199 The probability of default in 훼 is determined by the fundamentals of a contracting 200 problem. The basic of contracting problem developed in their paper ranges from the expected

푧−휃 201 pay-off of high-return investment 퐹 (푧) = exp { − 1} to those of low-return investment 퐻 휎 푧−(휃−푐) 202 퐹 (푧) = exp { − 1}. The term 푧, which follows a Second Order Stochastic Dominance 퐿 휎 203 process, refers to the certain time the pay-off will occur. Meanwhile, 휃 refers to the business 204 cycle; it goes higher when the business cycle is in an expansion phase. The term 휎 refers to 8

푘 퐹퐿(푧;휃) 205 the standard deviation, while c is a constant. The ratio between the returns is = 푒휎 > 퐹퐻(푧;휃) 206 1, where 푘 is a constant.

207 By summing up the 퐹퐻(푧) and 퐹퐿(푧), the payoff of the investment can be obtained as 208 follows: 푧 209 ( ) ( ) ( ) 훥휋 푧; 휃 = ∫−∞(퐹퐿 푠; 휃 − 퐹퐻 푠; 휃 )푑푠 (1)

푘 푧 210 휎 ( ) = (푒 − 1) ∫−∞ 퐹퐻 푠; 휃 푑푠

푘 211 = (푒휎 − 1) 푥 휎퐹퐻(푠; 휃)

212 where 푠 stands for the second order derivative of the option price with respect to the strike 213 price evaluated at 푧. Restating the equation into an Indifference Curve constraint form will 214 result in

푘 ∗ 215 (푒휎 − 1) 푥 휎퐹퐻(푑 ; 휃) = 푟퐻 − 푟퐿 (2)

216 where 푑∗ is the debt ratio, and modification on the pay-off equation will result in

∗ 푟퐻−푟퐿 217 훼(휃) = 퐹퐻(푑 ; 휃) = 푘 (3) 휎(푒휎−1)

218 219 Substituting this equation into the probability of default 훼(휃) definition yields the following 220 equation

푑∗−휃 푟 −푟 221 훼(휃) = 퐹 (푑∗; 휃) = exp { − 1} = 퐻 퐿 (4) 퐻 휎 푘 휎(푒휎−1)

222 223 Equation (4) indicates that the probability of default, 훼, is a positive function of debt ratio, 푑∗, 224 and is a negative function of business cycle, 휃. 225 226 4.2. Systemic Risk Measures: LRMES and SRISK

227 As in Acharya et al (2012) and Brownlees and Engle (2017), 푆푅퐼푆퐾푖,푡, capital shortfall that a 228 bank expects to experience by bank i at period t if a crisis happens, is defined as 229

230 푆푅퐼푆퐾푖,푡 = 퐸푡−1(퐶푎푝𝑖푡푎푙 푆ℎ표푟푡푓푎푙푙푖|퐶푟𝑖푠𝑖푠) (5) 231 9

232 We calculate SRISK manually based on daily stock returns of each bank in our sample. We 233 follow computation steps as presented by New York University (NYU) Volatility Lab (2017). At 234 the operational level, there are several versions of SRISK. We adopt the global dynamic 235 version due to its suitability for a cross-country SRISK analysis, which makes the result of SRISK 236 of the different banks in different countries comparable to each other. The global version of 237 SRISK calculation is based on the Global Dynamic MES and the Dynamic Conditional Beta 238 (DCB) as documented in Engle (2016). 239 The LRMES of bank i at the time t is defined as 240

log(1−푑)∗훽푖,푡 241 퐿푅푀퐸푆푖,푡 = 1 − 푒 (6) 242 243 where 푑 is the six-month crisis threshold for a market decline; set at 40 percent as per the

244 common practice. Meanwhile 훽푖,푡 is the DCB. To compute the LRMES, again we follow the the 245 steps as outlined by NYU Volatility Lab (2017). 246 Finally, after obtaining the LRMES, we can compute the SRISK. SRISK is simply the 247 expected capital the government needs to inject to a bank if the bank is about to default. If 248 we sum up all the individual SRISK in the system, we will get the total expected capital shortfall 249 of the financial system, which means the total capital injection needed to be provided by the 250 government to bail-out the system. SRISK can be obtained by: 251

252 푆푅퐼푆퐾푖,푡 = 푘. 퐷퐸퐵푇푖,푡 − (1 − 푘). 퐸푄푈퐼푇푌푖,푡. (1 − 퐿푅푀퐸푆푖,푡) (7) 253 254 where k stands for the capital requirement. In this study, we use k = 8%, following the 255 standard capital requirement level as in Basel requirement and applied by all countries in the 256 observation. LRMES is the Long-Run Marginal Expected Shortfall, EQUITY is the current 257 market capitalization of the bank at t, while DEBT is the total liability of the bank. 258 259 5. Data and Methodology 260 5.1. Data 261 All countries in our dataset are the developing economies, except for Singapore. All of 262 these countries are relatively small compared to the size of the world economy, with 10

263 Indonesia as the largest economy in our sample. These countries are in general sensitive 264 toward fluctuation of the world economy. Therefore, we believe it is important to represent 265 influence of the world business cycle in our analysis. Thus, in addition to the home country 266 business cycle, which is represented by each country annual GDP growth, we also take into 267 account the world business cycle, which is represented by G20 annual GDP growth. 268 269 [Table 1] 270 271 We compute SRISK of the listed banks in ASEAN-5 countries for 2001:Q1 to 2017:Q2. 272 We choose this span of time to make our analysis focused on the period after the Asian 273 financial crisis of 1998. Our dataset covers around 70-100% of the total assets of the banking 274 system for each country (see Table 1). The Dynamic Conditional Beta (DCB) is calculated 275 quarterly at the end of each quarter over time. See Appendix A for a more detailed discussion 276 on how we calculate LRMES and SRISK. 277 278 [Table 2] 279 280 In addition to the business cycle and leverage ratio, we use several control variables, 281 both at bank-level and country-level, following some variables used in Weiß et al. (2014); Quin 282 and Zhou (2018); and Varotto and Zhao (2018). At bank level, we use deposit, return on asset 283 (ROA), and loan loss provision (LLP). We use LLP instead of Nonperforming Loan (NPL) as bank- 284 level risk proxy due to data limitation. At country-level, we employ inflation, deposit 285 insurance presence, money market rate, and exchange rate. 286 287 [Table 3] 288 289 We also use deposit insurance dummy as another control variable. Most countries in 290 our dataset have implemented deposit insurance scheme since 2005. Indonesia adopted it in 291 in 2005, followed by Malaysia (2005), Singapore (2006), and Thailand (2008). The Philippines 292 is the only exception, which started in 1963. The dummy variable is set to be 1 whenever a 293 country has a deposit insurance scheme in place and 0 if otherwise. Because Philippines have 294 already implemented the scheme far before the observation period of our dataset, they have 11

295 constant value of 1 in for deposit insurance dummy for the whole time period of the sample. 296 Hence, we cannot analyze the impact of the adoption when we conduct country level 297 estimation for Philippines. 298 All returns, LRMES, SRISK, and any other value variables in this study are based on local 299 currency. Nevertheless, all of the variables are going to be in terms of growth or ratios; it is 300 safe to compare them across countries. We also add inflation rate and depreciation rate of 301 local currencies against US Dollar as additional control variables. 302 The SRISK applied in this research is a market-based measure. Since the stock market 303 in developing countries are characterized as shallow, there may be some caveats of the 304 application of a market-based measurement. If the turnover of the stocks of bank in the 305 dataset is substantially high, the index might reveal its suitability to explain the dynamics. One 306 example is to examine the stock turnover volume. 307 308 [Figure 5] 309 310 Figure 5 provides overview of the turnover ratio for each stock quarterly. The turnover 311 ratio is counted as the total number of volume transaction compared to the total stock 312 available. The turnover ratio on average is 7.6 percent along the sample period. 313 314 [ 315 Figure 6] 316 317 318 We also employ a dummy for crisis in our study. 319 Figure 6 represents the volatility index (VIX) for the sample period. The shaded area shows 320 the period of high volatility, which spans from 2008q1 to 2011q4. This period marks the 321 definition of crisis period in this study. The period is employed as the basis of the crisis dummy 322 variable. This period covers the 2008 financial crisis up until the burst of commodity boom 323 and Eurozone bond crisis in the early 2012. 324 325 5.2. Empirical Model 12

