Structure of Banks' Balance Sheet and Systemic Risk of Banking Network

Document Type : Research Paper

Authors

1 Ph.D Student of Accounting, Kish International Branch, Islamic Azad University, Kish Island, Iran.

2 Assistant Professor of Accounting, Central Tehran Branch, Islamic Azad Univercity, Tehran, Iran.

3 Assistant Professor of Accounting, South Tehran Branch, Islamic Azad Univercity, Tehran, Iran.

10.22103/jak.2020.15392.3203

Abstract

Objective: The purpose of this paper is to estimate the systemic risk of the banking industry, considering the structure of banks' balance sheets in the interbank money market. To do this research, it develops a network model based on banks' balance-sheet interdependence to analyze how the liquidity risk in a bank moves to other banks. The model shows that liability of the typical bank is an asset in the balance sheet of other banks. When a borrower bank runs out of liquidity and fails to commit to settling its debts in the interbank money markets, lenders run short and reduce their activities in the interbank money market, leaving other banks with liquidity constraints. In fact, a crisis in one bank moves to other banks, which may pose a risk of transmission. In this paper, to measure the systemic risk, the liquidity index in the interbank money market is introduced that is the weighted average of banks' liquidity position. We use daily transactions in the interbank money market from 2013 to 2019 and apply Adrian and Brunnermeier (2016) ∆CoVaR measure and quantile regression to estimate the systemic risk.
Method: The method of collecting data includes document information and record data from interbank money market transactions extracted from the central bank website. In this study, the sample consists of daily transactions of reciprocal deposits of 32 active banks in the interbank money market between November 2013 and July 2019. The interbank money market started its operation in 2009 with two banks. The volume of interbank transactions has increased significantly in the last decade so that in 2019 the volume of transactions is 2.4 times the total liquidity of the economy. The method of data analysis is a quantitative one using E-views software and MATAB software, respectively. To estimate △CoVaR, we first considered two Quantile regression models for 95 and 50 percent Quantiles using ordinary least squares (OLS). All parameters were statistically significant. To estimate the VaR of the stock market and banking system liquidity indices, we first considered the conditional variances of these two indices using the GARCH family models. We estimated different GARCH models and the best model fitted to the data. In the next stage, we used estimated conditional variances to estimate conditional VaR(CoVaR) for two different 95 and 50 percent quantiles based on the estimated parameters of quantile regressions. At the last phase, we calculated △CoVaR based on estimated CoVaR for two quantiles.
Findings: The results of the research showed that the null hypothesis of normality of distribution of both stock market index and liquidity status of the interbank money market is rejected based on Jarque-Bera, Andersen-Darling, and Cramér–Von Mises statistics. Dickey-Fuller and Philips-Peron unit root test results showed that both variables are stationary. The results also indicated that the distribution of ∆CoVaR is not normal. Based on the average of ∆CoVaR,  in the case of a banking system liquidity crisis, the stock market index will decrease by 2081 units per day on average. The maximum decrease in the stock market is 7088 units in the case of a banking crisis.
Concluding: The research result indicates the interdependent structure of banks' balance sheets in the interbank money market. According to the results, the central bank, as a market monitoring authority, should control daily transactions in the interbank money market. It also should take the structure of banks' balance sheet interdependence into consideration in the estimation of the banking system systemic risk.

