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A Study of the Influence and Influence of Factors Affecting the Stability of the Banking System in Selected Countries of the Mena Region | ||
| توسعه و سرمایه | ||
| Volume 6, Issue 1 - Serial Number 10, September 2021, Pages 1-18 PDF (640.94 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22103/jdc.2021.16550.1107 | ||
| Authors | ||
| Babak Kouhi leilan1; Rahim Dabbagh* 2; Seyyed Ziaoddin Kiaalhosseini3; Farhad Rahbar4 | ||
| 1Ph.D. Student of Economic, Mofid University, Qom, Iran. | ||
| 2Associate Professor of Industrial Engineering, Urmia University of Technology, Urmia, Iran. | ||
| 3Associate Professor of Economic, Mofid University, Qom, Iran. | ||
| 4Professor of Economic, University of Tehran, Tehran, Iran. | ||
| Abstract | ||
| Objective: Banking, by its very nature, involves a wide range of risks. Banking supervisors should identify their risks and evaluate and manage them. Therefore, the factors affecting banking stability should be identified and applied in proportion to the importance of each relevant strategy. Methods: The method of the present research is descriptive and applied and using descriptive and inferential methods, the data have been analyzed and then the obtained results have been analyzed. It has been used to identify the impact of credit and liquidity risks on banking stability in selected member countries of the Mena region using the Panel Gentle Transfer Regression (PSTR) model, which is one of the prominent regime change models. Results: According to the results of MATLAB software, it was found that the variables of liquidity risk and credit risk in both regimes have the greatest impact on the stability of the banking system of MENA member countries and also the impact of credit risk in both regimes is greater than liquidity risk. Therefore, formulating appropriate policies to manage credit risk reduction can lead to the stability of the country's banking system, which in turn will strengthen the monetary system. Analyzing the relationship between economic factors and risks on banking stability is an important issue that has been addressed in this study. The thresholds of credit risk as a turning point and distinguishing point of the two regimes expressed in the PSTR model, for these equations, have been estimated according to the Akaik and Schwartz tests (3.36 and 3.83), respectively. The results of parameter slope estimation showed that the adjustment speed from one regime to the second regime was equal to 0.194, which indicates their gentle adjustment speed. In the first regime before the threshold, ie the linear part of the PSTR model, the variables of credit risk, payment facilities, inflation and crisis and shocks to countries have a negative and significant effect on the banking system. In contrast, the variables of facility to deposit ratio, liquidity risk, bank size, return on assets, efficiency of banks and GDP have a positive and significant effect on the banking system. In the second regime, the nonlinear part of the PSTR model, the variables of facility to deposit ratio, bank size, inflation, capital to asset ratio, facilities and crises and shocks that have hit a country have a negative and significant effect on the banking system. In contrast, the variables of liquidity risk, credit risk, return on assets, efficiency of banks, gross national product and payment facilities of banks have a positive and significant effect on the banking system. Conclusion: According to the results of this study (first and second scenarios), liquidity risk, in addition to a positive effect on banking stability, intensifies its positive effect on the banking stability of countries. Credit risk also has a significant effect on off-line banking stability, which has been confirmed. In other words, according to the results of the estimated model, the variables of liquidity risk and credit risk in both regimes have the greatest impact on the stability of the banking system of MENA member countries, so that the effect of credit risk in both regimes is greater than liquidity risk. Therefore, formulating strategies to reduce instability in the country's banking system, credit risk management can be an important factor to increase banking stability, which will strengthen the monetary system. | ||
| Keywords | ||
| Banking Stability; Panel Threshold Regression; Credit Risk; Mena Countries | ||
| References | ||
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رحیمی باغی ، علی ؛ عرب صالحی نصرآبادی، مهدی؛ واعظ برزانی، محمد. (1398). ارزیابی ریسک سیستمی در خرده نظام‌های مالی کشور با استفاده از روش گرنجر غیرخطی. مدیریت دارایی و تأمین مالی، 7(2)، 80-59.
پوستین‌چی، مجتبی؛ تحصیلی، حسن؛ کریم‌زاده، مصطفی. (1395). بررسی تأثیر رقابت در صنعت بانکداری بر ثبات بانک‌ها. اقتصاد پولی مالی، 23(11)، 145-123.
جز ملکی، مهرداد؛ دباغ، رحیم؛ بهنیا، سهراب. (1399). آزمون نظریه آشوب و پیش‌بینی قیمت‌های آتی صنایع فراورده‌های نفتی. فصلنامه مطالعات اقتصادی کاربردی ایران، 9(34)، 109-135.
دباغ، رحیم؛ گلمرادی، حسن؛ باقری، آرش. (1398). بررسی عملکرد مالی بانک‌های اسلامی و غیراسلامی در کشورهای منتخب با استفاده از مدل CAMEL. مطالعات مالی و بانکداری اسلامی، 4(10)، 114-85.
دباغ، رحیم؛ آقاپور، صابر؛ نخودچی، اشکان. (1400). رتبه‌بندی استان‌های کشور از لحاظ عملکرد زیست‌محیطی صنایع با رویکرد توسعه‌یافته تصمیم‌گیری. مهندسی عمران امیرکبیر، 53(12)، 19-19.
رستمیان، فروغ؛ حاجی‌بابایی، فاطمه. (1388). اندازه‌گیری ریسک نقدینگی بانک سامان با استفاده از مدل ارزش در معرض خطر. پژوهشنامه حسابداری مالی و حسابرسی، 1(3)، 199-174.