326 Equation (4) outlines the role of business cycle and leverage in affecting the default 327 probability. We use SRISK as the proxy for the default probability in this context. Meanwhile, 328 the main explanatory variables in focus are GDP growth and leverage ratio. 329 We implement two styles of estimation. The first one is Feasible Generalized Least 330 Square (FGLS). This model is selected due to the data characteristics. As a comparison, this 331 study also provides analysis with fixed-effect OLS with robust error specification. The 332 fundamental reason of application of FGLS method comes from the consideration of 333 heteroskedasticity and autocorrelation. The dataset in this study comprises multiple banks 334 across five countries. Furthermore, many variables employed in the analysis are denominated 335 in local currencies. Thus, the dataset employed in this study is by nature very prone toward 336 heteroskedasticity problem. The FGLS estimation, as outlined by Cameron and Trivedi (2009), 337 will provide efficient and consistent estimation. Moreover, Baltagi (2005) and Hsiao (2003) 338 outlined the ability of FGLS style estimation to handle the autocorrelation problem observed 339 in the dataset. 340 341 [

342 Table 4] 343 344 The result of heteroskedasticity and autocorrelation for the equation (8) and (9) are 345 provided in

346 Table 4. We implement standard Breusch-Pagan test for heteroskedasticity. For panel 347 autocorrelation test, we implement panel autocorrelation test from Drucker (2003). We 348 exhibit autocorrelation and stationarity in equation (8) and (9). We also exhibit 349 heteroskedasticity in both equations. This result is the ground of our preference toward FGLS 350 estimation. However, we provide estimation results with both FGLS and OLS FE and use both 351 results in support for each other. 352 In the estimation process, all banks in one country are brought in as one-panel 353 estimation at ASEAN level, and at each country. Thus, there are in total six panels in the 354 model, following the total of five countries. Those panel are: (1) ASEAN; (2) Indonesia; (3) 355 Malaysia; (4) The Philippines; (5) Singapore; and (6) Thailand. The following is our estimation 356 equation for SRISK: 357 13

358 퐿푅푀퐸푆푖푗,푡 = 훼 + 훽1푊퐺퐷푃푡−1 + 훽2퐻퐺퐷푃푗,푡−1 + 훽3퐼푁푆푅푗,푡 + 훽4퐼푁퐹퐿푗,푡−1 +

359 훽5푀푀푅퐴푇퐸푗,푡−1 + 훽6퐸푋푅퐴푇퐸푗,푡−1 + 훽7퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 + 훽8퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 +

360 훽9퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 + 훽10퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 + 훽11퐿퐸푉푅푖,푗,푡−1 + 훽12퐷퐸푃푖,푗,푡−1 +

361 훽13푅푂퐴푖,푗,푡−1 + 훽14퐿퐿푃푖,푗,푡−1 + 훽15퐶푅퐼푆푖,푗,푡 + 훽16푇퐼푀퐸푖,푗,푡 + 휀푖푗,푡 (8) 362

363 푆푅퐼푆퐾푖푗,푡 = 훼 + 훽1푊퐺퐷푃푡−1 + 훽2퐻퐺퐷푃푗,푡−1 + 훽3퐼푁푆푅푗,푡 + 훽4퐼푁퐹퐿푗,푡−1 +

364 훽5푀푀푅퐴푇퐸푗,푡−1 + 훽6퐸푋푅퐴푇퐸푗,푡−1 + 훽7퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 + 훽8퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 +

365 훽9퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 + 훽10퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 + 훽11퐿퐸푉푅푖,푗,푡−1 + 훽12퐷퐸푃푖,푗,푡−1 +

366 훽13푅푂퐴푖,푗,푡−1 + 훽14퐿퐿푃푖,푗,푡−1 + 훽15퐶푅퐼푆푖,푗,푡 + 훽16푇퐼푀퐸푖,푗,푡 + 휀푖푗,푡 (9) 367 368 We estimated both equations (8) and (9) for each panel. The first estimation puts

369 퐿푅푀퐸푆푖푗,푡 of bank i, in country j, at time t, as the dependent variable. This equation

370 specifically observes the cyclical behavior of LRMES. The second equation puts 푆푅퐼푆퐾푖푗,푡, in 371 delta of log-transformed, as the dependent variable. This equation observes the cyclicality 372 nature of SRISK toward dynamics of business cycle and liabilities. 373 In our estimation, the business cycle is represented by two variables, the world annual 374 GDP growth (푊퐺퐷푃) and home-country annual GDP growth (퐻퐺퐷푃). Due to data limitation, 375 we use annual GDP growth of total G-20 countries as a proxy of the world annual GDP growth. 376 We believe relying only on the home country GDP cannot fully observe the dynamics of the 377 business cycle, since economic size of countries in our observations are relatively small 378 compared to world GDP, and all these economies are highly connected with the world 379 economy. The predicted sign of the business cycle variable is negative. The framework argues 380 that the probability of default is countercyclical toward the business cycle. When the 381 economy is in good condition, the systemic risk of the banks will be lower.

382 Our second focus in this study is on how the leverage ratio (퐿퐸푉푅푖,푗,푡−1), total 383 liabilities divided by total book equity of the bank, influences the systemic risk. As pointed out 384 in BASEL III leverage framework, excessive leverage inside banks is one of the main sources 385 of financial instability. 386 The term 퐼푁퐹퐿 is country inflation rate. The term 퐼푁푆푅 is a dummy variable which represents the 387 presence of deposit insurance for each country. The term 푀푀푅퐴푇퐸 is money market rate, which 388 represents interest rate in our estimation. We choose money market rate to represent interest rate 389 because, in panic situation, we believe banks will rely on money market to find cushion fund to 390 stabilize their balance sheet. The term 퐸푋푅퐴푇퐸 is country exchange rate, in year-on-year (YOY) 14

