Factors affecting bank liquidity risk in vietnam

Nội dung text: Factors affecting bank liquidity risk in vietnam

1. FACTORS AFFECTING BANK LIQUIDITY RISK IN VIETNAM Nguyen Thi Thu Trang1*- Cung Hong Lam - Pham Thi Trang ABSTRACT: The paper examines the factors impact on the bank’s liquidity risk for 12 Vietnamese commercial banks during the period from 2007 to 2017. The results show that there is a significant positive relationship between Return on Assets, loans to total assets, lending rate and liquidity risk ratio. In addition, non- performing loans have a significant negative relationship with bank liquidity risk. A group of solution is suggested in order to minimize the bank liquidity risk in Vietnam. Keywords: bank, liquidity risk, panel data 1. INTRODUCTION According to Basel (2008), “Liquidity risk is the risk that a financial entity which cannot fully find capital sources to fulfill obligations without affecting daily business activities and its financial situation”. Liquidity risk is the ability in which banks cannot perform financial obligations quickly, or they will have to mobilize additional funds with high expense or sell their assets with low price. During the global financial crisis, many commercial banks struggle with liquidity risk. That was the reason why Basel (2010) has paid more attention to liquidity risk. Specifically, Basel suggest to promote the ability to restore liquidity in the short term through specific regulations on Liquidity Coverage Ratio (High quality liquid assets/ Total net cash outflow over 30 days), and the long term through Net stable funding ratio (= available stable funding/ Required stable funding) In Vietnam, the most recent regulation related to liquidity risk in bank is Circulars 13/2018/TT-NHNN and Circulars 16/2018/TT-NHNN, in which commercial banks and other credit institutions have to maintain the strong liquidity management system. Understanding which factors affecting the liquidity risk is, there for, play an important role for commercial banks. In this paper, we examine the determinants impact the liquidity risk in commercial banks of Vietnam from 2007 to 2017. The next section shows the literature review, following the section 3 and 4 presents the research methodology and results. The last sections is about conclusion and recommendations. 2. LITERATURE REVIEW The study analysis about factors affecting liquidity risk in bank usually consists of two groups: (i) micro factors which are bank-specific characteristics and (ii) macro factors which are macroeconomic determinants. Macro factors Economic growth: Economic growth affects the entire economy and society, in which the banking * Banking Academy, No.12 Chua Boc, Hanoi 100000, Vietnam, Corresponding author. Tel.: +84969110287, E-mail address: trangntt@hvnh.edu.vn , Banking Academy, No.12 Chua Boc, Hanoi 100000, Vietnam.
2. 482 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA sector is affected immensely powerful. In theory, during the economic recession, banks will tend to reserve much more liquidity and limit loans (Rauch et.al (2010), Vodova (2013). In contrast, in the period of economic growth, banks will reduce the liquidity reserves and have more loans to make profit. Higher returns come with higher risk, so the economic growth and liquidity risk direct proportion (Chung-Hua Shen et.al , 2009) Thanks to the economic growth ,the system of Bank’s liquidity assets increase significantly. That also is found in the study of Lowe (2002), Garr (2013). Inflation: Fisher’s model (1930) defines market rate which includes real interest rate and expected inflation. Inflation rate affects division financial resources efficiently. Chung Hua Shen et al (2009) indicates that inflation rises, the cost of borrowing fell. This is beneficial to the borrower rather than the lender because the borrower will make profit due to increase of the price of goods which are bought by borrowing money. According to Vodova study (2013), the change of inflation rate affects in the same direction with liquidity risk. Castro (2013) agrees with this view. He said that when inflation rises, although real interest rates that borrowers have to bear in this case reduced, their real incomes are reduced, the solvency of customers also reduce. Some other views also show similar correlations, such as Garr (2013), Mkukwana (2013) Interest rate: Commercial banks set up their borrowing and lending interest rate, but it fluctuates in a certain range. This is considered one of the factors influence on liquidity risk. If basic rate increases, it makes lending rate increase, volume of loan would reduce. That can decrease liquidity risk but not be good for profit target of banks. According to Lucchctta(2007), and Wilbert (2014) interest rate has