Assessing the effect of macroeconomic shocks on systemic risk of the banking system using the SVAR model in Iran
Subject Areas : General
ali ostadhashemi
1
()
seyed jalal sharif
2
()
ali Souri
3
(اقتصاد نظری، دانشکده اقتصاد، دانشگاه تهران)
Keywords: Systemic risk of banking system, macroeconomic variables, conditional risk value, quantitative regression, vector autoregression model,
Abstract :
In this study, we have used the total capital market index (TEDPIX) as an index of the real sector of the economy and the index of banks and credit financial institutions as an index that explains the developments of the banking system. Also, oil revenues, exchange rate uncertainty, tax revenues, liquidity, nominal interest rates, inflation uncertainty and GDP have been used as macroeconomic variables in the research period (1370-1396). To estimate the systemic risk of the banking system, the quarterly data of the banks' index is used and the value at risk of the return of the seasonal data of the index is estimated using an exponential GARCH model. In order to model the interaction of macroeconomic variables and systemic risk of the banking system, an unrestricted vector autoregression (VAR) model was estimated and then using instantaneous impact functions and based on Chulsky analysis, systemic risk response to other variables was investigated and analyzed. In order to identify the channels of impact of economic shocks on the systemic risk of the banking system, based on the structures of the Iranian economy, a structural vector autoregression (SVAR) model was specified and then the instantaneous impact functions were extracted and the effect of macro variable shocks on the systemic risk of the banking system was investigated. Also, the effect of systemic risk of the banking system on macroeconomic variables was investigated and analyzed using instantaneous impact functions. Finally, the interaction model of macroeconomic variables and systemic risk of the banking system was approved using the vector autoregressive model.
1. باقری نژاد، جعفر و جاوید، غزاله (1393). ارائه مدل نوآوری باز در صنعت بانکداری ایران: مطالعه موردی بانک پارسیان، فصلنامه نوآوری و ارزش‌آفرینی، شماره 5، سال 3، فصل بهار-تابستان سال .1393
2. صادقی شریف سیدجلال، سوری علی، استادهاشمی علی (1397). مدلسازی و برآورد ریسک سیستم بانکی در قالب یک مدل شبکه‌ای با استفاده از سنجه CoVaR، پژوهش هاي پولي بانكي.دوره 11، شماره 36. 210-183.
3. صادقی، مهدی. (1392)، مدیریت ریسک سیستمیک در نهادهای مالی بازار سرمایه ایران. نشریه مدیریت پژوهش، توسعه و مطالعات اسلامی، 38 - 1.
4. معظمی، منصور و سرعتی آشتیانی، نرجس (1391)، راهبردهای مقابله با اثرات تحریم بر صنعت نفت ایران با تأکید بر توسعه همکاری‌های دانشگاه و صنعت، فصلنامه نوآوری و ارزش‌آفرینی، شماره 2، سال 1، فصل پاییز-زمستان سال1391.
5. مهدوی کلیشمی غدیر، الهی ناصر، فرزین وش اسداله و گیلانی پور جواد (1396)، ارزیابی ریسک سیستمی در شبکه بانکی ایران توسط معیار تغییرات ارزش در معرض خطر شرطی. مجله مهندسي مالي و مديريت اوراق بهادار. شماره 33.
6. ندائی، امین و خوارزمی، امیدعلی (1393)، تحلیل سیستمی تأثیر ریسک‌پذیری بر نوآوری در پارک علمی و فناوری پردیس، فصلنامه نوآوری و ارزش‌آفرینی، شماره 5، سال 3، فصل بهار-تابستان سال .1393
7. نقی لو، احمد و محمد نظامی، طاهره (۱۳۹۳)، ارتباط بین متغیرهای کلان اقتصاد و ریسک اعتباری بانک‌ها، کنفرانس بین‌المللی مدیریت و مهندسی صنایع، مرکز همایش‌های بین‌المللی صداوسیما.
1. Abedola, S.S., Yusoff, W.S.W., Dahalan, J., 2011. An ARDL approach to the determinants of non-performing loans in Islamic banking system in Malaysia. Kuwait Chapter Arab. J. Bus. Manage. Rev. 1 (2), 20–30.Paper, 3957.
2. 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.
3. Adrian, T., Brunnermeier, M. K., 2010. CoVaR. Federal Reserve Bank of New York Staff Report (348).
4. Aggarwal, R. (1981). Exchange rates and stock prices: a study of three US capital markets under floating exchange rates. Akron Business and Economics Review, 12, 7–12.
5. Aggarwal, R., Demirgüc ¸-Kunt, A., Martinez Peria, M.S., 2006. Do Workers’Remittances Promote Financial Development? World Bank Policy Research Working R., 3957.
6. Benoit, S., et al., A theore"cal and empirical comparison of systemic risk measures. 2013.
7. Bernal, Oscar, Gnabo, Jean-Yves, and Gregory Guilmin, (2013), Assessing the contribution of banks, insurance and other financial, Working Paper.