Keywords

References

ابریشمی، حمید، مهرآر،ا محسن؛ رحمانی، محمد. (1397). اندازه‌گیری و تحلیل ریسک سیستمی در بخش بانکداری ایران و بررسی عوامل مؤثر بر آن. مدل‌سازی اقتصاد سنجی، 4(3)، 36-11.
اخگر، محمدامید؛ جلوزان، ثریا. (1394). بررسی اثر هموار‌سازی سود بر ریسک غیر سیستماتیک شرکت‌های پذیرفته‌ شده در بورس اوراق بهادار تهران. دانش حسابداری، 6(20)، 145-123.
بکی حسکویی مرتضی، صمدی ساناز. (1391). برآورد ریسک پرتفوی سرمایه‌گذاری با استفاده از مدل‌های کاپولای شرطی. سومین کنفرانس ریاضیات مالی و کاربردها، دانشگاه سمنان.
دستخوان، حسین؛ شمس قارنه، ناصر. (1396). مقایسه شاخص‌های ارزیابی ریسک سیستمی در شبکه‌های مالی: شناسایی شرکت‌های مهم از نظر سیستمی در بازار بورس تهران. مدل‌سازی ریسک و مهندسی مالی، 2(1)، 21-1.
شیرمحمدی، فاطمه؛ چاوشی، کاظم. (1394). بررسی ریسک سیستمی نظام مالی ایران. پایان‌نامه کارشناسی ارشد، دانشگاه علوم اقتصادی، دانشکده مدیریت نهادهای اقتصادی.
صادقی شریف، سید جلال؛ سوری، علی؛ استادهاشمی، علی. (1397). مدلسازی و برآورد ریسک سیستم بانکی در قالب یک مدل شبکه‌ای با استفاده از سنجهCoVaR. پژوهش‌های پولی و بانکی، ۱۱(۳۶)، 210-183.
فلاح‌زاده ابرقویی، احمد؛ تفتیان، اکرم؛ حیرانی، فروغ. (1398). بررسی رابطۀ افشای اطلاعات با همزمانی قیمت سهام و ریسک سقوط آن با استفاده از سیستم معادلات همزمان. دانش حسابداری، 10(1)، 194-169.
فرزین‌وش، اسدالله؛ الهی، ناصر؛ گیلانی‌پور، جواد؛ مهدوی، غدیر. (1396).ارزیابی ریسک سیستمی در شبکه بانکی ایران توسط معیار تغییرات ارزش در معرض خطر شرطی. مهندسی مالی و مدیریت اوراق بهادار، 8(33)، 281-265.
References
Abrishami, H., Mehrara, M., Rahmani, M. (2019). Measuring and analysis of systemic risk in iranian banking sector and investigating its determinants. Journal of Econometric Modeling, 4(3), 11-36 [In Persian].
Acharya, V., Engle, R., R.chardson, M. (2012). Capital shortfall: A new approach to ranking and regulating systemic risks. The American Economic Review, 102(3), 59-64.
Acharya, V., Engle, R., Richardson, M. )2012(. Capital shortfall: A new approach to ranking and regulating systemic risks. American Economic Review, 102(3), 59-64.
Acharya, V., Pedersen, L., Philippon, T., Richardson, M. (2009). Regulating systemic risk. In restoring financial stability: How to repair a failed system, Wiley.
Acharya, V., Pederson, L., Philippon, T., Richardson, M. (2010). Measuring systemic risk. Working Paper, Federal reserve bank of Cleveland.
‎Acharya, V., Engle, R., Richardson, M. (2012). Capital shortfall: A new approach to ranking ‎and‏ ‏ regulating systemic risks. The American Economic Review, 102(3), 59-64.‎
Acharya, V.V., Pedersen, L.H., Philippon, T., Richardson, M.P. )2010(. Measuring systemic risk. The Review of Financial Studies, 30(1), 2–47.
Acharya, V.V., Richardson, M. (2009(. Causes of the financial crisis. Critical Review, 21(2-3), 195–210.
Adrian, T., Brunnermeier, M. (2009). CoVaR. paper presented at the CEPR/ESI 13th annual conference on financial supervision in an uncertain world in Venice. Staff report 348, Federal reserve bank of New York.
Adrian, T., Brunnermeier, M. (2016). CoVaR. American Economic Review, 106(7), 1705-1741.
Adrian, T., SongShin, H. (2009). Liquidity and leverage. Journal of Financial Intermediation, 19(3), 418-1437.
Akhgar, M.O., Jelvezan, S. (2015). Effect of income smoothing on ‎unsystematic risks of companies listed in Tehran Stock Exchange. Journal of Accounting ‎Knowledge, 6(20), 123-145 [In Persian]‎.
Allen, F., Gale, D. (2000a). Financial contagion. The Journal of Political Economy, 108(1), 1–33.
Allen, F., Gale, D. (2000b). Bubbles and crises. The Economic Journal, 110(460), 236–255.
Anderson, T.W., Darling, D.A. (1952). Asymptotic theory of certain goodness of fit criteria ‎based on stochastic processes. Annals of Mathematical Statistics, 23, 193–212.‎
Anderson, T.W., Darling, D.A. (1954). A test of goodness of fit. Journal of the American ‎Statistical Association, 49, 765–769.‎
Baky Haskuee, M., Samadi, S. (2012). A copula-GARCH approach to estimation of investment portfolio risk. A Paper Presented at 3rd  Conference on Mathematical Finance and Applications, Semnan University [In Persian].
Bernal, O., Gnabo, J.Y., Grégory, G. (2014). Assessing the contribution of ‎banks, insurance and other financial services to systemic risk. Journal of Banking & Finance, 47, 270-287‎.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.‎
Bolton, P., Santos, J., Scheinkman, J. (2011). Inside and outside liquidity. Quarterly Journal of Economics, 126, 259–351.‎
Caballero, R., Krishnamurthy, A. (2008). Collective risk management in a flight to quality episode. Journal of Finance, 62, 2275–302.‎
Darling, D.A. (1957). The Kolmogorov–Smirnov, Cram'er–von Mises tests. Annals of Mathematical Statistics, 28(4), 823–838.‎
Dastkhan, H., Shams Gharneh, N. (2017). Systemic risk measures in financial markets: Identifying the systemically important companies in Tehran Stock Exchange. Journal of Risk Modeling and Financial Engineering, 2(1), 1-21 [In Persian].