significant influence to the Bank’s liquidity supply. Fadarc (2011) after researching liquidity risk and the financial crisis of Nigieria shows that the factor which affects liquidity risk is interest rate. So the interest rate and bank’ liquidity has the same dimensional relationship. This relationship is also found in the study of the Boss (2006), Folawewo and Tennant (2008), Ramlall (2009). Micro factors The size of bank: when the bank holds a large amount of assets, the bank will active in all activities of receiving deposits, loans. At that time, the amount surplus will always be safe high liquidity. In addition, large banks often receive the trust of other banks, so they can take advantage in the interbank market or received supports from the last lenders (Delechat et al 2012). The bigger the bank, the better it gets. On the other hand, the “too big to fail” argument gives the impression that large banks, due to their implicit assurances, can mitigate the cost of raising capital and that allows them to invest in riskier assets. Therefore, the mobilized capital decreased and the total assets decreased, that lead to liquidity risk, bank size has oppositely effect on the liquidity of banks. However, from a different point of view, the size of the bank has not affected liquidity risk. This is also evidenced by Valla’s study with Vodová (2013) who argue that size does not have a significant impact on liquidity risk Credit concentration: Lending is one of the most important operations of commercial banks. These loans also often provide more returns than other assets such as investing in securities. However, they are potential risky. Holding a large amount of loans will increase the risk that a bank may face, the risk may be credit risk, liquidity risk and some other specific risks. As a result, bank’ liquidity is also affected by the lending ratio. Loans are double-edged sword coexist in the banking system. (Aspachs et al 2005; Lucchetta, 2007). Credit risk: The main activity of the bank is to receive and lend the money from customers in order to make a profit. So if a loan falls into a state of insolvency, it means the rotation of the bank activities will delay at a point and greatly affect the total capital of the bank. According to Duttweiler (2009), in order to
3. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 483 maintain liquidity, on the one hand, commercial banks must ensure that the total value of assets is greater than the debts at all times. If loans is not able to recover and losses in the securities business, it will make the value of the property to be lower than the debt and lead to bank insolvency, may must close or sell assets to another bank. Some studies of Iqbal (2012); Vong and Chan (2009) showed the same relationship between NPL and liquidity risk. The bigger the NPL are, the more liquidity risk are. That require commercial banks to minimize NPL. In addition, some studies that suggest that there is a negative relationship between credit risk and liquidity risk Raghavan (2003) Cai and Thakor (2008), Wagner (2007). Profitability of the bank: Profitability is a very important tool in evaluating the banking busines. It is also a factor which would effect on bank’ liquidity. Basing on the theory of “expected bankruptcy costs” (Berger, 1995), a low-interest bank would prioritize lending to make profit, thus making total asset particularly high liquidity assets are declining. Conversely, if the bank has high profitability, it will limit excessive credit growth to invest in high liquidity assets (Bunda and Desquilbet, 2008). Thus, the higher the profitability rate is, the greater the value of liquidity assets is. Profitability ratios are often used as ROA and ROE. ROA is a measurer in managing and using of asset to make profit. ROE measures of profitability on the equity of a bank. 3. RESEARCH METHODOLOGY The aim of the research is to examine the model and evaluate the factors affecting the liquidity risk of commercial banks in Vietnam. Based on the theory, the researchers have applied and extended previous researches, and use of variables in accordance with the commercial banks in Vietnam. Research suggests the following model: LDRi,t = β0 + β1.SIZEi,t + β2.ROAi,t + β3 .TLAi,t + β4 .NPLi,t + β5 .GDPi,t + β6.INFi,t + β7.RATEi,t In which: (Loan to Deposit Ratio): dependent variables which is the index measuring liquidity risk of commercial banks, calculated as the ratio of loans to short-term deposit. If the ratio is too high, it means that the bank may not have enough liquidity to cover any unforeseen fund requirements. Conversely, if the ratio is too low, the bank may not be earning as much as it could be. Based on the collected data and the characteristics of the commercial banks in