Volume 26, Issue 3 (Autunm 2021)
JPBUD 2021, 26(3): 49-74 |
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Bastanzad H, Davoudi P. (2021). The Impact of Macro Systematic Shocks on the Non-Performing Loans: Multivariate Stochastic Volatility Model. JPBUD. 26(3), 49-74. doi:10.52547/jpbud.26.3.49
URL: http://jpbud.ir/article-1-2012-en.html
URL: http://jpbud.ir/article-1-2012-en.html
1- Department of Economics, Monetary and Banking Research Institute, Tehran, Iran. , h.bastanzad@mbri.ac.ir
2- National Iranian Center of Competition, Tehran, Iran.
2- National Iranian Center of Competition, Tehran, Iran.
Abstract: (2592 Views)
Generalized non-performing loans ratio (GNPLs) is empirically considered as a key prudential soundness indicator which is computed by the ratio of non-performing loans (overdue loans, arrears, doubtful loans, and rescheduled loans) to the total outstanding loan that also affects banks’ lending capacity. The GNPLs is evidently influenced by the macro systematic shocks (GDP growth, foreign exchange rate, inflation, and lending interest rate) which are statistically examined for a sample bank. In this regard, the impact of four systematic shocks on the GNPLs is estimated by a Vector Autoregressive (VAR) model during 2003-2020. In this context, the Impulse Response Function (IRF) of GNPLs is also examined against four contingent shocks while instantaneously variance decomposition of the GNPLs is estimated for the short and long term. The impact of the first and second moments of the shocks on GNPLs is estimated by the Multivariate Stochastic Volatility Model as well. The IRF output indicates that the GNPLs grows due to the shocks of exchange rate depreciation, GDP reduction, and inflation growth in the short time, while lending rate insignificantly affects the GNPLs owing to low historical volatilities as well as big arbitrages among bank lending rates for different economic sectors. The GNPLs Variance Decomposition highlights that GDP growth and inflation affect the GNPLs deviations in the short run, while the foreign exchange rate constantly motivates the GNPLs in the long run. In other words, the foreign exchange rate has strongly affected the GNPLs deviations in the long run, owing to its role as a nominal anchor and financial stability indicator in the macroeconomic environment. The GNPLs high Volatility which is estimated by conditional variance is also recognized in five different periods (2003, 2007, 2010, 2016, and 2019), mainly because of the foreign exchange rate unification in 2003, monetary expansion for self-employed loans in 2007, international sanctions in 2010, and 2009, as well as assets market recession in 2016 respectively. In this regard, the GNPLs deviations have also strongly correlated with output growth and foreign exchange rate Volatility.
Keywords: Financial Health, Non-Performing Loan, Macro Systematic Risk, VAR, Stochastics Volatility Model.
Type of Study: Research |
Subject:
financial economics
Received: May 10 2021 | Accepted: Dec 18 2021 | ePublished: Mar 06 2022
Received: May 10 2021 | Accepted: Dec 18 2021 | ePublished: Mar 06 2022
References
1. Acharya, V. V. (2009). A Theory of Systemic Risk and Design of Prudential Bank Regulation. Journal of Financial Stability, 5(3), 224-255. [DOI:10.1016/j.jfs.2009.02.001]
2. Acharya, V., Engle, R., & Pierret, D. (2014). Testing Macroprudential Stress Tests: The Risk of Regulatory Risk Weights. Journal of Monetary Economics, 65(1), 36-53. [DOI:10.1016/j.jmoneco.2014.04.014]
3. Basel Committee on Banking Supervision (2009). Principles for Sound Stress Testing Practices and Supervision. Bank for International Settlements, Basel. [Link:https://www.bis.org/publ/bcbs147.pdf]
4. Basel Committee on Banking Supervision (2017), Supervisory and Bank Stress Testing: Range of Practices. Bank for International Settlements, Basel. [Link:https://www.bis.org/bcbs/publ/d427.pdf]
5. Basel Committee on Banking Supervision (2021). Instructions for Basel III Monitoring. Bank for International Settlements, Basel. [Link:https://www.bis.org/bcbs/qis/biiiimplmoninstr_jan21.pdf]
6. Bernanke, B. S. (2013). Stress Testing Banks: What Have We Learned? A Speech at the" Maintaining Financial Stability: Holding a Tiger by the Tail" Financial Markets Conference Sponsored by the Federal Reserve Bank of Atlanta, Stone Mountain, Georgia.
7. Bräuning, F., & Fillat, J. L. (2019). Stress Testing Effects on Portfolio Similarities among Large US Banks. Federal Reserve Bank of Boston Research Paper Series Current Policy Perspectives Paper No. 19-1.
