Using Bayesian Approach to Study the Time Varying Correlation among Selected Indices of Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Economics, Faculty of Economic and Management, Urmia University, Urmia, Iran

2 Prof., Department of Financial Economics, Faculty of Economic and Management, Urmia University, Urmia, Iran

3 Assistant Prof., Department of Economics, Faculty of Economic and Management, Urmia University, Urmia, Iran

4 Assistant Prof., Department of Economics, Faculty of Economic, Kharazmi University, Tehran, Iran

Abstract

Objective: The effect of the contagion between financial assets is one of the most important challenges that investors are faced with. Financial markets have been severely affected by economic, political, and social events and they are subject to volatility and turmoil that causes financial investors and analysts to be disturbed. Therefore, the main objective of the present study is to investigate the effect of shocks on the return volatility of the selected indices.
Methods: The main objective of this study is to use random simulation techniques for GARCH models considering multivariate skewed distributions using the Bayesian approach. For this purpose, we used the daily return data of selected Tehran Stock Exchange (TSE) indices during the period from December 13, 2009 to Jan 29, 2003 and also the flexible types of multivariate distributions that can model both skewedness and fat tail.
Results: The results of the Bayesian DCC GARCH (1.1) model showed that the effect of shocks on the return volatility is not the same  for the selected indices and this situation implies different risk conditions and uncertainty in the returns of these groups. The results also showed that the stock returns of the automotive group spread more volatility from the previous day to the current day in comparison to Banking group and Petroleum group respectively.
Conclusion: Shocks on variables affects the correlations among them. Also, GED distribution was identified as an appropriate distribution in line with the characteristics of the skewedness of the studied indices. Therefore, in estimating the risk of assets and portfolio optimization, one can exploit the main idea used in this study.