Diamond, P., Rajan, R. (2011). Fear of fire sales, illiquidity seeking and credit freezes.‎ Quarterly Journal of Economics, 126, 557–91.‎
Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. Inflation. Econometrica, 50, 987–1008.‎
Fallahzadeh Abarghouhei, A., Taftiyan, A., Heirany, F. (2019). Relationship between information disclosure with the stock price synchronicity and crash risk of falling stock prices within simultaneous equations system. Journal of Accounting Knowledge, 10(1), 169-194 [In Persian].
Fique, J., Page, F. (2013). Rollover risk and endogenous network dynamics. Computer Management Science, 10, 213–230.
Gorton, G., Metrick, A. (2010). Regulating the shadow banking system. Brookings Papers on Economic Activity, 41(2), 261–292.‎
Haldane, A., May, R. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), 351–355.
Haldane, A.G. (2009). Rethinking the financial network. Bank of England, speech given by the executive director, financial stability, bank of England at the financial student association, Amsterdam.
Heider, F., Horiova, M., Holthausen, C. (2009). Liquidity hoarding and interbank spreads: the role of counterparty risk. Journal of Financial Economics, 118, 336-354.‎
Jabbour, C.J.C. (2013). Environmental training in organizations: From a literature review to a framework for future research. Resources, Conservation and Recycling, 74(1), 44–155.
Jarque, C.M., Bera, A.K. (1980). Efficient tests for normality, heteroscedasticity and serial independence of regression residuals. Economic Letters, 6, 255–259.‎
Jarque, C.M., Bera, A.K. (1987). A test for normality of observations and regression residuals. International Statistical Review, 55(2), 163–172.‎
Jorion, P. (2001). Value at risk: The new benchmark for measuring financial risk. McGraw-Hill, New York, 2nd edition.
Kim B.H., Kim. S. (2013). Transmission of the global financial crisis to Korea. Journal of Policy Modeling, 35, 339–353.
Lage-Junior, M., Godinho-Filho, M. (2010). Variations of the Kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13–21.
‎Lage-Junior, M., Godinho-Filho, M. (2010). Variations of the Kanban system: ‎Literature ‎review and classification. International Journal of Production Economics, 125 ‎‎(1), 13–21.‎
Li, S., Lu, Y., Wu, Ch. (2019). Systemic risk in bank-firm multiplex networks. Finance Research Letters,In press, corrected proof Available online.
Liu, X.F., Tse, C.K. (2012). Dynamics of network of global stock market. Accounting and Finance Research, 1, 1-12.
Farzinvash, A., Elahi, N., Gilanipour, J., Mahdavi, G. (2017). The ‎evaluation of systemic risk in the iran banking system by delta conditional value at risk (‎CoVaR) criterion. Financial Engineering and Portfolio Management, 8(‎‎33), 265-281 [In Persian]‎.
Mantegna, R.N. (1998). Hierarchical structure in financial markets, the European physical. Journal B - Condensed Matter and Complex Systems, 11, 193-197.
Pasquariello, P. (2002). Imperfect competition, information heterogeneity and financial contagion, Working Paper.
Sadeghi Sharifi, S.J., Souri , A., Ostadhashemi, A, (2018). Modeling and estimating the risk of the banking system in form of a network model using CoVaR, Journal of Monetary and Banking Research, 11(36), 183-210 [In Persian].
Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4), 1513–1520.
Shirmohammadi, F., Chavoshi, K. (2015). Investigating the systemic risk of the Iranian financial system. Master Thesis, University of Economic Sciences, School of Management of Economic Institutions [In Persian].
Silva, T.C., Souza, S.R.S.D., Tabak, B.M. (2016). Network structure analysis of the Brazilian interbank market. Emerging Markets Review, 26(1), 130-152.
Stephens, M.A. (1970). Use of Kolmogorov–Simonov, Cramer–von mises and related statistics without extensive tables. Journal of the Royal Statistical Society, 32, 115–122.‎
Stiglitz, J.E., Weiss, A. (1981). Credit rationing in markets with imperfect information. The American Economic Review, 71(3), 393–410.
Zhou, C. (2010). Are banks too big to fail? Measuring systemic importance of financial institutions. International Journal of Central Banking, 6(4), 205–250.
Zhou, C. (2013). The impact of imposing capital requirements on systemic risk. Journal of Financial Stability, 9(3), 320–329.
Zhou, Ch., Tarashev, N. (2013). Looking at the tail: Price-based measures of systemic importance. BIS Quarterly Review, 2013, 47-61.
Zhu, K., Liu, D., Wu, J., Sun, L. (2012). The research of the regional financial risk early-warning model integrating the regression of lagging factors. AASRI Procedia, 1(1), 428-434.
Journal of Accounting Knowledge
Volume 11, Issue 3 - Serial Number 42
December 2020
Pages 195-225

Files

History

  • Receive Date: 18 February 2020
  • Revise Date: 23 March 2020
  • Accept Date: 19 May 2020

Share

How to cite

Statistics