Evaluation of the Effect of the Banking Sector Systemic Risk on the Macroeconomic Performance of Iran

Document Type : Research Paper

Authors

1 Prof., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.

2 Ph.D. Candidate, Department of Finance, Faculty of Management, University of Tehran, Iran.

3 Assistant Prof., Department of Finance, Faculty of Mathematics, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.

4 Assistant Prof., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.

Abstract

Objective: Systemic risk is the cause of many financial crises and has adverse effects on economic performance at the macro level. For effective policy-making of systemic risk management, it is necessary to measure and monitor systemic risk and to study the mechanism of its effect on macro-economy. This paper aims at investigating the relationship between banking sector systemic risk and the performance of macroeconomic indexes including Gross Domestic Production (GDP), GDP without oil, the components of GDP and the value added of the sectors.
Methods: One of the best systemic risk measures is SRISK index which is used in this article, and the relationship between the changes of macroeconomic indexes and the changes of SRISK is evaluated using autoregressive distributed lags model.
Results: There is a significant negative relationship between the banking sector systemic risk of Iran and GDP (with and without oil) for horizon of 12 months. Value added of construction, financial sector and industry sector is influenced more than other sectors from the changes of systemic risk of banking system. Furthermore, all of the components of GDP are influenced by the changes of systemic risk but this influence is stronger and more durable for the fixed investment component.
Conclusion: In addition to the increase of the probability of financial crisis, the increasing of systemic risk has long-term adverse effects on macroeconomic performance and investments. In order to take a timely measure for decreasing the adverse effects of systemic risks, Policy-makers should monitor SRISK index continuously.

Keywords


Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2-47.
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.
Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. The American Economic Review, 106(7), 1705.
Allen, L., Bali, T. G., and Tang, Y. (2012). Does systemic risk in the financial sector predict future economic downturns? The Review of Financial Studies, 25(10), 3000–3036.
Andrews, D.W.K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3), 817–858.
Bernanke, B. S., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. Handbook of macroeconomics, 1, 1341-1393.
Bernanke, Ben. (1983). Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics, 98(1), 85–106.
Bisias, D., Flood, M., Lo, A. W., & Valavanis, S. (2012). A survey of systemic risk analytics. Annual Review of Financial Economics, 4(1), 255-296.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623-685.
Brownlees, C., & Engle, R. F. (2016). SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 30(1), 48-79.
Brunnermeier, M. K., & Oehmke, M. (2013). Bubbles, financial crises, and systemic risk. In Handbook of the Economics of Finance, 2, 1221-1288.
Brunnermeier, M. K., Gorton, G., & Krishnamurthy, A. (2012). Risk topography. NBER Macroeconomics Annual, 26(1), 149-176.
Cardarelli, R., Elekdag, S., & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7(2), 78-97.
Danesh Jafari, D., Mohammadi, T., Botshekan, M. H., & Pashazadeh, H. (2017) .Analysis of the Systemic Risk in the Banking System Using Dynamic Conditional Correlation (DCC), Journal of Monetary and Banking Research, 33, 457-480. (in Persian)
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22.
Dufour, J. M., & Roy, R. (1985). Some robust exact results on sample autocorrelations and tests of randomness. Journal of Econometrics, 29(3), 257-273.
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)
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007.
Farzinvash, A., Elahi, N., Gilanipour, J. & Mahdavi, Gh.(2017). The evaluation of systemic risk in the Iran banking system by delta Conditional Value at Risk (CoVaR) criterion. Journal of Financial Engineering and Portfolio Management, 8(33), 265-281. (in Persian)
Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics, 119(3), 457-471.
Hansen, L. P. (2013). Challenges in identifying and measuring systemic risk. In Risk topography: Systemic risk and macro modeling (pp. 15-30). Chicago: University of Chicago Press.
Holmstrom, B., & Tirole, J. (1997). Financial intermediation, loanable funds, and the real sector. the Quarterly Journal of economics, 112(3), 663-691.
Jobst, M. A. A., & Gray, M. D. F. (2013). Systemic contingent claims analysis: Estimating market-implied systemic risk (No. 13-54). International Monetary Fund.
Leahy, J. V., & Whited, T. M. (1996). The effect of uncertainty on investment: some stylized trends. Journal of Money, Credit & Banking, 28(1), 64-83.
Merton, R. (1973). Theory of Rational Option Pricing. Bell Journal of Economics, 4(1), 141-183.
Mohammadi, S., Raei, R., Tehrani, R., Faizabad, A. (2009). Modeling Volatility: Evidence from Tehran Stock Exchange. Financial Research Journal, 11(27), 97-110. (in Persian)
Mohammadiaghdam, S., Ghavam, M., Fallah Shams, M. (2017). Assessment of the Systemic Risk Originated from the Currency Shocks in the Financial Markets of Iran. Financial Research Journal, 19(3), 475-504. (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. (in Persian)
RDC Team. (2008). R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.
Rostami, M., Haqiqi, F. (2013). Using MGARCH to Estimate Value at Risk. Financial Research Journal, 15(2), 215-228. (in Persian)
Tsay, R. S. (2013). Multivariate time series analysis: with R and financial applications. Hoboken, New Jersey: John Wiley & Sons.
Zeileis A (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software, 11(10), 1–17.