Keywords

Main Subjects


References
Aielli, G. P. (2013). Dynamic conditional correlation: on properties and estimation. Journal of Business & Economic Statistics, 31(3), 282-299.‏
Allen, D., Amram, R., & McAleer, M. (2011). Volatility spillovers from the Chinese stock market to economic neighbors.‏ Mathematics and Computers in Simulation, 94, 238-257.
Ardia, D. (2008). Bayesian estimation of the GARCH (1,1) model with normal innovations. Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications, 17-37.‏
Asai, M. (2016). Bayesian Analysis of General Asymmetric Multivariate GARCH Models and News Impact Curves. Journal of the Japan Statistical Society, 45(2), 129-144.‏
Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 74(1), 3-30.‏
Bala, D. A., & Takimoto, T. (2017). Stock market's volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach. Borsa Istanbul Review, 17(1), 25-48.‏
Bauwens, L., Hafner, C. M., & Pierret, D. (2013). Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics, 28(5), 743-761.‏
Bauwens, L., & Laurent, S. (2005). A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. Journal of Business & Economic Statistics23(3), 346-354.‏
Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109.‏
Bekaert, G., Harvey, C.R., & Ng, A. (2005). Market integration and contagion. The Journal of Business, 78, 39-69.
BenSaïda, A. (2018). The contagion effect in European sovereign debt markets: A regime-switching vine copula approach. International Review of Financial Analysis, 58, 153-165.‏
Berben, R. P., & Jansen, W. J. (2005). Comovement in international equity markets: A sectoral view. Journal of International Money and Finance, 24(5), 832-857.‏
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.‏
Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The review of economics and statistics, 72(3), 498-505.‏
Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. Journal of political Economy, 96(1), 116-131.‏
Bonga-Bonga, L. (2018). Uncovering equity market contagion among BRICS countries: an application of the multivariate GARCH model. The Quarterly Review of Economics and Finance, 67, 36-44.‏
De Grauwe, P. (2012). Lectures on behavioral macroeconomics. Princeton University Press.‏
Doan, T. A. (2013). RATS handbook for ARCH/GARCH and volatility models, June, 2013.
Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: understanding how it spreads. The World Bank Research Observer, 15(2), 177-197.‏
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.‏
Engle, R. (2004). Risk and volatility: Econometric models and financial practice. American economic review, 94(3), 405-420.‏
Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150.‏
Engle, R. F. (2011). Long-term skewness and systemic risk. Journal of Financial Econometrics, 9(3), 437-468.‏
Fernández, C., & Steel, M. F. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371.‏
Fiorucci, J.A., Ehlers, R.S., Louzada, F., & Fiorucci, M.J. A. (2016). Package ‘bayesDccGarch’.‏ http://cran.ism.ac.jp/web/packages/bayesDccGarch/bayesDccGarch.pdf.
Fleming, J., Kirby, C., & Ostdiek, B. (1998). Information and volatility linkages in the stock, bond, and money markets1. Journal of financial economics, 49(1), 111-137.
‏Forbes, K., & Rigobon, R. (2001). Measuring contagion: conceptual and empirical issues. In International financial contagion (pp. 43-66). Springer, Boston, MA.‏
Frank, N., & Hesse, H. (2009). Financial spillovers to emerging markets during the global financial crisis (No. 9-104). International Monetary Fund.‏
Gómez, E., Gomez-Viilegas, M. A., & Marin, J. M. (1998). A multivariate generalization of the power exponential family of distributions. Communications in Statistics-Theory and Methods, 27(3), 589-600.‏
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance48(5), 1779-1801.‏
He, C., Silvennoinen, A., & Teräsvirta, T. (2008). Parameterizing unconditional skewness in models for financial time series. Journal of Financial Econometrics, 6(2), 208-230.‏
Heidari, H., Molabahrami, A. (2012). Portfolio Optimization Using Multivariate GARCH Models: Evidence from Tehran Stock Exchange. Financial Research Journal, 12(30), 35-56. (in Persian)
Hein, L. U. (2015). Investigating Correlation and Volatility Transmission among Equity, Gold, Oil and Foreign Exchange. MaRBLe, 2.‏
Hong, H., & Stein, J. C. (2003). Differences of opinion, short-sales constraints, and market crashes. The Review of Financial Studies, 16(2), 487-525.‏
Karami, S., Rastegar, M. (2018). Estimation of Return and Volatilities Spillover between Different Industries of Tehran Stocks’ Exchange. Financial Engineering and Portfolio Management , 9(35), 323-342. (in Persian)
Khalifa, A. A., Hammoudeh, S., & Otranto, E. (2014). Patterns of volatility transmissions within regime switching across GCC and global markets. International Review of Economics & Finance, 29, 512-524.‏
Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. The review of financial studies, 11(4), 817-844.‏
Laurent, S., Boudt, K., & Danielsson, J. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244-257.‏
Lin, W. L., Engle, R. F., & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. Review of Financial Studies, 7(3), 507-538.‏
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.‏
Massacci, D. (2014). A two-regime threshold model with conditional skewed Student t distributions for stock returns. Economic Modelling, 43, 9-20.‏
Masson, M. P. R. (1998). Contagion: Monsoonal effects, spillovers, and jumps between multiple equilibria (No. 98-142). International Monetary Fund.‏
McAleer, M., Hoti, S., & Chan, F. (2009). Structure and asymptotic theory for multivariate asymmetric conditional volatility. Econometric Reviews, 28(5), 422-440.‏
Nasrollahi, Z., Tyebi, R., Fotovat, A., & Eskandaripour, Z . (2018). Transmission of Volatility between Stock Markets of Iran, India and Turkey Using BEKK-GARCH Model. Financial Monetary Economics, 25(15), 77-92. (in Persian)
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.‏
Rostami, Mohammad Reza, & Haqiqi, Fatemeh. (2013). Using MGARCH to Estimate Value at Risk. Financial Research Journal, 15 (2), 215-228. (in Pesrsian)
Salisu, A. A. (2016). Modelling oil price volatility with the Beta-Skew-t-EGARCH framework. Economics Bulletin, 36(3), 1315-1324.‏
Shahyaki Tash, M., Mirbagherijam, M. (2015). Survey the asymmetric correlation between stock return, trading volume and volatility of Tehran stock exchange market (DCC-GARCH Approach). Journal of Economic Research (Tahghighat- E- Eghtesadi), 50(2), 359-387. (in Persian)
Stavroyiannis, S. (2017). Is the BRICS decoupling effect reversing? Evidence from dynamic models. International Journal of Economics and Business Research, 13(3), 303-315.‏
Tsutsui, Y. (2002). The interdependence and cause of Japanese and US stock prices: an event study. Asian Economic Journal, 16(2), 97-109.‏
Tse, Y. K., & Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics20(3), 351-362.‏
Virbickaitė, A., Ausín, M. C., & Galeano, P. (2016). A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection. Computational Statistics & Data Analysis100, 814-829.‏