measuring contagion risk among vietnam''s listed commercial banks

TRƯỜNG ĐẠI HỌC KINH TẾ QUỐC DÂN BÁO CÁO KẾT QUẢ NGHIÊN CỨU ĐỀ TÀI THAM GIA XÉT GIẢI THƯỞNG "SINH VIÊN NGHIÊN CỨU KHOA HỌC" NĂM 2016 MEASURING CONTAGION RISK AMONG VIETNAM’S LISTED COMMERCIAL BANKS Thuộc lĩnh vực khoa học công nghệ: Kinh tế – Tài – Ngân Hàng Ha Noi - 2016 Measuring contagion risks among Vietnam’s listed commercial banks Abstract: In this research, we apply the Distance to Default (DD) model to measure the contagion risk across listed commercial banks of Vietnam Particularly, we try to measure how other banks are affected when one banks fall into the extreme event and find the signal of contagion risk The result of this paper show the overall picture of this banking system: individual banks are not only affected by idiosyncratic shocks within the system but also the common shocks to the financial markets To conclude, banks are susceptible to others’ operating situation and this result may have important application for the whole Vietnam banking system Contents Tables and Figures Tables Figures Introduction In recent year, contagion risk is becoming a hot trend globally and has been debated intensely ever since The world economic are getting more and more integrated, financial institutions increased their interconnectedness to each other It is needless to say that this integration provides us with huge advantages of labor, information and cost savings, which ultimately leads to improvement of the liquidity and depth of the market However, with great advantages come great obstacles to overcome In this situation, it is the case of “contagion” in the system “Contagion” as many financial professionals has defined to be the transmission of some individual shocks among the financial institutions With the huge amount of transactions happening in all over the world and the amount of connections rapidly increasing between our financial systems, there are high chances that when shocks hitting one financial institution, they could be spreading contagion to other institutions domestically and overseas One of the most famous cases for contagion can be found is the collapse of Lehman Brothers In September 15 of 2008, Lehman Brothers Holding Inc (LBHI) officially announced its bankruptcy with its last financial report of $639 billion in assets and $613 billion of liabilities This was the largest failure of a corporation in the United State history They have survived many trials, building up the entire empire for more than 150 years only to be brought to their knees in only a few weeks At the beginning of the end, their consultant has reminded them that the lack of liquidity maybe the reason for their soon to be downfall after the near failure case of Bear Stearns in March Bear Stearns was the fifth largest investment bank in the country before the fall with its stock priced at $160 a year before When JPMorgan Chase & Co.'s proceeded with the takeover, Bears’ stock price was at a stunning point of $2 It was predicted that the contagion would spread to other banks, especially Lehman Brothers However, the upper management doesn’t seem to take the risk of liquidity so seriously, quoting “told the rating agencies that it was focused on building ‘liquidity fortress” The result was inevitable In their final days, it was reported that “cash and collateral were being tied up by [its] clearing banks … [and] cash had drained very quickly over the last three days of the previous week [preceding its filing]” Even though it was reported that the bank’s situation was in solvency, it was soon discovered that their value of liabilities far exceeded the value of assets for some of Lehman’s assets are valued without basing on any specific reason The Examiner has come to the conclusion that LBHI was in insolvency at the beginning on September and at the time of its filing; the bank has already faced the shortfall of $35 billion If this is the situation of countries in the first world-group, it becomes direr when it comes to developing countries such as Vietnam As mentioned before, the interconnectedness between our domestic institutions has risen immensely through the developing of the financial system as well as the forming of some financial holdings The matter is becoming more and more of an emergency as Vietnam has joined the AEC – Asian Economic Community and TPP – Trans Pacific Partnership, our financial system could even be more complex and integrated The Trans-Pacific Strategic Economic Partnership Agreement – TPP has provided a