Liquidity Risk Management in Modern Interbank Payment Systems

Subject Areas : Journal of Investment Knowledge

rassol khoshbin ¹ , Farzin Rezaei ² , Mohammad Ali Rastegar ³

1 - PhD Student of Accounting, Department of Accounting, Qazvin Branch, Islamic Azad University of Qazvin, Iran
2 - Associate professor of IAU, Department of accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - Assistant Professor, Department of Financial Engineering, School of Industrial and Systems Engineering, Tarbiat Modarres University, Tehran

Received: 2019-12-29 Accepted : 2020-02-10 Published : 2022-09-23

Keywords: Liquidity Buffer, Over Threshold Method, Interbank Payment Systems, Risk Appetite, Liquidity Risk Management,

Abstract :

In this study, in order to measure the liquidity risk in interbank payment systems, the time series of daily data balances of an Iranian bank's payment systems from 01/01/94 to 31/5/98 and then We examined stationary time series with Dickey Fuller and Philips Peron tests and compared the expected value and risk value of payment systems data with the historical method and compared with the Pareto method. The results of the Kopik and Christofferson tests showed that Pareto's generalized approach to better manage banks' liquidity risk is better than historical method based on daily data of payment systems. The bank can then provide liquidity management operations to manage the liquidity risk in the payment system

References:

• خشنود، زهرا، اکبری آلاشت، طاهره، خوانساری، رسول. (1392). طراحی رهنمود جدید برای مدیریت ریسک نقدینگی در سیستم بانکی ایران تهران.پژوهشکده پولی و مالی.


• عبده تبریزی، حسین. رادپور، میثم. (1388). اندازه‌گیری و مدیریت ریسک بازار: رویکرد ارزش در معرض ریسک. تهران: انتشارات آگاه).


• فلاح طلب، حسین، عزیزی، محمدرضا. (1393). کاربرد تئوری مقدار فرین در پیش‌بینی ارزش در معرض ریسک. فصلنامه علمی پژوهشی دانش سرمایه‌گذاری، سال سوم، شماره 1، صفحه 159-180.


• رهنمای رود پشتی، فریدون؛ نیکو مرام، هاشم؛ طلوعی اشلقی، عباس؛ حسین زاده لطفی، فرهاد و بیات، مرضیه (1394) بررسی کارایی بهینه‌سازی پرتفوی بر اساس مدل پایدار با بهینه‌سازی کلاسیک مجله مهندسی مالی و مدیریت اوراق بهادار، 23، 29-59.


• Allen, D. E. Singh, A. K. & Powell,R. J. (2011). Extreme market risk – An extreme value theory approach...Mathematics and computers in simulation, 94, 310-328.


• Bahmani Oskooee, M. & Harvey, H.(2011). Exchange rate volatility and industry trade between the US and Malaysia. International Business and Finance, 25(2), 127-155.


• Basel Committee on Banking Supervision. (2000). Sound Practices for Managing Liquidity in Banking Organizations, February.


• Basel Committee on Banking Supervision. (2009). International Framework for Liquidity Risk Measurement, Standards, and Monitoring, December.


• Basel Committee on Banking Supervision. (2011a). Basel III: A global regulatory framework for more resilient banks and banking systems. Basel, Switzerland:Bank for International Settlements (BIS). Available from http://www.bis.org/publ/bcbs189.pdf


• Basel Committee on Banking Supervision, (2012). Monitoring Indicators for Intraday Liquidity Management. http://www.bis.org/publ/bcbs225.htm


• Basel Committee on Banking Supervision, Basel III(2017): Finalising post-crisis reforms. Available at:https://www.bis.org/bcbs/publ/d424.htm.


• Bystrom, H. N. (2004). Managing extreme risks in tranquil and volatile markets using conditional extreme value theory. International Review of Financial Analysis, 13, 133–152.


• Chakravorti, S. (2000). Analysis of systemic risk in multilateral net settlement systems. J. Int. Financ. Mark. Inst. Money 10 (1), 9–30.


• Cheung K.C. Yuen F.L. (2019). On the uncertainty of VaR of individual risk, Journal of Computational and Applied Mathematics 367.


• Cucinelli, D. (2013), The Determinants of Bank Liquidity Risk within the Context of Euro Area, University of Rome Tor Vergata, Vol. 2, Issue. 10, pp.51- 64, 2013.


• Danielsson, J. & de Vries, C. G. (1997b). Tail index and quantile estimation with high frequency data. Journal of Empirical Finance, 4, 241–257.


• Dimitrakopoulos, D. N. Kavussanos, M. G. & Spyrou, S. I. (2010). Value at risk models for volatile emerging markets equity portfolios. The Quarterly Review of Economics and Finance, 50(4), 515-526.


• Fisher, R. A. & Tippett, L. H. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Cambridge Philosophical Society, 24, 180-190.


• Gardner Mona J; Dixiel.Mills. (1994), “Managing Financial Institution”, Third edition, Elizabeth Widdicombe- Florida U.S.A.


• Gujarati, D. (1995). Basic Econometrics, 3rd ed. McGraw-Hill.Hill, B. (1975). A simple general approach to inference about the tail of a distribution.


• Humphrey, D.B. ( 1986). Payments finality and risk of settlement failure. In: Saunders, A. White, L. (Eds.), Technology, and the Regulation of Financial Markets:Securities, Futures and Banking. Lexington Books, Lexington, MA, pp. 97–120.


• Jinqing Zhang Liang He Yunbi An (2019). Measuring banks liquidity risk An option pricing approach. Journal of Banking & Finance Volume 111, February 2020, 105703.


• Ismal, Rifki. (2010). Strengthening and improving the liquidity management in Islamic banking. Humanomics. 26(1). PP 18-35.


• Goudarzi, M. Khanarinejad, K. & Ardakani, Z. (2012). Investigation the Role of Exchange Rate Volatility on Iran‘s Agricultural Exports (Case Study: Date, Pistachio and Saffron). World Applied Sciences Journal, 20(6), 904-909.


• Morgan, J. P. (1996). RiskMetrics technical documentTM(4th ed). New York.


• Mutu, S. Balogh, P. & Moldovan, D. (2011). The efficiency of value at risk models on central and eastern European stock markets. International Journal of Mathematics and Computers in Simulation, 5(2), 110-117.


• Reed, E. Gill, E.K. (1989), “Commercial Banking”. Fourth Edition, Prentice Hall Englewood Cliffs, New Jercy.


• Robert De Cauxa Markus Bredea, Frank McGroarty (2016). Payment prioritisation and liquidity risk in collateralised interbank payment systems Robert De Cauxa Markus Bredea, Frank McGroarty Int. Fin. Markets, Inst. and Money 41 (2016) 139–150.


• Ruozi, Roberto, Ferrari, Pierpoalo.( 2013). Liquidity Risk Management in Banks: Economic and Regulatory Issues. Springer Briefs in Finance Publishers' Graphic LLC,Italy, pp. 1–54.


• Singh, A. K. Allen, D. E. & Robert, P. J. (2013). Extreme market risk and extreme value theory. Mathematics and computers in simulation, 94, 310-328.


• Suaiso, J. O. Q. & Mapa, D. S. (2010). Measuring market risk using extreme value theory. Philippine Review of Economics, 46(2).