Systemic Risk in TSE Banking Sector

Document Type : applied

Authors

1 Assistant Prof., Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran

2 MSc. Student, Financial Engineering, Khatam University, Tehran, Iran

Abstract

Systemic risk is the risk of collapse in the financial system. Due to the financial crisis that hit the world economy in 2008, the study of systemic risk in the banking sector became more attractive for researchers. In this research we study systemic risk in the Iranian banking sector by using a conventional systemic risk measure, ∆CoVaR. To compute the measure, we employ dynamic conditional correlation model. For this purpose, we estimate the mentioned systemic risk measure of the seven Iranian banks from March of 2010 to March of 2015. Then using panel data regression, we investigate the relationships between the systemic risk measure and certain bank characteristic variables (i.e. VaR, size-Log of Equity, Leverage ratio). Our empirical findings shows that, the systemic risk contributions of banking sector is high and studied banks has different ranking based on ∆CoVaR. finally, the systemic risk contribution is closely related to mentioned bank characteristic variables
JEL: G11, G21, G32
How to cite this paper: Rastegar, M. & Karimi, N. (2016). Systemic Risk in TSE Banking Sector. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 1–19. (In Persian)

Keywords

Main Subjects


Acharya, V., Pederson, L., Philippe, T. & Richardson, M. (2010). Measuring Systemic Risk, Technical Report. Department of Finance, NYU.
Adrian, T., & Brunnermier, M. K. (2011).CoVaR. NBER Working Paper, No.17454.
Bollerslev, T. (1990). Modeling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 72.
Ahmadi, Z., & Farhanian, M. (2014). Measuring Systemic Risk in the Tehran Stock Exchange, CoVaR and MES Approach. Quarterly Journal of Securities Exchange,7(26), 3-23. (In Persian).
Banulescu, G., Denisa, D., & Elena, I. (2014). Which are the SIFIs a CES Approach to Systemic Risk, European University Institute.
Brownlees, C. T., & Engle, R. (2012).Volatility, Correlation, and Tails for Systemic Riskmeasurement, Working Paper.
Caufman, G., & Scott, E. (2003). What is the Systemic Risk and Do Bank Regulations Retard or Contribute to It, The Independent Review, 371.
Christodoulakis, E., & Satchell, S. (2002). A Model with Time-varying Correlations , Working Paper.
Choi, H. (2012). Predicting the Present with Google Trends, Working Paper.
Dungey, M., Luciani, M., & Verdas, D. (2013). Googling SIFIs
Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model.J.Bus, Econ, Stat, 20, 339-350.
Engle, R., & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH, Working Paper.
Girardi, G., & Ergun, A. T. (2013). Systemic Risk Measurement: Multivariate GARCH Estimation of Covar. J. Bank. Finance, 37, 3169-3180.
Hosseini, A., & Razavi, S. (2014). The Role of Capital in Systemic Risk of Financial Institutions. Empirical Research in Accounting, 4(13), 127-147. (In Persian).
Huang, X., & Zhou, H. (2009). A Framework for Assessing the Systemic Risk of Major Financial Institutions, J, Bank Finance, 2036-2049.
Madan, D., Pistorius, M., & Schoutens, W. (2013). The Valuation of Structured Products Using Markov Chain Models. Quantitative Finance, 13, 125-136.
Rodriguez , M., & Pena, J. (2013).Systemic Risk Measures,Working Paper.
Sadeghi, M. (2011). Systemic Risk Mitigation in Capital Market. (In Persian).
Segoviano, M., & Goodhart, C. (2009), Banking Stability Measures, Working Paper.
Tse, Y., & Tsui, A. (2002), A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Timevarying Correlations, Business & Economic Statistics, 3, 351-362.