391 growth form. We also use a dummy variable 퐶푅퐼푆, which represents crisis period. We define crisis 392 period to be between 2008Q1 to 2011Q4, based on the dynamics of global volatility index (VIX) 393 presented in 394 Figure 6. 395 To examine whether country-specific economic characteristics play a role in explaining 396 the cyclicality of the banks’ systemic risk, we use three interaction variables in our estimation, 397 which are interactions between 퐻퐺퐷푃 with 퐼푁퐹퐿, 퐼푁푆푅, 푀푀푅퐴푇퐸, and 퐸푋푅퐴푇퐸. 398 We also have some bank-specific control variables, to control for banks heterogeneity. 399 The term 퐷퐸푃 stands for deposit, which is normalized by dividing with the total assets. The 400 term 푅푂퐴, which stands for return-on-assets, represents the banks’ performance. Term 퐿퐿푃 401 stands for loan loss provision fund, which is normalized by dividing with the total assets. This 402 variable represents risk level inside the bank. Table 5 provides the summary statistics for the 403 explanatory variables. 404 405 [ 406 Table 5] 407 408 6. Result 409 6.1. LRMES and SRISK 410 Table 6 presents summary statistics of LRMES and SRISK calculation. The first panel presents 411 LRMES results. LRMES shows the share of a bank in the system’s total expected shortfall for 412 a given time period. At ASEAN level, the overall LRMES of the banks in our dataset ranging 413 from -120 to 37 percent. Indonesia, with the biggest number of banks in the sample, has 414 LRMES ranging from -38 to 36 percent. Compared to other countries, Indonesia’s LRMES is 415 the most volatile with a standard deviation of 12 percent. In the second position is The 416 Philippines, with a standard deviation of 9 percent. Malaysia, Singapore, and Thailand are 417 significantly more stable. 418 SRISK, as the main concern of this study, is represented by the amount of capital 419 shortfall an institution bears if an extreme event occurs. In this study, the SRISK amount is in 420 local currency in order to avoid currency bias in the analysis. As in Engle (2016), the SRISK 421 value is represented in a positive value and any capital surplus is regarded as zero SRISK value. 422 We do not include this zero SRISK value in our estimations. 423 15

424 [Table 6] 425 426 To be able to compare SRISK across country and institution, we construct new simple 427 measurement, that is SRISK-to-Equity ratio. This is a ratio between the value of SRISK with the 428 book value of equity of the bank. SRISK is calculated using the market information, in which 429 the market value of equity is one of main drivers of the information. The SRISK-to-Equity ratio 430 can give us a description of how bad a crisis – and systemic risk – can deteriorate a single 431 institution both in term of book and market capitalization. In general, at ASEAN level, SRISK- 432 to-Equity ratio has average of 25 percent. This number can be interpreted that on average 433 when crisis happens, most banks in ASEAN countries may lose about 25 percent of its book 434 value, with standard deviation of 18.6 percent. 435 By looking at Table 6, we can deduce that in our sample, on average, 87 percent3 banks 436 in The Philippines are having non-zero SRISK; this is the highest among the five countries. Next 437 on the ranking are Indonesia and Singapore with 63 and 39 percent respectively. The last are 438 Thailand and Malaysia with only 31 and 19 percent respectively. Interestingly, the relative 439 size of SRISK with respect to equity on average for The Philippines and Indonesia are also high 440 at 26 to 28 percent. Thailand, with relatively low number of banks having non-zero SRISK, has 441 a relatively lower size of SRISK per bank at 13 percent. As for Malaysia, the SRISK-to-Equity 442 ratio seems high at 31 percent. Meanwhile, Singapore is the lowest with the SRISK-to-Equity 443 ratio at 6 percent. 444 445 [Figure 7] 446 447 448 Figure 7 shows the scatter plot of LRMES over-time by country. The distribution of 449 LRMES tends to be concentrated. It is quite normal as LRMES value is solely based on market 450 information. Again, it simply represents fractional loss contribution toward the total market 451 Expected Shortfall. There seems to be a slight trend for the LRMES to be less scattered. 452 453 [Figure 8] 454

3 Divide the number of observations for SRISK by number of observations for LRMES for The Philippines to get 87 percent. 16

455 Figure 8, on the other hand, represents the scatter plot of SRISK over-time by country. 456 Again, SRISK is presented in a positive value, as capital surpluses are represented as zero 457 SRISK. In line with our discussion above, banks in The Philippines and Indonesia are showing 458 a growing trend of systemic risks, in particular after the Global Financial Crisis 2008. As for 459 Singapore, the high size of SRISK seems to be dominated by the spike during the period right 460 after the GFC. After that, the systemic risk in Singaporean banks seems to be relatively very 461 low compared to others. As for Thailand, although it seems to be trending up, the relative size 462 of its SRISK is significantly lower than its peers. 463 For the case of Malaysia and Singapore, the pattern seems to be different. SRISK 464 distributions for these two countries tend to be unstable at the beginning and end of the 465 observation window. For the case of Singapore, SRISK also sprawled after the crisis period, 466 especially in 2009 and 2010. 467 As before, the SRISK-to-Equity ratio can give us a more complete picture of the 468 dynamics of SRISK as given by Figure 9. We can see that the most actions happen in The 469 Philippines, Indonesia, and Singapore. Systemic risk in Thailand looks relatively much more 470 benign. Malaysia, on the other hand, is showing a clear trend of stabilizing with increasingly 471 lower systemic risk. 472 473 [Figure 9] 474 475 The diverse pattern of systemic risk development for each country might be caused 476 by some factors. The stock market relative liquidity could play a role. As a market-based 477 measure, LRMES and SRISK are highly related to the dynamics of the stock market in each 478 country. The Philippines, Indonesia, and Singapore are where the size of the stock market 479 transaction is very low relative to its economy, i.e. the least active. The stock market in 480 Thailand and Malaysia are relatively more active and liquid. See Figure 8. Additionally, the 481 size of the banking sector relative to the financial market in each country could affect the 482 dynamics of their banking system’s SRISK. By looking at Figure 1, while there is a common 483 trend for less role of banks in the financial system overall, The Philippines, Indonesia, and 484 Singapore are having the lowest banking sector shares compared to Thailand and Malaysia. 485 486 6.2. Systemic Risk Cycle 17

487 We conduct estimation in two settings. The first estimation focuses on how business cycle 488 and leverage ratio could explain the dynamics of LRMES. Meanwhile, the second estimation 489 focuses on SRISK. Our estimation results are presented in Table 7 and Table 8. Both LRMES 490 and SRISK estimation are conducted with two specification: (1) FGLS estimation with 491 heteroskedasticity option; and (2) OLS fixed effect with robust error option (OLS FE). The 492 estimations are conducted for six panels, covering regressions at ASEAN level and each 493 country panel. 494 495 [Table 7] 496 497 LRMES estimation with FGLS reveal that the world’s GDP, 푊퐺퐷푃, does not have a 498 statistically significant influence on LRMES. Meanwhile for the home country business cycle, 499 퐻퐺퐷푃, there is a weak evidence of counter-cyclicality only for Indonesia but not for other 500 countries. In the FGLS estimation, Malaysia and Thailand show seemingly contradictive sign 501 on the impact of 퐻퐺퐷푃. But it would be misleading to interpret this as a procyclical 502 relationship between systemic risk and the business cycle, since, as discussed in the previous 503 section, the majority of banks in Malaysia and Thailand are having negative values for LRMES 504 within our sampled period. The indication of procyclicality in the OLS FE estimation for ASEAN 505 and Thailand panels should be also understood using the same lens of analysis. 506 The next variable of interest to us is the leverage ratio, 퐿퐸푉푅. We find some evidence 507 that leverage is significantly affecting LRMES especially for Philippines with the theoretically 508 correct signs. This is consistent with our theoretical framework which predicts that leverage 509 ratio will increase probability of default. This result also suggests a potential role of leverage 510 ratio as a control tool for the regulators for mitigating systemic risk. 511 The money market rate, 푀푀푅퐴푇퐸, is found to be mostly significant and with the right 512 negative signs in both FGLS and OLS FE estimations. This is especially true for the ASEAN, 513 Indonesia, The Philippines, and Thailand panels. The significant negative coefficient suggests 514 that controlling interest rate, i.e. monetary policy, could serve as an effective tool for 515 mitigating systemic risk in some contexts. 516 The second part of our estimation focus on the variable 푆푅퐼푆퐾, which is our preferred 517 systemic risk measurement for this study. The dependent variable, 푆푅퐼푆퐾, is measured in the 518 total amount of equity which a bank will lose in the case a crisis (extreme event) hits the 18