large number of opportunities for us to improvise our market With the expectation of bring TPP become the agreement of liberalization of the 21 st century, Viet Nam and other member countries has together made commitments about optimizing the approach to the financial market, especially finance-banking services Hence, Vietnam will not only obtain great developing opportunities but will also face many challenges Particularly, by equipping suitable business strategy in the following period –from 2015 to 2030, this can be the chance for Vietnam’s banking system, which is currently weak, to have the significant improvement On the other hand, the competitive pressure also dramatically increases due to the participation of foreign banks in our financial market, especially the financial institutions from US, Japan and Australia The foreign banks with greater financial resources and professional manage skills will have significant impact on domestic banks Because of this reason, when Vietnam joined TPP, so as to raise their competitive ability against the foreign institutions, improving the measuring and managing risks activities must be the top priority for our domestic banks For the reason mentioned above, it is necessary to detect the problem soon and focus on the stress points even in the case when a crisis is to be unenviable This could provide extra time to prepare counter plans Thus, in this paper, we attempts to make a contribution to measure the contagion risk among bank of Vietnam We will be discussing a methodology by Gropp and Moerman (2004) to locate the direction of contagion from one institution to another We propose to use market data, especially the distance to default, to examine bank contagion The approach based on the use of “co-exceedance” and extreme value theory in examining contagion risk among institutions To appropriately apply those measurements domestically, we decided to examine a number of financial institutions in Vietnam In particular, eight commercial banks will be chosen to be representatives of those institutions for contagion examination For the sake of guaranteed and secured data and statistics, these eight banks which are set to be in sample have their data to be in full disclosure In the research conducted recently in the paper of Gropp et al in 2003, the distance to default method is proved to be a fairly appropriate way to measure bank risk for its superiority to other measure methods; avoiding subordinated debt spreads as an example By definition, the default point is the point at which the assets of the bank are equal to the liabilities This method uses both the data and information of stock price returns with asset volatility, leverage and also represents the number of standard deviations away from the default point As our research is conducted among a small number of subjects in the same religion, the whole picture for contagion risk could hardly to be demonstrated Nevertheless, we believe this research could create a foundation for further research later Different literatures suggest a lot of definitions about contagion risk However, in this paper, we define contagion as one bank being hit by an idiosyncratic shock, which is transmitted to other banks We will not specify the channel of transmission, but one could imagine money markets, payment systems, equity (ownership) links and “pure” contagion Follow that definition, we applies a concept called “co-exceedance” to measure contagion risk A co-exceedance is a period (in this paper: a week) during which two or more banks first difference in the distance to default (ln(∆dd)) was in the 5th percentile positive or negative tail For the first part of this paper, we will discuss about different literature on the subject of contagion and methods to measure contagion risk In the second part, we will show the detail method that was applied in this paper The third part will be focused on analyzing result of the research, and for the final part: conclusion and implication for stakeholders Literature Reviews and theoretical background Integration of financial institution and risk increasing industry consolidation, financial deregulation, and globalization are increasing trend in all over the world Financial institutions like banks, insurances, securities are getting more and more complex, connected and integrated Together with it is this trend of the rapid growth in number of large, multi-line financial institutions also known as financial conglomerate This integration in financial systems allows institutions