Vietnam, the researchers examined the impact of two groups of factors: (i) The micro-factors group characterized by individual banks: size of total assets (SIZE), the ratio of loans to total assets (TLA), Return on Assets (ROA), the non-performing loan ratio (NPL); (ii) The macro-factors group including: economic growth (GDP), inflation (INF), the average lending rate (RATE). Table 1 show the specific terms of content, calculations and expected relationship between factor and liquidity risk. Table 1. Description of the variables used in the model and how to measure Expected Variables Symbol Description sign Dependent variable = loan/(deposits from customers + deposits from 1 Loan to deposit ratio LDR other credit institutions) Independent variable 2 Size of total assets SIZE = Logarit(total asset) -
4. 484 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA The ratio of loans to total = Loans (customers + other credit institutions) 3 TLA + assets / Total assets 4 Return on Assets ROA = Net income / Total assets + 5 Non-performing loan ratio NPL = Total non-performing loans / Total loans - 6 Economic growth GDP + 7 Inflation INF - 8 The lending rate RATE +/- The micro-economic data was collected from the financial reports, the annual reports of 12 commercial banks in Vietnam in the period 2007-2017. The macro data was collected from official sources and credibility from the General Statistics Office of Vietnam, Ministry of Finance, The State Bank of Vietnam for the period of the study 2007-2017. The regression analysis using panel data can use three models to analyze that is: First, the Pooled OLS model: the model is not in control of each specific characteristics of each of the banks in the research. In fact, this is not reasonable because each bank has their-own characteristics and the situation is different year by year. Second, FEM (Fixed Effects Model): is developed from Pooled OLS model when there is more control over each special omen between different banks, and there is correlation between residual part of the model and the independent variables. Third, REM (Random Effects Model): is the random effect model, also developed from the tissue Pooled OLS ordered and when to have more control over each of the different characteristics of the banks, but there is no correlation between residual part of the model and the independent variables established. 4. RESULTS Descriptive statistics Table 2 - summarizes statistical information about the variables used in the model. The rate of LDR from 2007 to 2017 is 64.88% with a standard deviation is 17.72%. The rate of average liquidity risk is 64.89% with the smallest value is 18.84% and the maximum value is 99.59%; nevertheless liquidity risk difference between the banks and the years are not greater with standard deviation is 17.7%. In addition, based on the table, we can also have the general assessment of the independent variables - the factors affecting the liquidity risk of the bank through the parameters of the average value, standard deviation and fluctuating margin. Table 2.Descriptive statistics of the variables Variable Obs Mean Std. Dev. Min Max LDR 132 64.8854 17.72058 18.84 99.59184 SIZE 132 8.143035 0.4829366 6.383572 9.080007 ROA 132 1.111643 0.9748852 -5.99 5.12 TLA 132 51.84967 14.1514 10.59 74.65 NPL 132 2.037879 1.090488 0.08 5.35 GDP 132 6.276364 0.8917769 5.23 8.48 INF 132 8.410909 6.11604 0.63 19.9 RATE 132 11.22727 3.097231 7.6 17
5. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 485 Correlation and multicollinearity Table 3 showed the correlation matrix between pairs of variables used in the model, indicating these variables have relatively loose correlation with absolute value from 0.0207 to 0.4174, except the correlation between pairs of dependent variable TLA and independent variables LDR is 0.8983. According to La Porta et al (2002) when the correlation between the independent variables exceeds 0.9, the model was capable of multi-collinear defects. Such models did not concern the research on the phenomenon of multi-collinear. Table 3.Pearson correlation matrix between the variables in the model LDR SIZE ROA TLA NPL GDP INF RATE LDR 1.0000 SIZE 0.2932 1.0000 ROA 0.0132 -0.2025 1.0000 TLA 0.8983 0.4174 -0.0822 1.0000 NPL -0.2793 -0.1935 -0.1159 -0.2371 1.0000 GDP -0.0207 -0.1835 0.1961 0.0246 -0.0747 1.0000 INF -0.0401 -0.3816 0.2503 -0.2742 0.0313 0.1627 1.0000 RATE -0.0418 -0.2993 0.1411 -0.3092 0.0505 -0.1253 0.8947 1.000 VIF (variance inflation factor) is used to examine the phenomenon of autocorrelation coefficient in the model. VIF is an indicator which used to to test the multicollinearity phenomenon of regression equation. If VIF > 10, multicollinearity phenomenon will occur. Test results showed that the coefficient of the VIF