8. Castro, V., 2013. Macroeconomic determinants of the credit risk in the banking system: the case of the GIPSI. Econ. Model. 31, 672–683.
9. Delgado, J., Saurina, J., 2004. Credit Risk and Loan Loss Provisions. An Analysis with Macroeconomic Variables. Banco de Espana Working Paper, No. 12.
10. Espinoza, R., Prasad, A., 2010. Nonperforming Loans in the GCC Banking System and their Macroeconomic Effects. IMF Working Paper, 224.
11. Flamini, V., McDonald, C., Schumacher, L., 2009. The Determinants of Commercial Bank Profitability in Sub-Saharan Africa. IMF Working Paper, 15.
12. Fofack, H., 2005. Nonperforming Loans in Sub-Sahara Africa. Causal Analysis and Macroeconomic Implication. World Bank Policy Research Paper, 3769.
13. Greenidge, K., Grosvenor, T., 2009. Forecasting Non-performing Loans in Barbados. Bank of Barbados Working Paper.
14. Guerra, S. M., Silva, T. C., Tabak, B. M., de Souza Penaloza, R. A., de Castro Miranda, R. C., jan 2016. Systemic risk measures. Physica A: Statistical Mechanics and its Applications 442 (1), 329–342.
15. John E. Golob, Je,.(1994). Does inflation uncertainty increase with inflation? Economic Review, issue Q III, No v. 79, no. 3, 27-38 16. Jorion, Ph. (2001). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill, New York.
17. Kangogo, N.J., Asienga, I.C., 2014. Factors affecting non-performance of personal loans in banking industry: case study of tier one banks in Kenya. Conference Proceedings, 4th Annual Conference Kabarak University, 2014.
18. Kline, Rex B. (2005). Principles and practice of structural equation modeling. New York: Guilford Press, - Methodology in the social sciences.
19. Lutkepohl, H. & Kratzig, M. (2004). Applied Time Series Econometrics. Cambridge University Press. pp. 321-350.
20. Mawili, G.M., 2013. The impact of macroeconomic factors on non-performing loans in the Kenyan banking industry. In: Proceedings of the 1st Annual Research Conference, Jomo Kenyatta University, 12–13 September 2013.
21. Nikolaidou, E., Vogiazas, S.D., 2014. Credit risk determinants for the Bulgarian banking system. Int. Adv. Econ. Res. 20 (1), 87–102.
22. Nkusu, M., 2011. Non-performing loans and macrofinancial vulnerabilities in advanced economies. IMF Working Paper 11/161.
23. Washington, G.K., 2014. Effects of macroeconomic variables on credit risk in the Kenyan banking system. Int. J. Bus. Commer. 3 (9), 1–26.
24. Zeman, J., Jurca, P., 2008. Macro Stress Testing of the Slovakian Banking Sector. National Bank of Slovakia Working Paper, 1/2008.
25. Acharya, Lasse H. Pedersen, Thomas Philippon, Matthew Richardson, Measuring Systemic Risk, The Review of Financial Studies, Volume 30, Issue 1, January 2017, Pages 2–47.
26. Li, S., Lu, Y., Wu, Ch. (2019). Systemic risk in bank-firm multiplex networks. Finance Research Letters, In press, corrected proof Available online.
27. Adrian, T., Brunnermeier, M. (2016). CoVaR. American Economic Review, 106(7), 1705-1741.
28. 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].
29. Kumar, V. (2018). Systemic Risk vs Systematic risk. Accounting Education, eBook,Retrievedfrom http://www.svtuition.org/2012/07/systemic-risk-vs-systematic-risk.html.
30. 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. (in Persian)
31. Leventides, J., Loukaki, K., & Papavassiliou, V. G. (2020). Simulating financial contagion dynamics in random interbank networks. Journal of Economic Behavior & Organization, 158, 500-525.
32. Erol, S., & Vohra, R. (2020). Network formation and systemic risk. Available at SSRN 2546310.
33. Eivazlu, R. & Rameshg, M. (2019). Measuring systemic risk in the financial institution via dynamic conditional correlation and delta conditional value at risk mode and bank rating. Asset Management and Financing, 7(4), 1-16. (in Persian)
34. Engel, J., Pagano, A., & Scherer, M. (2020). Reconstructing the topology of financial networks from degree distributions and reciprocity. Journal of Multivariate Analysis
35. Acharya, V. V., Richardson, M., Jan 2009. Causes of the financial crisis. Critical Review 21 (2-3), 195–210.
36. Lage-Junior, M., Godinho-Filho, M., May 2010. Variations of the Kanban system: Literature review and classification. International Journal of Production Economics 125 (1), 13–21.
37. Seuring, S., Mar 2013. A review of modeling approaches for sustainable supply chain management. Decision Support Systems 54 (4), 1513–1520.
38. Jabbour, C. J. C., May 2013. Environmental training in organisations: From a literature review to a framework for future research. Resources, Conservation and Recycling 74 (1), 44–155.