to achieve economies of scale and scope, saving cost, and making more profit However, it also contains special risk that creates more challenges for both management and regulation Difficulties in managing and regulating an integrated market can be credited to the merge of special threads to financial system such as systemic risk and contagion risk Systemic risk is mostly refer to the risk of collapse of an entire financial system or entire market, as opposed to risk associated with any one individual entity, group or component of a system that can be contained therein without harming the entire system (Banking and currency crises and systemic risk, George G Kaufman) It is sometimes erroneously referred to systematic risk, which is the vulnerability of the financial system to events which affect aggregate outcomes such as broad market returns, total economy-wide resource holdings, or aggregate income In the last couple of years, especially after the incident of 2008 crisis, significant concerns about the stability of national and international financial systems have been raised more than ever Systemic risk is now widely accepted as the fundamental underlying concept for the study of economic and finance In addition, contagion risk is another concern of modern financial system Unlike the systemic risk, the definition of contagion risk varies among authors Later, in the next part of this literature review, we will debate about these definitions However, it can be 10 understand in general as the transmission of some individual shocks among the financial institutions Contagion risk, referred in Hal Scot, 2011 (Contagion in the Financial Crisis and What to Do About It in the Future) is one of “three Cs” that central to systemic risk (other two are connectedness, and correlation) Contagion denotes the process whereby the failure of one institution either causes the creditors of others to withdraw funding in a manner akin to a classic bank run or sets off a general panic leading markets to freeze Despite the fact that systemic risk has an increasing impact all over the world and has gradually become a trending topic globally and even in Vietnam, some conferences was held to pay concern about the matter, there are hardly any official papers researching into this matter or in the field of contagion risk in the country It is necessary to realize the danger contagion hold as an individual threat as much as the other dangers As mentioned before, contagion could utterly put down Lehman; the th largest bank in the US; so there is no reason that Vietnam is out if its’ reach Sooner or later, we will have to face this inevitable problem For this reason, our purposes in conducting this work is to make clear and direct intentions to point out the importance of contagion risk domestically and provide a proper method to measure its impacts in Vietnam 1.1 Characteristic and Definition The problem of contagion is longstanding in the regulation of financial institutions and the design of stable financial systems However, the study of this risk is extremely difficult This fact mainly comes from the question: “what exactly is contagion?” Even until today, economists still debates about the clear definition and characteristics of contagion Most of these disputes is about how much responsibility should be assigned to contagion effects Some analysts even believe that there is no contagion but only the worsening economic conditions that cause depositors to take their money out of weak banks and put it into healthier ones 54 Appendix I Figures and Tables Table 2.2.1 Databases eight commercial banks of Vietnam NO BANKS NAMES BANK FOR INVESTMENT AND DEVELOPMENT OF VIETNAM SAIGON HA NOI COMERCIAL JOIN STOCK BANK VIETNAM JOINT STOCK COMMERCIAL BANK FOR INDUSTRIAL AND TRADE JOINT STOCK COMMERCIAL BANK FOR FOREIGN TRADE OF VIETNAM ASIA COMMERCIAL JOINT STOCK BANK VIETNAM EXPORT - IMPORT COMMERCIAL JOINT STOCK BANK MILITARY COMMERCIAL JOINT STOCK BANK SAIGON THUONG TIN COMMERCIAL JOINT STOCK BANK Ticker Symbol BID SHB CTG VCV ACB EIB MBB STB 55 Figure 2.4.1.3 Debt and Equity for individual banks through the period of time in million VND The capital structure of BID The capital structure of SHB The capital structure of VCB The capital structure of CTG The capital structure of EIB The capital structure of MMB The capital structure of ACB The capital structure of STB Source: Authors’ calculation Figure 2.4.3.5 Stock price of individual banks of Vietnam from 2009 to 2015 Stock price of BID Stock price of SHB Stock price