519 economy. As commonly done in the literature, in this estimation, 푆푅퐼푆퐾 is transformed as 520 delta of logarithm, which represents growth of SRISK. The results of our estimation are 521 presented in Table 8. 522 Before moving further, we need to note that the number of observations for Table 8 523 is less than it was in Table 7 due to the fact that SRISK can only take positive values; 524 observations with negative LRMES from Table 7 will not appear in Table 8. For Thailand and 525 Malaysia panels, the decrease in the number of observations is very significant because banks 526 with non-zero SRISK comprise only 30 and 19 percent of their banking systems respectively. 527 528 [Table 8] 529 530 Our first focus is again the business cycle, which is represented by two variables, 531 푊퐺퐷푃 and 퐻퐺퐷푃. Similar to the LRMES case above, we also find weak evidence of counter- 532 cyclicality of SRISK. 푊퐺퐷푃 is not significant across the board, except in OLS FE estimation 533 using ASEAN panel. 퐻퐺퐷푃 is found to be significant with the theoretically correct sign for the 534 ASEAN and The Philippines panels only. Albeit weak, this result is consistent with our 535 theoretical framework prediction of the counter-cyclicality of the probability of default. 536 Our second focus variable is leverage ratio, 퐿퐸푉푅. Here we find a quite strong 537 evidence. In both FGLS and OLS FE models, we find that 퐿퐸푉푅 is significant for the ASEAN, 538 Indonesia, The Philippines, and Thailand panels, all with the theoretically correct positive sign. 539 This is clearly showing stronger results compared to the LRMES model above. Here, our 540 results strongly suggest that the more leveraged a bank is, the higher the systemic risk of the 541 bank. 542 Surprisingly, the deposit insurance, 퐼푁푆푅, turns out to be mainly irrelevant in our 543 sample, both in LRMES and SRISK models. Also, unfortunately, for The Philippines, we cannot 544 estimate the effect of deposit insurance for its country-specific panel because the deposit 545 insurance scheme has been implemented there since 1963, far before the starting point of 546 our observation. 547 In general, our models, both for 퐿푅푀퐸푆 and 푆푅퐼푆퐾, reveal a converging result which 548 mainly supports our theoretical framework prediction. The business cycle, which is 549 represented by 푊퐺퐷푃 and 퐻퐺퐷푃 are found to have negative (countercyclical) influence, 19

550 albeit weakly, towards the systemic risk of the banks. In addition, the leverage ratio is found 551 to have positive impact on the systemic risks. 552 553 554 6.3. Discussion 555 6.3.1. Cyclicality of Systemic Risk 556 Current strand of the literature shows us that systemic risk tends to be pro-cyclical. The 557 ground of this argument is obvious. As the economy grows, or experiences an expansion, 558 banks tend to excessively take risk, causing the systemic risk is to build up. This pro-cyclicality 559 argument is empirically supported by current existing literatures such as Liu (2016); Lenoci 560 (2017); and Tasca and Battiston (2016). 561 Our study then enriches this strand of literatures with a different point of view. Based 562 on our theoretical framework from Adrian and Shin (2013), systemic risk is predicted to be 563 countercyclical with regard to the business cycle. The line of argument goes as follows: when 564 the business cycle is in its expansion phase, the probability of default of the banks will be low. 565 The opposite, when the business cycle is in a contraction phase, the probability of default of 566 the banks will be high. This argument is weakly supported by our empirical results. 567 568 6.3.2. The World Business Cycle versus Home Country Business Cycle 569 All five countries in our observations are relatively small economies compared to world 570 economy. We believe such condition make countries in our observation are prone and 571 sensitive toward global fluctuation. Therefore, we believe it is important to account the 572 influence of the world business cycle in our analysis, instead of only considering the home 573 country business cycle. Although we do not find a strong evidence for the role of the world 574 business cycle, yet we still believe it plays some role as a control variable. At least it is 575 significant in the ASEAN panel for the SRISK model. Most of other studies which address 576 similar issue with our study , such as Liu (2016); Lenoci (2017); and Tasca and Battiston (2016), 577 did not account for this factor due to the fact that they work with the developed economies’ 578 data. 579 580 6.3.3. Leverage Ratio, Systemic Risk, and Macroprudential Management 20

581 The most important finding from this study is actually the strong evidence on the role 582 of leverage ratio in explaining the dynamics of the systemic risk. Our theoretical framework 583 predicted that leverage will positively influence the probability of default of a bank. Our 584 estimation results provide strong evidence for this prediction. We show significant and strong 585 correlation between leverage ratio of banks toward its systemic risk (SRISK) across almost all 586 panels. 587 Our results give support for the BASEL III Leverage Ratio Framework, which strongly 588 acknowledges the importance of bank to manage their leverage ratio at prudence level. We 589 confirm that leverage ratio is one of the most effective tools of macroprudential policy for 590 the central banks and/or bank supervisory agencies. 591 592 6.3.4. Systemic Risk and The Role of Deposit Insurance 593 Our study also provides an interesting examination of the role of deposit insurance 594 scheme toward systemic risk. In general, our results suggest negative relationship between 595 the implementation of deposit insurance scheme toward systemic risk. However, since our 596 results are only significant for some panels, we can only deduce cautiously on this matter, 597 and cannot draw a pristine conclusion. This results then provide an opportunity for further 598 researches to examine the effect of deposit insurance toward the systemic risk. 599 As literatures suggested, implementation of deposit insurance may induce banks to 600 increase their risk-appetite and thus tend to increase risk inside the banks. The banks may act 601 as such because their deposit is fully insured. However, we need to be aware that deposit 602 insurance always come with certain terms and conditions of which banks must comply with. 603 Therefore, instead of inducing banks to act irresponsibly, deposit insurance makes banks 604 become more discipline. 605 606 7. Conclusion 607 This study examines cyclicality of systemic risk through the business cycle dynamics. We 608 believe having a comprehensive understanding of the nature of systemic risk behavior is a 609 necessary condition if the regulators wish to mitigate it. Empirical estimations are conducted 610 using data from 73 listed banks in ASEAN-5 countries, i.e. Indonesia, Malaysia, The Philippines, 611 Singapore, and Thailand. The data spans in quarterly frequency from 2001Q1 to 2017Q2. We 612 employ SRISK as the measure of systemic risk as it represents the expected capital shortfall 21

613 of a bank when a crisis occurs. At theoretical level, we borrow a theoretical framework from 614 Adrian and Shin (2013), which predicted that probability of default (in our context, systemic 615 risk) is positively correlated with leverage level and countercyclical with the business cycle 616 condition. 617 Our estimation results show that in general, SRISK reveals countercyclical behavior 618 under the influence of the business cycle, which is aligned with our theoretical framework 619 prediction. The general countercyclicality pattern is exhibited to be similar for most countries, 620 although with different significance level. This result is then providing contrast viewpoint with 621 the current existing literature such as Liu (2016); Lenoci (2017); and Tasca and Battiston 622 (2016), which outlined procyclical behavior of systemic risk. Since our estimation is based on 623 a different theoretical framework with those literatures, we believe the different in results is 624 obvious, and will enhance the strands of the literatures in this issue. As an effort to provide 625 comprehensive analysis, our study put attention not only toward the home country business 626 cycle, but also the world business cycle – represented by G20 quarterly annual GDP growth 627 rates. We employ such dual-business cycle approach because all countries in our dataset are 628 relatively small compared to the world economic size and very sensitive toward the world 629 economic fluctuation. 630 Another important focus of this study is to find out how leverage level of the banks 631 might correlate with their systemic risk. Our theoretical framework predicted positive 632 relationship between both variables, and this prediction is strongly supported by our 633 empirical estimations. The results observe significant role of leverage level in explaining 634 dynamics of SRISK. This finding thus gives a solid sustenance for the BASEL III Leverage Ratio 635 Framework, which outlined the importance of banks to maintain its leverage level to be at 636 the utmost prudence level. 637 638 8. References 639 Acharya, V., Engle, R., & Richardson, M. (2012). Capital Shortfall: A New Approach to Ranking and 640 Regulating Systemic Risks. American Economic Review, 59-64. 641 Acharya, V., Pedersen, L., Philippon, T., & Richardson, M. (2016). Measuring Systemic Risk. The Review 642 of Financial Studies. 643 Acharya, V., Santos, J., & Yorulmazer, T. (2010). Systemic Risk and Deposit Insurance Premiums. 644 Federal Reserves Bank of New York Economic Policy Review. 22