8. Engle III, R. F., & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. National Bureau of Economic Research Cambridge, Working Paper No. 8554. [DOI:10.3386/w8554]
9. Glasserman, P., & Tangirala, G. (2016). Are the Federal Reserve's Stress Test Results Predictable? The Journal of Alternative Investments, 18(4), 82-97. [DOI:10.3905/jai.2016.18.4.082]
10. Harvey, A., Ruiz, E., & Shephard, N. (1994). Multivariate Stochastic Variance Models. The Review of Economic Studies, 61(2), 247-264. [DOI:10.2307/2297980]
11. Heidari, H., & Nourbahksh, I. (2015). Application of Bayesian Macro-Econometrics for Operationalizing Stress Test in Karafarin Bank. Quarterly Journal of Islamic Finance and Banking Studies, 1(1), 225-246. [Link: http://jifb.ibi.ac.ir/article_49430.html]
12. Heidari, H., Saberian Ranjbar, S., & Nili, F. (2011). The Impact of Macroeconomic Variables on Banks'Balance Sheet: A Stress Test Approach. Monetary & Banking Research, 3(8), 43-86. [Link:http://jmbr.mbri.ac.ir/article-1-91-fa.html]
13. Hirtle, B., & Lehnert, A. (2015). Supervisory Stress Tests. Annual Review of Financial Economics, 7(1), 339-355. [DOI:10.1146/annurev-financial-111914-042040]
14. Hosszejni, D., & Kastner, G. (2021). Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and Factorstochvol. Journal of Statistical Software, 100(12), 1-34. [DOI:10.18637/jss.v100.i12]
15. Jacquier, E., Polson, N. G., & Rossi, P. (1999). Stochastic Volatility: Univariate and Multivariate Extensions: CIRANO.
16. Kapinos, P. S., Martin, C., & Mitnik, O. A. (2018). Stress Testing Banks: Whence and Whither? Journal of Financial Perspectives, 5(1), 1-20.
17. Leitner, Y., & Yilmaz, B. (2019). Regulating a Model. Journal of Financial Economics, 131(2), 251-268. [DOI:10.1016/j.jfineco.2018.08.010]
18. Melino, A., & Turnbull, S. M. (1990). Pricing Foreign Currency Options with Stochastic Volatility. Journal of Econometrics, 45(1-2), 239-265. [DOI:10.1016/0304-4076(90)90100-8]
19. Morgan, D. P., Peristiani, S., & Savino, V. (2014). The Information Value of the Stress Test. Journal of Money, Credit and Banking, 46(7), 1479-1500. [DOI:10.1111/jmcb.12146]
20. Quigley, D., & Walther, A. (2015). Inside and Outside Information: Fragility and Stress Test Design. University of Oxford Working Paper.
21. Rostami, M., Nabizade, A., & Shahi, Z. (2018). Factors Affecting Credit Risk of Commercial Banks of Iran with Emphasis on Banking and Macroeconomic Specific Factors. Journal of Asset Management and Financing, 6(4), 79-92. [DOI:https://dx.doi.org/10.22108/amf.2018.105889.1156]
22. Salehabadi, A., & Allahyari, M. (2016). The Use of Stress Testing in Regulatory and Supervisory Perspectives. Journal of Investment Knowledge, 5(19), 213-234. [Link:http://jik.srbiau.ac.ir/article_9334.html]
23. Sepehrdoust, H., & Berjisian, A. (2014). Estimation of Bank Credit Refunding Failure; Using Logit Regression. The Journal of Planning and Budgeting, 19(1), 31-52. [Link:http://jpbud.ir/article-1-1135-fa.html]
24. Shabani, A., & Jalali, A.-H. (2012). Causes of Nonperforming Assets in Iran's Baking System and the Solutions. The Journal of Planning and Budgeting, 16(4), 155-181. [Link:http://jpbud.ir/article-1-456-fa.html]
25. Tett, G. (2015). Stress Tests for Banks are a Predictable Act of Public Theatre. Financial Times, 5 March.
26. Valipour Pasha, M., & Bastanzad, H. (2015). The Impact of Macroeconomic Indicators on the Nonperforming Loans (Case of Iran). Journal of Money and Economy, 10(1), 63-82. [Link: http://jme.mbri.ac.ir/article-1-188-en.html]
27. Williams, B. (2017). Stress Tests and Bank Portfolio Choice: New York University.
28. Zhou, X., Nakajima, J., & West, M. (2014). Bayesian Forecasting and Portfolio Decisions Using Dynamic Dependent Sparse Factor Models. International Journal of Forecasting, 30(4), 963-980. [DOI:10.1016/j.ijforecast.2014.03.017]
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