of VCB Stock price of CTG Stock price of EIB Stock price of MMB Stock price of ACB Stock price of STB Mean Distance to Default Log-differenced DD (weekly different) Number of observations: ln(∆dd) Number of tail observations (5%) Number of tail observations (10%) 1st Minimum Maximum 3.926 -0.00083 1.625 -0.10017 10.895 0.19849 Standard deviation 1.454 0.01476 268.625 13.375 26.75 76 319 20 41 88.780 5.578 11.042 Table 3.1.1 Overall descriptive statistics of the research Descriptive statistic of distance to default BID Mean Maximum Minimum SHB VCB CTG 3.32 3.62 3.231 2.724 5.29 7.38 5.105 4.487 2.45 2.30 2.267 1.625 MB EIB B 4.64 5.043 7.99 9.316 3.03 2.841 ACB STB 4.973 3.796 10.89 5.489 2.352 2.275 56 Standard Deviation 0.909 0.701 of 369 1534 0.55 1513 1.18 1501 1.09 1.596 1428 926 2.072 0.750 1536 1533 No Observations Table 3.1.2 Descriptive statistic of distance to default of individual banks 57 Figure 3.1.3 Distances-to-default of individual banks BIDV SHB VCB CTG EIB MBB ACB STB Source: Authors’ calculation Figure 3.2.3 Weekly 1st difference in Distances-to-Default of individual banks ln(∆DD) BIDV SHB VCB CTG EIB MBB ACB STB Week equal to day 1/29/2016 and Week 319 equal to 11/30/2009 Source: Authors’ calculation 58 (Co-) exceedances for weekly log-differenced distance to defaults for individual banks (5% and 10% both tails) Statistics of (co-) exceedances 5% tails Number of (co-) exceedances in the Number of (co-) exceedances in the bottom tails top tails >6 0 >6 BID 2 69 73 0 1 SHB 2 30 30 1 VCB 2 30 30 1 CTG 2 30 29 1 EIB 2 2 10 27 28 1 MB 1 0 18 18 1 B ACB 2 10 29 29 2 1 STB 1 3 30 29 1 0 Table 3.4.2.2 Summary statistics of (co-) exceedances for weekly log-differenced distance to defaults for individual banks (5% tails) Statistics of (co-) exceedances 10% tails Number of (co-) exceedances in Number of (co-) exceedances in the the bottom tails top tails >6 0 >6 BID 0 67 70 1 1 1 SHB 4 11 29 28 13 10 2 VCB 5 10 28 28 12 6 2 CTG 3 11 27 27 12 9 2 EIB 4 11 26 27 MB 2 18 17 34 3 B ACB 3 29 28 10 2 STB 4 18 27 28 11 10 1 Table 3.4.2.3 Summary statistics of (co-) exceedances for weekly log-differenced distance to defaults for individual banks (10 % tails) 59 Source: Authors’ calculation Cross-table matrix of individual banks 5% both tails Number of times banks in 5% bottom tails BID SHB VCB CTG EIB MBB ACB STB Total BID 2 19 SHB 3 21 VCB 4 21 CTG 5 32 EIB 3 27 MBB 2 3 14 ACB 4 26 STB 3 3 26 Total 19 21 21 32 27 14 26 26 Table 3.4.3 Number of times banks in 5% bottom tails of individual banks Number of times banks in 5% top tails BID SHB VCB CTG EIB MBB ACB STB Total BID 2 SHB 3 4 20 VCB 2 2 18 CTG 3 19 EIB 3 16 MBB 1 11 ACB 4 27 STB 4 15 Total 20 18 19 16 11 27 15 Table 3.4.4.2 Cross-table for number of times banks in 5% top tails of individual banks Source: Authors’ calculation Cross-table matrix of individual banks 10% both tails BID SHB Number of times banks in 10% bottom tails BID SHB VCB CTG EIB MBB ACB STB Total 3 24 10 10 12 53 60 VCB 10 14 11 11 62 CTG 14 10 12 65 EIB 10 14 12 57 MBB 4 30 ACB 10 11 14 10 60 STB 12 11 12 12 10 65 Total 24 53 62 65 57 30 60 65 Table 3.4.4.1 Cross–table for number of times banks in 10% bottom tails of individual banks Number of times banks in 10% top tails BID SHB VCB CTG EIB MBB ACB STB Total BID 15 SHB 15 12 12 63 VCB 11 10 42 CTG 15 11 14 63 EIB 6 32 MBB 4 24 ACB 12 10 14 12 63 STB 12 12 42 Total 15 63 42 63 32 24 63 42 Table 3.4.4.3 Cross-table for number of times banks in 10% top tails of individual banks Source: Authors’ calculation Granger-Causality Banks BID SHB VCB CTG EIB MBB ACB STB BID SHB VCB CTG EIB MBB ACB STB Table 3.5 Pairwise Granger-Causality between Commercial banks of Vietnam December 2009 – January 2016 Source: Authors’ calculation 61 Correlation BANK BID SHB VCB CTG EIB MBB ACB STB BID 0.397 0.521 0.556 0.168 0.438 0.575 0.217 SHB 0.397 0.350 0.375 0.162 0.305 0.266 0.311 VCB 0.521 0.350 0.344 0.152 0.324 0.265 0.173 CTG 0.556 0.375 0.344 0.245 0.361 0.333 0.204 EIB 0.168 0.162 0.151 0.245 0.184 0.263 0.149 MBB 0.438 0.305 0.324 0.361 0.184 0.529 0.187 ACB 0.574 0.266 0.264 0.333 0.263 0.529 0.281 Table 3.3.1 Correlation of weekly 1st difference in distance to default Source: Authors’ calculation STB 0.2171 0.311 0.173 0.204 0.149 0.187 0.281 62 Appendix II Calculating the Distance-to-Default The distance-to-default (DD) measure is based on the structural valuation model of Black and Scholes (1973) and Merton (1974) The authors first drew attention to the concept that corporate securities are contingent claims on the