are smaller than 10 should the phenomenon multicollinearity in the model is assessed as not serious. Table 4.Test multicollinearity phenomenon Variable VIF 1/VIF INF 8.69 0.115094 RATE 8.38 0.119356 GDP 1.68 0.595693 SIZE 1.43 0.700718 TLA 1.33 0.754282 ROA 1.15 0.872452 NPL 1.11 0.900998 Mean VIF 3.39 So we could conclude the majority of the variables in the model did not have the multicollinearity phenomenon with each other and this will be a positive sign in the testing and selection of appropriate econometric model Results of research model Regression includes 3 models: Pooled OLS, FEM and REM. According to the results of regression model of OLS the model explained was 87.15% fluctuation of the liquidity risk of 12 banks. However, to increase the fit of the model as well as reviews are cross-impact of the variables of time (year) and object (bank) need to use regression analysis to the effect fixed (Fixed effect model-FEM) or random (Random effects model-REM). This method has been applied in the study of the EbruCaglayan et al (2010). Regression results according to FEM and REM are outlined in table 5 Table 5. Regression Results of Pooled OLS, FEM and REM models Regression Results LDR OLS FEM REM SIZE -2.2532 -5.2701 -2.2255 ROA 0.0974 0.6614 1.0000 TLA 1.1236 0.9893 1.2194
6. 486 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA NPL -1.0683 -0.8584 -0.9959* GDP -0.4721 -0.7139 -0.4746 INF -0.1913 -0.2379 -0.1921 RATE 1.6984 1.2916 1.6741 C 5.7486 49.5512 6.7413* R2 87.84% 86.71% 87.84% F-test prob(F-statistic) =0.000 prob(F-statistic) =0.000 prob(F-statistic) =0.000 Note: * ; and mean significant respectively at level 10%, 5% and 1% Source: Regression with Stata Hausman-test is applied to test which model, the FEM or REM estimate method is more suitable. When running the test shows the Prob> chi2 = 0.4599 greater than 0.05 so Random effects model (REM) more suitable than Fixed effect model (FEM). So the model regression model is as follows: LDR = 6.7413 - 2.2255*SIZEi,t + ROAi,t + 1.2194*TLAi,t - 0.9959*NPLi,t -0.4746*GDPi,t - 0.1921*INFi,t + 1.6741*RATEi,t 5. CONCLUSION AND RECOMMENDATION According to the results obtained from the Random effects model (REM) in Table 5, there are 4 factors that impact significantly on the rate of liquidity risk (LDR) of the bank, which is factors: Return on Assets (ROA); The ratio of loans to total assets (TLA); Non-performing loan ratio (NPL) and interest rates (RATE) impact on liquidity risk of the bank. Return on Assets (ROA) have an impact in the same way to LDR of commercial banks with the significance level of 10%, while the higher profitability ratio, the greater liquidity risk. Relationship so the same conclusion, the research of Valla and Escorbiac (2006) and Vodova (2013). In contrast to a research point of Bonfim and Kim (2008), Bui Nguyen Kha (2016) and Vu Thi Hong ( 2011). Delechat et al (2012) explain this, because the bank’s profitability is high, often tend to be less hold liquid assets, they can easily find sources other liquid when need. Thus, the liquidity risk of banks higher. Besides the high liquidity assets often have low profitability ratio should remain low liquidity assets also helped banks have higher profitability. Therefore, Vietnamese commercial banks should strengthen the profitability by determine the reasonable size and structure of assets – liabilities. The ratio of loans to total assets (TLA) with LDR positive impact with the significance level of 10%. Thus, the ratio of loans to total assets increased, the liquidity risk of the bank also increased. Bonin and associates (2008) suggest that when holding an amount of lending to customers is too large will cause the bank at risk when these withdrawals big money and unpredictable while loans do not promptly final settlement, which lead to the loss of liquidity of banks. Indriani (2004), Angora and Roulet (2011) agrees on the positive relationship between the ratio of loans to customer with the liquidity risk of banks. NPL ratio (NPL) have inverse ratio relationship with the liquidity risk ratios with the significance level of 10%. This relationship is consistent with research by Vodova (2011) and Vu Thi Hong (2012) suggested that between NPL and liquidity risk relationship is inversely proportional. The reason is that banks NPL ratio high will tend to move into assets with high liquidity because preventing the worst case occur, the bank still has assets of high liquidity to meet needs payment. This conclusion is the same point with Raghavan’s research (2003) Cai and Thakor (2008), Wagner (2007). Because lending is a major activity and accounts for a large proportion in Vietnamese commercial banks, so the bank should manage the credit portfolio which is fit with their own risk appetite.
7. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 487 The average lending rate (RATE) impact covariates with the liquidity risk of banks with significant levels of 0.001% for the loan rate on deposits. This result is consistent with the conclusions of the research groups of Valla and Escorbiac (2006), Vodova (2013), Boss et al(2009), Folawewo and Tennant (2008), Ramlall (2009). They indicated the same direction relations between the liquidity of banks with the interest rate of the general economy and in particular interest rates. When interest rates rise, the cost of borrowing increased and increasing of the bad debt, affecting the ability to recover a debt from which increases the liquidity risk of banks. Therefore, maintaining the interest rate at a reasonable level and commercial banks should also act more actively in both market I and II which will stabilize bank liquidity in Vietnam. 6. CONSTRUCTION OF REFERENCES Please ensure that every reference cited in the text is also present in the reference list (and vice versa). Any references cited in the abstract must be given in full. References must be listed at the end of the paper. Do not begin them on a new page unless this is absolutely necessary. Authors should ensure that every reference in the text appears in the list of references and vice versa. Indicates references by (Hair and alii, 2006)or [1] in the text. Some examples of how your references should be listed are as follows: BOOK: Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New Jersey: Pearson Prentice Hall. Journals: Basu, S., Waymire, G.B., (2006). Recordkeeping and human evolution. Accounting Horizons, 20 (3), 201–229. REFERENCES Angora, A. and Roulet, C., (2011). Transformation Risk and its Determinants: A New Approach Based on Basel III Liquidity Management Framework. Arif A., and Anees A. N., (2012). Liquidity Risk and Performance of Banking System. Journal of Financial Regulation and Compliance, Vol.20, Iss: 2, p.182-195 Aspachs, O., Nier, E., Tiesset, M., (2005). Liquidity, Banking Regulation and Macroeconomics. Proof of shares, bank liquidity from a panel the bank’s UKpresident, Bank of England working paper. Berger, Allen N., Anil K Kashyap, and Joseph M. Scalise, (1995). The transformation of the U.S. banking industry: What a long, strange trip it’s been. Brookings Papers on Economic Activity 2: 55-218. BIS (2008). Principles for sound Liquidity Risk Management and Supervision, Basel: Bank for International Settlements. ISBN 92-9197-767-5 BIS (2009). International framework for liquidity risk measurement, standards and monitoring. BIS (2010). Basel III: International framework for liquidity risk measurements, standard~ and monitoring, Basel Committee on banking Supervision, 1-48 Bui Nguyen Kha (2016). Determinants the Vietnamese commercial liquidityrisk.Finance Journal, 03/09/2016 Bunda, I. and J. B. Desquilbet, (2008). The Bank Liquidity Smile across Exchange Rate Regimes. International Economic Journal, 22(3), 361-386. ISSN 1743-517X Bonfim, D., Kim, M., (2008). Liquidity Risk in Banking: Is there herding? International Economic Journal, Vol. 22, No. 3, p. 361-386. Boss, M., Krenn, G., Puhr, C., Summer, M., (2006). Systemic risk monitor: a model for systemic risk analysis and stress testing of banking systems. Financial Stability Report, 1(June), OesterreichischeNationalbank, 83-95 Cai J., Thakor A. (2008). Liquidity Risk, Credit Risk and Interbank Competition. SSRN Electronic Journal Castro V. (2013). Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Economic Modelling, Vol. 31, pp. 672-683