عیسی‌زاده، سعید؛ شاعری، زینب. (1390). بررسی تأثیر وضعیت ثبات کلان اقتصادی بر کارایی نظام بانکی (مطالعه موردی کشورهای خاورمیانه و شمال آفریقا). پژوهشنامه اقتصاد کلان، 6 (12)، 84-63
گرجی بندپی، ابراهیم؛ انواری رستمکلائی، فرزانه. (1397). نقش بانک مرکزی در ایجاد سیکل‌های تجاری در اقتصاد ایران. پژوهش‌های اقتصاد پولی مالی، 25(15)، 32-1.
References
Agnello, L., Sousa, R.M. (2012). How do banking crises impact on income inequality? Applied Economics Letters, 19(15), 1425-1429.
Baltagi, B. H., & Li, Q. (1995). Testing AR (1) against MA (1) disturbances in an error component model. Journal of Econometrics, 68(1), 133-151.
Colletaz, G., Hurlin, C. (2006). Threshold effects in the public capital productivity: an international panel smooth transition approach. document de Recherche du Laboratoire d'Economie d'Orléans.
Gonzalez, A., Terasvirta, T., Van Dijk, D. (2005). Panel smooth transition regression models. SSE/EFI working paperseries in economics and finance no. 604. Colletaz, G., & Hurlin, C. (2006). Threshold effects in the public capital productivity: an international panel smooth transition approach. document de Recherche du Laboratoire d'Economie d'Orléans. 2006-1.
Eitrheim, Ø., Teräsvirta, T. (1996). Testing the adequacy of smooth transition autoregressive models. Journal of Econometrics, 74(1), 59-75.
Dabbagh, R., Agapoor, S., Nokhodchi, A. (2022). Ranking Iranian provinces in terms of the environmental performance of industries with a developed decision-making approach. Amirkabir Journal of Civil Engineering, 53(12), 19-19 [In Persian].
Dabbagh, R., Golmoradi, H., Bagri, A. (2019). Financial Performance Comparison of Islamic and Conventional Banking in Selected Countries using the CAMEL Model. Quarterly Journal of Islamic Finance and Banking Studies, 4(Autumn), 85-114 [In Persian].
De Jonghe, O., Dewachter, H., Ongena, S. (2020). Bank capital (requirements) and credit supply: Evidence from pillar 2 decisions. Journal of Corporate Finance, 60, 101518.
Escribano, A., Jorda, O. (1999). Improved testing and specification of smooth transition regression models. In Nonlinear time series analysis of economic and financial data (pp. 289-319). Springer, Boston, MA.
Fok, D., Van Dijk, D., Franses, P.H. (2005). Forecasting aggregates using panels of nonlinear time series. International Journal of Forecasting, 21(4), 785-794.
Gorji Bandpi, E., Anvari Rostamkolaii, F. (2018). The Role of the central bank in creating business cycles in the iranian economy. Monetary & Financial Economics, 25(16), 1-32 [In Persian].
Imbierowicz, B., Rauch, C. (2014). The relationship between liquidity risk and credit risk in banks. Journal of Banking & Finance, 40, 242-256.
Issazadeh, Saeed; Poetry, Zeinab. (2012). Investigating the effect of macroeconomic stability on the efficiency of the banking system (Case study of the Middle East and North Africa). Macroeconomics Research Letter, 6(12), 63-84 [In Persian].
rahimi baghi, A., Arabsalehi Nasrabadi, M., Vaez Barzani, M. (2019). Assessing the Systemic Risk in the Financial Sub-Systems of Iran, using Nonlinear Granger Method. Journal of Asset Management and Financing, 7(2), 59-80 [In Persian].
Rifqah, A.S., Hassan, H.H. (2019). The relationship between bank’s credit risk, liquidity, and capital adequacy towards its profitability in Indonesia. International Journal of Recent Technology and Engineering, 7(5), 225-237.
Rostamian, F., Haji Babaei, F. (2009). Measuring Bank Liquidity Risk Using Venture Value Model (Case Study: Saman Bank). Financial Accounting and Auditing Research, 1(3), 174-199 [In Persian].
Jaiswal, S. (2010). Relationship between asset and liability of commercial banks in India, 1997-2008. International Research Journal of Finance and Economics, 49, 43-58.
Jozmaleki, M., Dabbagh, R., & Behnia, S. (2020). Chaos theory and predict future prices in the oil products. Journal of Applied Economics Studies in Iran, 9(34), 109-135 [In Persian].
Jude, E. (2010). Financial development and growth: panel smooth regression approach. Journal of Economic Development, 35(1), 15-33.
Kleinow, J., Moreira, F., Strobl, S., Vähämaa, S. (2017). Measuring systemic risk: comparison of alternative market-based approaches. Finance Research Letters, 21, 40-46.
Lopez, G., Moreno, A., Ruhia, A.A., Symmetric, CoVaR. (2011). An application to international banking. Systemic Risk. Basel III, Financial Stability and Regulation.
Poustin chi, M., Tahsili, H., Karim Zadeh, M. (2016). The Effect of Competition in Banking on the Stability of Banks. Monetary & Financial Economics, 23(11), 123-145 [In Persian].
Rahimi Baghi, A., ArabSalehi, M., Vaez Barzani, M. (2019). Assessing the Systemic Risk in the Financial System of Iran using Granger Causality Network Method. Financial Research Journal, 21(1), 121-142.
Rostamian, F., & Haji, B. F. (2009). TO Measure bank liquidity risks with value at risk (VAR) model (Case Study: Saman Bank) [In Persian].
Schinasi, G. (2004). Defining financial stability. IMF Working Paper 04/187.
Smaga, P. (2013). Assessing involvement of central banks in financial stability. Center for financial stability Policy hfd. | ||
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