GJR-Copula-CVaR Model for Portfolio Optimization: Evidence for Emerging Stock Markets

Author

Department of Financial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

Abstract

Abstract





T





his paper empirically examines the impact of dependence structure between the assets on the portfolio optimization, composed of Tehran Stock Exchange Price Index and Borsa Istanbul 100 Index. In this regard, the method of the Copula family functions is proposed as powerful and flexible tool to determine the structure of dependence. Finally, the impact of the dependence structure on the risk identification and the optimized portfolio selection, will be analyzed. The results show that the t-student copula function provides the best performance among other Copula functions. Also, empirical evidence suggests that the performance of the GJR-Copula-CVaR method is relatively more accurate and more flexible than other common methods of optimization.
 

Keywords

References

Ang, A., & Bekaert, G. (2002). International Asset Allocation with Regime Shifts. Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Chen, J. (2002). Asymmetric Correlations of Equity Portfolios. Journal of Financial Economics, 63(3), 443-494.
Beine, M. (2004). Conditional Covariance’s and Direct Central Bank Interventions in the Foreign Exchange Markets. Journal of Banking & Finance, 28(6), 1385-1411.
Boubaker, H., & Sghaier, N. (2013). Portfolio Optimization In The Presence Of Dependent Financial Returns with Long Memory: A Copula Based Approach. Journal of Banking & Finance, 37(2), 361-377.
Brooks, C., Burke, S., Heravi, S., & Persand, G. (2005). Autoregressive Conditional Kurtosis. Journal of Financial Econometrics, 3(3), 399-421.
Chen, Y. H., & Tu, A. (2013). Estimating Hedged Portfolio Value-at-Risk Using The Conditional Copula: An Illustration Of Model Risk. International Review of Economics & Finance, 27(C), 514-528.
Das, S., & Uppal, R. (2004). Systemic Risk and International Portfolio Choice. Journal of Finance, 59(6), 2809-2834.
Embrechts, P., McNeil, A., & Straumann, D. (2002). Correlation and Dependence in Risk Management: Properties and Pitfalls. In M. A. H. Dempster (Ed.), Risk Management: Value at Risk and Beyond (176-223). Cambridge: Cambridge University Press.
Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
Fantazzini, D. (2008). Dynamic Copula Modelling for Value at Risk. Frontiers in Finance and Economics, 5(2), 72-108.
Frank, M. (1979). On The Simultaneous Associativity of F(x, y) and x+y-F(x,y). Aequationes Mathematicae, 19(1), 194-226.
Glosten, L., Jagannathan, R., & Runkle, D. (1993). On The Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.
Gumbel, E. (1960). Bivariate Exponential Distributions. Journal of American Statistical Association, 55(292), 698-707.
Hartmann, P., Straetmans, S., & DeVries, C. (2004). Asset Market Linkages in Crisis Periods. The Review of Economics and Statistics, 86(1), 313-326.
Harvey, C., & Siddique, A. (1999). Autoregressive Conditional Skewness. Journal of Financial and Quantitative Analysis, 34(4), 465-487.
He, X., & Gong, P. (2009). Measuring the Coupled Risks: A Copula-Based CVaR Model. Journal of Computational and Applied Mathematics, 223(2), 1066-1080.
Huang, J. J., Lee, K. J., Liang, H., & Lin, W. F. (2009). Estimating Value at Risk of Portfolio by Conditional Copula-GARCH Method. Insurance: Mathematics and Economics, 45(3), 315-324.
Mesfioui, M., & Quessy, J. F. (2005). Bounds on the Value-at-Risk for the Sum of Possibly Dependent Risks. Insurance: Mathematics and Economics, 37(1), 135-151.
Palaro, H., & Hotta, L. (2006). Using Conditional Copula to Estimate Value at Risk. Journal of Data Science, 4(1), 93-115.
Patton, A. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2), 527-556.
---------- (2002). Applications of Copula Theory in Financial Econometrics (Unpublished Doctoral Dissertation), University of California, USA.
Poon, S. H., Rockinger, M., & Tawn, J. (2004). Modelling Extreme-Value Dependence in International Stock Markets. Statistica Sinica, 13(4), 929-953.
Sadique, S., & Silvapulle, P. (2001). Long-term Memory in Stock Market Returns: International Evidence. International Journal of Finance & Economics, 6(1), 59-67.
Schmidt, R. (2003). Dependencies of Extreme Events in Finance: Modelling, Statistics and Data Analysis (Unpublished Doctoral Dissertation), Ulm University, Germany.
Sklar, A. (1959). Fonctions de Répartition À N Dimensions Et Leurs Marges. Paris: Université Paris.
Song, P. K. (2000). Multivariate Dispersion Models Generated From Gaussian Copula. Scandinavian Journal of Statistics, 27(2), 305-320.
Iranian Economic Review
Volume 22, Issue 4
October 2018
Pages 990-1015

Files

Share

How to cite

Statistics