Modified systemic risk model with ∆CoVaR approach in banking system with an Emphasis on Bank Indicators

Document Type : Original Article

Authors

1 PhD Student, Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

10.30495/ijfma.2023.72208.1987

Abstract

The present study presents the revised systemic risk model with the Changing conditional value-at-risk (∆CoVaR) approach in banking network with an emphasis on bank indicators. Systemic risk investigates the potential capacity of financial crisis spread among banks and ultimately the real sector of the economy through simultaneously increasing the fat tail of loss distribution. This is a descriptive-analytical research in terms of method and a developmental/applicative study in terms of purpose. The research time zone is 2009/03/21-2021/01/19. The research data includes the weekly average stock price of seven banks (Mellat, Tejarat, Saderat, EN Bank, Parsian, Karafarin, and Sina) listed in stock exchange and the weekly average of the general stock market index from Rahavardnovin system, and data related to the banks’ financial metrics are extracted from the financial statements of the banks in the Codal website.
To measure each bank’s share in systemic risk, the measure (∆COVaR) is employed. We show the better fit of ∆CoVaR for measuring risk compared to VaR and CoVaR models. The ratings of the investigated banks are tested by means of two criteria (RMSE) and (MAE) and it is concluded that in some banks, the crisis has higher destructive effects on the entire financial system than that in other banks. Finally, the association between systemic risk and the financial parameters of the investigated banks is reviewed and it is concluded that the improvement of the capital adequacy ratio (CAR) has an inverse and significant relationship with systemic risk.

Keywords

References

  • Adrian, Tobias and Markus K. Brunnermeier. 2016. "CoVaR." American Economic Review, 106(7):1705-41.
  • Adrian,T., K. Brunnermeier, M, 2011, COVAR. NATIONAL BUREAU OF ECONOMIC RESEARCH, Cambridge.
  • Girardi, G., Ergün, T., 2013, “Systemic risk measurement: Multivariate GARCH estimation of CoVaR,” Journal of Banking & Finance 37 (2013) 3169–3180.
  • Acharya, V., 2009. A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability 5, 224–255.
  • Acharya, V., Pedersen, L.H., Philippon, T., Richardson, M., 2010. Measuring Systemic Risk. Working paper, New York University.
  • Adrian, T., Brunnermeier, M.K., 2011. CoVaR. Working paper, Federal Reserve Bank of New York.
  • Allen, L., Bali, T.G., Tang, Y., 2010. Does systemic risk in the financial sector predict future economic downturns? Working paper, SSRN.
  • Bauwens, L., Laurent, S., 2005. A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics 23, 346–354.
  • Benoit, S., Colletaz, G., Hurlin, C., Perignon, C., 2012. A Theoretical and Empirical Comparison of Systemic Risk Measures. Working paper, SSRN.
  • Billio, M., Getmansky, M., Lo, A.W., Pelizzon, L., 2012. Econometric measures of systemic risk in the finance and insurance sectors. Journal of Financial Economics 104, 535–559.
  • Brownlees, C., Engle, R., 2011. Volatility, Correlation and Tails for Systemic Risk Measurement. Working paper, New York University. Christoffersen, P., 1998. Evaluating interval forecasts. International Economic Review 39, 841–862.
  • Duffie, D., Pan, J., 1997. An overview of Value at Risk. The Journal of Derivatives 4, 7– 49.
  • Engle, R.F., 2002. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics 20, 339–350.
  • Giot, P., Laurent, S., 2003. Modelling daily Value-at-Risk using realized volatility and ARCH type models. Journal of Empirical Finance 11, 379–398.
  • Hansen, B., 1994. Autoregressive conditional density estimation. International Economic Review 35, 705–730.
  • Huang, X., Zhou, H., Zhu, H., 2009. A framework for assessing the systemic risk of major financial institutions. Journal of Banking and Finance 33, 2036–2049.
  • Lorenzoni, G., 2008. Inefficient credit booms. Review of Economic Studies 75, 809– 833.
  • Mainik, G., Schaanning, E., 2012. On Dependence Consistency of CoVaR and some other Systemic Risk Measures. Working paper, ETH Zürich.
  • Mittnik, S., Paolella, M., 2000. Conditional density and Value-at-Risk prediction of Asian currency exchange rates. Journal of Forecasting 19, 313–333.
  • Kupiec, P., 1995. Techniques for verifying the accuracy of risk measurement models.Journal of Derivatives 3, 73–84.
  • Segoviano, M., Goodhart, C., 2009. Banking Stability Measures. Working paper, IMF.

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