asset value of the issuing firm.14 This insight is clearly illustrated in the simple case of a firm issuing one unit of equity and one unit of a zero-coupon bond with face value D and maturity T At expiration, the value of debt, BT, and equity, ET, are given by: (A.1) (A.2) VT is the asset value of the firm at expiration The interpretation of equations (A.1) and (A.2) is straightforward Bondholders only get paid fully if the firm’s assets exceed the face value of debt, otherwise the firm is liquidated and assets are used to partially compensate bondholders Equity holders, thus, are residual claimants in the firm since they only get paid after bondholders Note that equations (A.1) and (A.2) correspond to the payoff of standard European options The first equation states that the bond value is equivalent to a long position on a risk-free bond and a short position on a put option with strike price equal to the face value of debt The second equation states that equity value is equivalent to a long position on a call option with strike price equal to the face value of debt Given the standard assumptions underlying the derivation of the Black-Scholes option pricing formula, the default probability in period t for a horizon of T years is given by the following formula: (A.3) 14 Models built on the insights of Black and Scholes (1973) and Merton (1974) are known in the literature as structural models 63 Where N is the cumulative normal distribution, VT is the value of assets in period t, r is the risk-free rate, and σA is the asset volatility The numerator in equation (A.3) is referred to as distance-to-default An examination of equation (A.3) indicates that estimating default probabilities requires knowing both the asset value and asset volatility of the firm The required values, however, correspond to the economic values rather than the accounting figures It is thus not appropriate to use balance sheet data for estimating these two parameters Instead, the asset value and volatility can be estimated It is possible to solve the following equations (A.4) and (A.5) for the asset value and volatility: (A.4) (A.5) if Et, the value of equity; σ E , the equity price return volatility; and D, the face value of liabilities, are known; and d1 and d2 are given by: (A.6) (A.7) The first two parameters can be calibrated from market data: the value of equity corresponds to the market value of the firm, and the equity volatility corresponds either to historical equity volatility or implied volatility from equity options The last parameter, the face value of liabilities, D, is usually assumed equal to the face value of short-term liabilities plus half of the face value of long-term liabilities; the time horizon T is usually fixed at one year.41 Once the asset value and volatility are estimated, the default probability of the firm could be derived from equation (A.3) 64 65 Appendix III Calculating distance-to-default using Excel III.1 Calculating from Market data We can calculate the Volatility of Equity using following steps: First, we calculate the Equity rate of return using the Excel function: =ln(dayt/dayt-1) Then, we calculated the Market value of Equity using the Excel function = Stdev.S(Equity rate of return of 130 days)*sqrt(Stock exchange operating day per year) 130 days standard deviation is chosen as it equal moth of stock exchange, which is suggested in Gropp and Moerman (2004); Stock exchange operating day per year is set to be equal average of 252 days 66 Picture III.1 Calculating Volatility of Equity using Excel III.2 Calculating Distance to default The Distance to default can be calculated as: As we can see in the formula, the DD required the Market value of Asset Volatility of Asset and can be calculated according the following steps: and 67 First, we set and as variables After that, we use the Solver tool in Excel to solve for two above variables so that and are equal to the following function: With d1 and d2 equal to The input for above function can be found using market data: T: is set to be equal r: is set to be equal 5% Volatility of Equity: could be calculated using the method in Appendix III.1 Market value of Equity : can be calculate by multiply the stock price (days) to number of outstanding shares Debt: equal to Book Value of Debt As the Market value of Asset : is calculated in VND which is very big, usually in trillions, so the value of DD calculated by Excel sometime is not the absolute result However, the error is too small (around 10-7%) so it creditability is acceptable 68