8. 488 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA Chung-Hua-Shen et al., (2009). Bank Liquidity Risk and Performance, working paper. Delechat, C., Henao, C., Muthoora, P., and Vtyurina, S., (2012). The Determinants of Banks’ Liquydity Buffers in Central America, IMF Working Paper Series, WP/12/301 Duttweiler, R. (2009). Managing Liquidity in Banks. Wiley, Singapore EbruÇağlayan and NazanŞak (2010). The Determinants of Capital Structure: Evidence from the Turkish Banks. Journal of Money, Investment and Banking, Issue 15 (2010), 57-65, ISSN 1450-288X Fadare, S.O. (2011). Banking sector liquidity and financial crisis in Nigeria. International Journal q/ Economics and Finance, 3(5), 3-11 Fisher, R.A. (1930). The genetically theory of natural selection, Clarendon Press, Oxford. Folawewo, A.O., & Tennant, D. (2008). The Determinants of Interest Rate Spreads in Sub-Saharan African Countries: A Dynamic Panel Analysis. Pretoria, South Africa: Annual African Econometrics Society Conference. Garr D. K. (2013). Determinants of credit risk in the banking industry of Ghana. Developing Country Studies, 3(11). Ghosh, S. (2010). Credit Growth, Bank Soundness and Financial Fragility: Evidence from Indian Banking Sector. South Asia Economic Journal.11(1), 69-98. ISSN 1391-5614 Indriani, V., (2004). The relationship between Islamic financing with risk and performance of commercial banks in Indonesia. Bachelor of Accounting University of Indonesia Iqbal, M. J., (2012). Banking Sector’s Performance In Bangladesh An ApplicationOf Selected Camels Ratio. Asian Institute of Technology, School of Management, Thailand, La Porta, R., F. L´opez-de-Silanes, A. Shleifer, and R. W. Vishny (2002). Investor protection and corporate valuation. Journal of Finance 57 (3), 1147–1170 Lucchetta, M.(2007). What do data say absout monetary policy, Bank Liquidity and Bank risk taking?”.Economic Notes by Banca Monte deiPaschi di Siena SpA, 36(2), 189-203 Mkukwana, K. (2013). The impact of macroeconomics factors on the risk of default: the case of residential mortgages. (Doctoral Dissertattion) Peter S. Rose (2004). Commercial Bank Management. ISBN-13: 978-0256152111 Raghavan, R.S. (2003). Risk Management in Banks. The Chartered Accountant, February Ramlall, I. (2009). Determinants of Capital Structure Among Non-Quoted Mauritian Firms Under Specificity of Leverage: Looking for a Modified Pecking Order Theory. International Research Journal of Finance and Economics, 31, pp. 83-92 Rauch, C., Steffen, S., Heckethal, M. and Tyrell, C, (2010). Determinants of bank liquidity creation. Journal of Money Investment and Banking, 10, 145-176. Valla, N. and B. Saes-Escorbiac, (2006). Bank liquidity and financial stability. Banque de France Financial Stability Review. pp. 89-104 National Bank of Romania Vu Thi Hong, (2012). Determinants the Vietnamese commercial liquidity risk. Vodová.P, (2013). Determinants of Commercial Banks’ Liquidity in Hungary and Poland. working paper Vong, A., & Chan, H. (2009). Determinants of Bank Profitability in Macau. Macau Monetary Research Bulletin, 12, pp. 93-113 Wagner W. (2007). The liquidity of bank assets and banking stability. Journal of Banking & Finance, Vol. 31, No. 1, pp. 121-139 Wilbert, C. (2014). Zimbabwean Commercial Banks Liquidity and Its Determinants,. lntemationalJoumal of Empirical Finance, 2(2). 52-64