645 Adrian, T., & Brunnermeier, M. (2016). CoVaR. American Economic Review, 106, 1705-41. 646 Adrian, T., & Shin, H. (2013). Procyclical Leverage and Value-at-Risk. The Review of Financial Studies. 647 Allen, L., Bali, T., & Tang, Y. (2012). Does Systemic Risk in the Financial Sector Predict Future Economic 648 Downturns? Review of Financial Studies, 25(10), 3001-3036. 649 Ayomi, S., & Hermanto, B. (2013). Systemic Risk and Financial Linkages Measurement in the 650 Indonesian Banking. Bulletin of Monetary Economics and Banking, 91-113. 651 Baltagi, B. H. (2005). Econometric Analysis of Panel Data, Third Edition. West Sussex: John Wiley & 652 Sons, Ltd. 653 Bank for International Settlements (BIS). (2017). Basel III: Finalising Post-Crisis Reforms. Bank for 654 International Settlements (BIS). 655 Bansal, R., & Yaron, A. (2004). Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles. 656 Journal of Finance, 59(4), 1481-1509. 657 Bansal, R., Kiku, D., Shaliastovich, I., & Yaron, A. (2014). Volatility, the Macroeconomy, and Asset 658 Prices. Journal of Finance, 69(6), 2471-2511. 659 Brownlees, C., & Engle, R. F. (2017). SRISK: A Conditional Capital Shortfall Measure of Systemic Risk. 660 The Review of Financial Studies, Volume 30, 48-79. 661 Calomiris, C., & Jaremski, M. (2016). Deposit Insurance: Theories and Facts. NBER Working Paper 662 Series. 663 Cameron, A. C., & Trivedi, P. K. (2009). Microeconometrics Using Stata. Texas: Stata Press. 664 David, A. (1997). Fluctuating Confidence in Stock Markets: Implications for Returns and Volatility. 665 Journal of Financial and Quantitative Analysis, 32(4), 427-462. 666 Engle, R. (2016). Dynamic Conditional Beta. Journal of Financial Econometrics, 643-667. 667 Engle, R., Jondeau, E., & Rockinger, M. (2014). Systemic Risk in Europe. Review of Finance, 1-46. 668 Epstein, L. G., & Zin, S. E. (1989, July). Substitution, Risk Aversion, and the Temporal Behavior of 669 Consumption and Asset Returns: A Theoretical Framework. Econometrica, 57(4), 937-969. 670 Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic Risk and the Macroeconomy: An Empirical Evaluation. 671 Journal of Financial Economics, 119, 457-471. 672 Hamilton, D., Hughes, T., & Malone, S. (2015). Measuring Systemic Risk in the Southeast Asian 673 Financial System. Moody's Analytics. 674 Hamilton, J. D., & Lin, G. (1996). Stock Market Volatility and the Business Cycle. Journal of Applied 675 Econometrics, 11(5), 573-593. 676 Hsiao, C. (2003). Analysis of Panel Data. Cambridge: Cambridge University Press. 677 Irawan, D., & Kacaribu, F. (2017). Tri-Cycles Analysis on Bank Performance: Panel VAR Approach. 678 Bulletin of Monetary Economics and Banking, 403-441. 23

679 Jacobs, J. (1998). Econometric Business Cycle Research: An Assessment of Method. University of 680 Groningen Working Paper. 681 Kusairi, S., Sanusi, N. A., & Ismail, A. G. (2018). Dilemma of Deposit Insurance Policy in ASEAN 682 Countries: Does It Promote Banking Industry Stability or Moral Hazard? Borsa Istanbul Review, 683 33-40. 684 Lenoci, F. (2017). Is Systemic Risk Pro-Cyclical? EFMA Annual Meeting 2017 Conference Paper. Athens: 685 European Financial Management Association. 686 Liu, M., & Staum, J. (2011). Systemic Risk Components and Deposit Insurance Premia. FDIC Center for 687 Financial Research Working Paper. 688 Liu, X. (2016). Measuring Systemic Risk with Regime Switching in Tails. Economic Modelling. 689 Lopez-Espinosa, G., Moreno, A., Rubia, A., & Valderrama, L. (2012). Systemic Risk and Asymmetric 690 Responses in the Financial Industry. IMF Working Paper. 691 Manap, T. A. (2019). Measuring Systemic Risk in Dual Banking System: The Case of Malaysia. In M. 692 Zulkibri, & T. Manap, Islamic Finance, Risk-Sharing and Macroeconomic Stability (pp. 151-170). 693 Cham: Palgrave Macmillan. 694 NYU Volatility Lab. (2017). Retrieved from New York University Volatility Lab (V-Lab): 695 https://vlab.stern.nyu.edu/docs/SRISK/GMES 696 Ozoguz, A. (2009). Good Times or Bad Times? Investors' Uncertainty and Stock Returns. Review of 697 Financial Studies, 22(11), 4377-4422. 698 Qin, X., & Zhou, C. (2018). Financial Structure and Determinants of Systemic Risk Contribution. Pacific- 699 Basin Finance Journal. 700 References 701 Schwert, G. W. (1989, December). Why Does Stock Market Volatility Change Over Time? Journal of 702 Finance. 703 Subrahmanyam, A., & Titman, S. (2013). Financial Market Shocks and the Macroeconomy. Review of 704 Financial Studies, 26, 2687-2717. 705 Tasca, P., & Battiston, S. (2011). Market Procyclicality and Systemic Risk. Quantitative Finance, 16(8), 706 1219-1235. 707 Varotto, S., & Zhao, L. (2018). Systemic Risk and Bank Size. Journal of International Money and Finance, 708 45-70. 709 Veronesi, P. (1999). Stock Market Overreaction to Bad News in Good Times: A Rational Expectations 710 Equilibrium Model. Review of Financial Studies, 12(5), 975-1007. 711 Weiß, G., Neumann, S., & Bostandzic, D. (2014). Systemic Risk and Bank Consolidation: International 712 Evidence. Journal of Banking & Finance, 165-181. 24

713 Zedda, S., & Cannas, G. (2017). Analysis of Banks' Systemic Risk Contribution and Contagion 714 Determinants Through the Leave-One-Out Approach. Journal of Banking and Finance, 1-16. 25

715 Appendix A. Construction of LRMES and SRISK 716 In this study, we calculated SRISK manually based on daily stock price of each bank in our 717 sample. We follow calculation process from NYU Volatility Lab (2017) and Engle (2016) to 718 calculate LRMES and SRISK. 719

720 721 Figure A. Illustration of Recursive Dynamic Conditional Beta (DCB) Calculation 722

723 First, we calculate Dynamic Conditional Beta (훽푖,푡) for equation (6). We calculate this 724 Beta for each bank in every time period (quarterly). The first quarter Beta (2001:Q1) comes 725 from the first day of the bank daily stock return in our dataset until the last trading day in 726 2001:Q1. The second quarter Beta (2001:Q2) comes from the first day stock return until the 727 last trading day in 2001:Q2. We do this process recursively for every time period for each 728 bank until last observation in our dataset (2017:Q2). Our ground for doing recursive 729 calculation is the basic rational expectation notion that economic agent will use every past 730 information that can be acquired to predict the future. This means Beta for 2017 comes from 731 the first day stock return (at 2001:Q1) until the last trading day in 2017:Q2. As we implement 732 this style of calculation for every time period in our dataset for each bank, it means the 733 calculation of Beta in crisis period also consider the stock return dynamics since the first day 734 in our dataset. The formula to calculate Dynamic Conditional Beta as in Engle (2016) is 735 presented in equation (A.1.). 736

737 푅푖,푡 = 훽푖,푡푅푚,푡 + 훾푖,푡푅푚,푡−1 + 푢푖,푡 (A.1.) 26

738

739 The term 푅푖,푡 refers to daily return of bank i at time t. The term 훽푖,푡 refers to the

740 Dynamic Conditional Beta (DCB). The term 푅푚,푡 refers to daily market return where bank i

741 operates at time t. The term 푅푚,푡−1 refers to daily market return at time t-1 where bank i

742 operates. Meanwhile, the term 푢푖,푡 is residual. 743 After acquiring the Beta, we use that to calculate the Long-Run Marginal Expected 744 Shortfall (LRMES). The LRMES is computed as: 745

log(1−푑)∗훽푖,푡 746 퐿푅푀퐸푆푖,푡 = 1 − 푒 (A.2.) 747

748 The term 퐿푅푀퐸푆푖,푡 stands for the LRMES of institution i at the time t. Meanwhile, term 푑 is 749 the six-month crisis threshold for a market decline. It is set to be 40 percent as per the 750 standard version and can be modified easily if we assume another threshold level. Meanwhile

751 훽푖,푡 is the DCB. 752 Finally, after obtaining the LRMES, we can compute SRISK, which is measured as 753 expected capital shortfall that will be experienced by a firm when the market is on a crisis. 754 SRISK can be obtained by: 755

756 푆푅퐼푆퐾푖,푡 = 푘. 퐷퐸퐵푇푖,푡 − (1 − 푘). 퐸푄푈퐼푇푌푖,푡. (1 − 퐿푅푀퐸푆푖,푡) (A.3.) 757 758 where k stands for the capital requirement. In this study, we use k = 8%, following the 759 standard capital requirement level as in Basel requirement and applied by all countries in the 760 observation. LRMES is the Long-Run Marginal Expected Shortfall, EQUITY is the current 761 market capitalization of the firm at t, while DEBT is the total liability of the firm. 762 763 764 765 766 767 768 27

769 Appendix B. Figures 770

90 80 70 60 50 40

30 BANKING TOBANKING TOTAL FINANCIAL FINANCIAL (%) SYSTEM 20 10 0

Indonesia Malaysia Philippines Singapore Thailand 771 772 Figure 1 Banking System Asset compared with Total Financial System in ASEAN 5, 2001-2016 773 Source: World Bank 774

775 776 Figure 2 Relationship Between NPL and GDP Growth in ASEAN-5 Countries, 2001-2016 777 Source: World Bank 778 28

16 15 14 13 12 11

CAR(%) 10 9 8 7 6 2010 2011 2012 2013 2014 2015 2016 2017

Indonesia Malaysia Philippines Singapore Thailand 779 780 Figure 3 Aggregate Banking System Capital Adequacy Ratio (CAR) of ASEAN Countries, 2010-2017 781 Source: World Bank 782 120

100

80

60 (%)

40

20

0

Indonesia Malaysia Philippines Singapore Thailand 783 784 Figure 4 Value of Stock Market Transaction (percent of GDP), 2001-2017 785 Source: World Bank 786 787 29

788 789 Figure 5 Turnover Ratio per Quarter 790 Source: Bloomberg 791

792 793 Figure 6 VIX Index 794 Source: Bloomberg 795 30

796 797 Figure 7 LRMES Dynamics by Country 798 31

799 800 Figure 8 SRISK Dynamics over Time by Country 801 32

802 803 Figure 9 SRISK-to-Equity Dynamics by Country 33

804 Appendix C. Tables 805 Table 1. List of Observations Sample Asset No Country Number of Banks Compared to Total System (%)4 1 Indonesia 37 77.59 2 Malaysia 7 76.80 3 The Philippines 14 72.31 4 Singapore5 3 100.00 5 Thailand 9 81.03 806 807 Table 2. List of Variables No Variables Frequency Observation Unit Source 1 Individual Stock Return Daily; to construct quarterly Individual Bank Bloomberg LRMES and SRISK 2 Asset Quarterly Individual Bank Bloomberg 3 Liability Quarterly Individual Bank Bloomberg 4 Equity Quarterly Individual Bank Bloomberg 5 Deposit Quarterly Individual Bank Bloomberg 6 Return on Asset (ROA) Quarterly Individual Bank Bloomberg 7 Loan Loss Provision (LLP) Quarterly Individual Bank Bloomberg 8 Market Capitalization Quarterly Individual Bank Bloomberg 9 Market Return Daily; to construct quarterly Country Bloomberg MES/SRISK 10 Real GDP Growth Quarterly Country IMF 11 Inflation Quarterly Country IMF 12 Money Market Rate Quarterly Country IMF 13 Exchange Rate to USD Quarterly Country IMF 14 Deposit Insurance Quarterly Country Various Sources6 808 809 Table 3. Deposit Insurance Establishment in Sample Countries Dummy No Country Establishment Date Source Start 1 Indonesia 22 September 2005 2005-Q4 Indonesia Deposit Insurance Corporation (2019)7 Malaysia Deposit Insurance 2 Malaysia 2 August 2005 2005-Q4 Corporation (2019)8 Philippine Deposit Insurance Corporation 3 The Philippines 22 June 1963 2001-Q1 (2019)9 Singapore Deposit Insurance Corporation 4 Singapore 1 April 2006 2006-Q2 (2019)10 Thailand Deposit Protection 5 Thailand 11 August 2008 2008-Q4 Agency (2019)11

4 Total system assets data are retrieved from CEIC Data for each country. 5 The figure is based on local banks in Singapore. There are only 4 local banks in Singapore, owned by 3 financial groups in our sample. Data retrieved on 12 October 2019. https://eservices.mas.gov.sg/fid/institution?sector=Banking&category=Local%20Bank 6 This variable is in dummy form. The value is 1 if at time t country j implements deposit insurance scheme, and 0 if otherwise. We acquire information about the implementation of the scheme from official website of each country’s deposit insurance corporation. 7 https://www.lps.go.id/en/web/guest/sejarah 8 https://www.pidm.gov.my/en/pidm/mandate/ 9 http://www.pdic.gov.ph/?nid1=37&pdic50=1 10 https://www.sdic.org.sg/public/history 11 http://www.dpa.or.th/en/site/index 34

810 811 Table 4. Heteroscedasticity and Autocorrelation Test for Equation (8) and (9) Equation Heteroscedasticity Autocorrelation Equation (8) Yes Yes Equation (9) Yes Yes 812 813 Table 5. Summary Statistics of Variables Variable Obs Mean Stdev Min Max 푊퐺퐷푃 3,082 3.5495 1.5664 -2.3619 5.4236 퐻퐺퐷푃 3,075 5.2594 2.3131 -8.8272 19.0401 퐼푁퐹퐿 3,082 4.3553 3.0628 -2.7821 17.7825 푀푀푅퐴푇퐸 3,049 4.3977 2.2700 0.0334 14.9567 퐸푋푅퐴푇퐸 3,082 0.0173 0.0881 -0.2369 0.3589 퐿퐸푉푅 3,015 9.6599 15.8991 -31.9149 642.8571 퐷퐸푃 3,010 0.7392 0.1216 0.0032 1.1040 푅푂퐴 3,013 0.0037 0.0375 -1.3094 0.6390 퐿퐿푃 2,953 0.0018 0.0081 -0.1483 0.2548 814 815 Table 6. Summary Statistics of SRISK and LRMES Country Unit Obs Mean Stdev Min Max LRMES ASEAN 3,082 0.1643 0.1064 -1.1970 0.3690 Indonesia 1,435 0.1325 0.1264 -0.3802 0.3634 Malaysia 405 0.1969 0.0527 0.0933 0.3042 The Philippines 565 0.1353 0.0876 -1.1970 0.2671 Singapore 179 0.2123 0.0145 0.1888 0.2442 Thailand 498 0.2453 0.0288 0.1782 0.3690

SRISK Indonesia Trillion Rupiah 908 3.0 5.9 0.0 42.0 Malaysia Million Ringgit 75 739.0 956.0 3.9 3,700.0 The Philippines Billion Peso 491 8.9 8.0 0.1 49.0 Singapore Million SGD 70 1,050.0 775.0 15.0 3,600.0 Thailand Billion Baht 157 14.5 15.5 0.0 64.0

SRISK-to-Equity ASEAN Ratio 1,701 0.2513 0.1864 0.0000 1.6957 Indonesia Ratio 908 0.2778 0.1911 0.0021 0.9667 Malaysia Ratio 75 0.3121 0.1970 0.0033 0.7308 The Philippines Ratio 491 0.2587 0.1774 0.0031 1.6957 Singapore Ratio 70 0.0577 0.0403 0.0003 0.1400 Thailand Ratio 157 0.1315 0.1046 0.0000 0.3833 816 35

817 Table 7. LRMES Estimation Results ASEAN Indonesia Malaysia The Philippines Singapore Thailand (1) FGLS Estimation 푊퐺퐷푃푡−1 0.0000 0.0000 0.0004 0.0000 -0.0001 -0.0002

퐻퐺퐷푃푗,푡−1 0.0001 -0.0044*** 0.0011** -0.0013 -0.0002 0.0010** 퐼푁푆푅푗,푡 0.0007 0.0098* 0.0042** (omitted) -0.0080*** 0.0004

퐼푁퐹퐿푗,푡−1 0.0001 0.0019** 0.0001 0.0008 -0.0004* 0.0002

푀푀푅퐴푇퐸푗,푡−1 -0.0018*** -0.0072*** -0.0013 -0.0048** -0.0007 -0.0004 퐸푋푅퐴푇퐸푗,푡−1 0.0001 -0.0224 -0.0026 0.0213 0.0018 0.0073

퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 -0.0004** -0.0017* -0.0008*** (omitted) 0.0001 -0.0005

퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 0.0000 -0.0003* 0.0000 -0.0001 0.0000 -0.0001*

퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 0.0001 0.0012*** -0.0002 0.0003 0.0001 0.0000 퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 -0.0004 0.0041 0.0006 -0.0092* -0.0009 0.0012

퐿퐸푉푅푖,푗,푡−1 0.0000 0.0000 0.0000 0.0009*** -0.0001 0.0000

퐷퐸푃푖,푗,푡−1 -0.0094*** -0.0047 -0.0067 -0.0315* 0.0358*** 0.0165***

푅푂퐴푖,푗,푡−1 0.0111* 0.0095 0.0599 -0.0142 0.0009 -0.0418

퐿퐿푃푖,푗,푡−1 0.0195 0.0243 0.0823 -0.0815 -0.5687 -0.0152

퐶푅퐼푆푖,푗,푡 -0.0008 -0.0024** -0.0001 0.0030 -0.0008 -0.0027**

푇퐼푀퐸푖,푗,푡 -0.0005*** -0.0007*** 0.0004*** -0.0011*** -0.0002*** -0.0009*** Constant 0.2798*** 0.3092*** 0.1071*** 0.3890*** 0.2346*** 0.4082***

Observations 3082 1435 405 565 179 498

(2) OLS Fixed-Effect Estimation

푊퐺퐷푃푡−1 -0.0007 0.0008 0.0004 -0.0026 0.0000 -0.0019*

퐻퐺퐷푃푗,푡−1 0.0026** -0.0012 0.0013 -0.0017 0.0001 0.0017***

퐼푁푆푅푗,푡 0.0042 0.0506 0.0115 (omitted) -0.0078 -0.0031

퐼푁퐹퐿푗,푡−1 0.0020*** 0.0024 0.0007 -0.0007 -0.0004 0.0021**

푀푀푅퐴푇퐸푗,푡−1 -0.0042** -0.0105 -0.0051 -0.0071 -0.0001 -0.0037*

퐸푋푅퐴푇퐸푗,푡−1 -0.0167 -0.1946 -0.0210 -0.0094 -0.0049** -0.0028

퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 -0.0027** -0.0091 -0.0018 (omitted) -0.0001 -0.0002 퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 0.0000 -0.0001 -0.0001 0.0002 0.0000 -0.0009** 퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 0.0001 0.0014 0.0002 0.0005 0.0002 0.0006**

퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 0.0002 0.0339 0.0043 -0.0109 -0.0010* 0.0011

퐿퐸푉푅푖,푗,푡−1 0.0000 0.0002 -0.0000*** 0.0006 0.0003 0.0000

퐷퐸푃푖,푗,푡−1 0.0030 -0.0104 -0.0162 -0.0926 0.0461* -0.0281

푅푂퐴푖,푗,푡−1 -0.0083 -0.0098 -0.0439 -0.6424 -0.3529* 0.5201

퐿퐿푃푖,푗,푡−1 0.0423 0.1070 -0.1191 0.5133 0.2259 0.1405

퐶푅퐼푆푖,푗,푡 -0.0041 -0.0055 0.0002 0.0142** -0.0014 -0.0080*

푇퐼푀퐸푖,푗,푡 -0.0001 -0.0001 0.0001 -0.0001 -0.0002 -0.0010*** Constant 0.1859*** 0.1717* 0.1895*** 0.2546* 0.2310*** 0.4761***

Observations 3082 1435 405 565 179 498 R-Squared 0.0308 0.0376 0.0445 0.0411 0.8380 0.6137 Log-Likelihood 5843 2575 1142 901 776 1409 AIC -11700 -5120 -2270 -1780 -1550 -2800 BIC -11600 -5030 -2250 -1720 -1540 -2770 818 * P < 10% ** P < 5% *** P < 1% 36

819 Table 8. SRISK Estimation Results ASEAN Indonesia Malaysia The Philippines Singapore Thailand (1) FGLS Estimation 푊퐺퐷푃푡−1 -0.0097 -0.0256 0.0159 0.0103 0.1350 -0.0075

퐻퐺퐷푃푗,푡−1 -0.0394 -0.0722 0.0061 -0.1083*** -0.0233 0.0135

퐼푁푆푅푗,푡 -0.1605 -0.3798 0.0700 (omitted) 1.2137 -0.1169

퐼푁퐹퐿푗,푡−1 0.0137 0.1569** -0.0256 0.0130 -0.1157 -0.0582

푀푀푅퐴푇퐸푗,푡−1 -0.0343** -0.1439* 0.0554 -0.0976* -1.0306* 0.0109

퐸푋푅퐴푇퐸푗,푡−1 -0.0010 -0.6424 1.3749 1.1729 0.5159 -0.0130 퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 0.0123 0.0688 0.0186 (omitted) -0.0376 -0.0587 퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 -0.0024 -0.0293** 0.0014 -0.0007 -0.0057 0.0205

퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 0.0079*** 0.0282* -0.0084 0.0231*** 0.0645 -0.0015

퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 0.0196 0.1324 -0.0447 -0.2032 -0.1083 0.2897

퐿퐸푉푅푖,푗,푡−1 0.0452*** 0.0395*** 0.0174 0.0202** 0.7721*** 0.1082***

퐷퐸푃푖,푗,푡−1 -0.1808** -0.2556 -0.3510 0.0377 0.3179 -0.1124

푅푂퐴푖,푗,푡−1 0.5297 -0.3263 2.5972 -0.9486 -149.8691 -13.8679

퐿퐿푃푖,푗,푡−1 -0.1042 -0.9727 2.8356 -3.0730 335.9709 48.0831*

퐶푅퐼푆푖,푗,푡 -0.0221 -0.0385 0.0157 0.0050 -0.3558 0.0265

푇퐼푀퐸푖,푗,푡 0.0024*** 0.0009 -0.0009 0.0064** -0.0372 0.0036 Constant -0.3164 0.2649 -0.0308 -1.0454 0.7429 -1.1917

Observations 1700 907 75 491 70 157

(2) OLS Fixed-Effect Estimation 푊퐺퐷푃푡−1 -0.0259* -0.0371 0.0216 -0.0129 0.1879 -0.0015

퐻퐺퐷푃푗,푡−1 -0.0534*** -0.0490 0.0614 -0.0880** -0.0118 -0.0244

퐼푁푆푅푗,푡 -0.1725 -0.4544 0.1734 (omitted) 1.4885 0.1293

퐼푁퐹퐿푗,푡−1 -0.0069 0.1020 -0.0793 -0.0561 -0.1986 0.0028

푀푀푅퐴푇퐸푗,푡−1 -0.0359* -0.0874 0.1610 0.0028 -0.7835 -0.2591 퐸푋푅퐴푇퐸푗,푡−1 0.7147 -0.6739 4.0908 1.5944 1.5852 -0.8455 퐻퐺퐷푃 ∗ 퐼푁푆푅푗,푡 0.0153 0.0700 0.0263 (omitted) -0.0981 -0.1145

퐻퐺퐷푃 ∗ 퐼푁퐹퐿푗,푡−1 0.0012 -0.0160 0.0190 0.0067 0.0051 0.0215

퐻퐺퐷푃 ∗ 푀푀푅퐴푇퐸푗,푡−1 0.0069** 0.0089 -0.0389 0.0139 0.0486 0.0254

퐻퐺퐷푃 ∗ 퐸푋푅퐴푇퐸푗,푡−1 -0.1593 0.0672 -0.3588 -0.2938 -0.5145 0.3515**

퐿퐸푉푅푖,푗,푡−1 0.0912*** 0.0851*** -0.0214 0.1161*** 0.7356 0.2525*

퐷퐸푃푖,푗,푡−1 -0.2933 -0.5810 0.1601 -0.6703 1.0399 -0.1472

푅푂퐴푖,푗,푡−1 -11.8759** -14.2404** 9.3794 -2.4288 -227.3296 -117.5401

퐿퐿푃푖,푗,푡−1 -3.8772** -3.7413 1.7625 -19.1134 471.1329 93.6154*

퐶푅퐼푆푖,푗,푡 -0.0640 -0.0362 -0.0676 0.0881 -0.3246 -0.4102

푇퐼푀퐸푖,푗,푡 0.0023 0.0012 -0.0018 0.0099** -0.0286 -0.0057 Constant -0.3257 0.3543 -0.2359 -2.0372** -1.3190 0.4939

Observations 1701 908 75 491 70 157 R-Squared 0.0483 0.0612 0.0588 0.0767 0.2589 0.1293 Log-Likelihood -1590 -753 -87 -364 -92 -194 AIC 3221 1537 181 753 187 403 BIC 3308 1614 188 808 192 428 820 * P < 10% ** P < 5% *** P < 1% 37

Table 9. Correlation Table (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (1) 푆푅퐼푆퐾 1 (2) 퐿푅푀퐸푆 -0.0099 1 (3) 푊퐺퐷푃 -0.0739 0.0772 1 (4) 퐻퐺퐷푃 -0.0812 -0.0672 0.4676 1 (5) 퐼푁푆푅 0.0161 -0.1521 -0.1208 -0.0219 1 (6) 퐼푁퐹퐿 -0.0205 -0.1034 0.1172 0.0278 0.028 1 (7) 푀푀푅퐴푇퐸 0.0039 -0.1724 0.0358 -0.0085 0.0551 0.7479 1 (8) 퐸푋푅퐴푇퐸 0.0294 -0.0918 -0.3728 -0.2075 0.0989 0.222 0.042 1 (9) 퐿퐸푉푅 -0.1291 -0.0225 0.0166 0.0798 0.0019 0.02 -0.0235 -0.01 1 (10) 퐷퐸푃 -0.0081 -0.307 -0.0419 0.0894 0.0937 0.2383 0.2966 0.1032 0.3656 1 (11) 푅푂퐴 0.0467 0.1227 0.0753 0.0297 -0.089 0.0377 0.0157 -0.0211 -0.0792 -0.0616 1 (12) 퐿퐿푃 -0.0173 0.0098 -0.0183 -0.0315 0.0594 -0.0021 0.0586 -0.07 -0.0281 0.022 -0.4728 1 (13) 퐶푅퐼푆 -0.0505 -0.0448 -0.1952 -0.0222 0.1295 0.1222 0.1079 -0.2637 0.0857 -0.0233 0.0239 -0.0084 1 (14) 푇퐼푀퐸 0.0431 -0.1301 -0.1412 -0.0703 0.4904 -0.2525 -0.3839 0.2821 -0.1338 0.0843 -0.0973 0.0685 -0.2479 1

38

Table 10. List of Banks in Dataset Indonesia Malaysia The Philippines Singapore Thailand Malayan Banking BDO Unibank DBS Group Holdings CIMB Group Holdings Metropolitan Bank & Trust Oversea-Chinese Banking Corporation Krung Thai Bank Publik Bank Bank of The Philippine Islands United Overseas Bank Siam Hong Leong Bank Philippine National Bank Bank Cimb Niaga Alliance Financial Group Security Bank Bank of Ayudhya Indonesia BIMB Holdings China Banking TMB Bank Bank Pan Indonesia Kenanga Investment Bank Rizal Commercial Banking CIMB Thai Bank Bank Permata Union Bank of the Philippines TISCO Financial Group East West Banking Kiatnakin Bank

Bank Maybank Indonesia Philippine Savings Bank Bank OCBC NISP Asia United Bank Bank Bukopin Philtrust Bank Bank Pembangunan Daerah Jawa Barat dan Banten Philippine Bank of Communications Bank Tabungan Pensiunan Nasional Citystate Savings Bank Bank Mega

Bank Mayapada Internasional Bank Sinarmas Bank QNB Indonesia Bank Artha Graha Internasional Bank Victoria International Bank Woori Saudara Indonesia 1906

Bank Jtrust Indonesia

Bank Capital Indonesia Bank MNC Internasional Bank China Construction Bank Mestika Dharma

Bank Nusantara Parahyangan Bank Rakyat Indonesia Agroniaga Bank Bumi Arta Bank of India Indonesia Bank Pembangunan Daerah Banten

Bank Maspion Indonesia Bank Agris Bank Harda Internasional

Bank Mitraniaga Bank Ganesha Bank Panin Dubai Syariah