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

Author

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  • Moien Nikusokhan

    (Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.)

Abstract

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.

Suggested Citation

  • Moien Nikusokhan, 2018. "GJR-Copula-CVaR Model for Portfolio Optimization: Evidence for Emerging Stock Markets," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 22(4), pages 990-1015, Autumn.
    • Handle: RePEc:eut:journl:v:22:y:2018:i:4:p:990
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    Cited by:

    1. Nader Trabelsi & Aviral Kumar Tiwari, 2019. "Market-Risk Optimization among the Developed and Emerging Markets with CVaR Measure and Copula Simulation," Risks, MDPI, vol. 7(3), pages 1-20, July.
    2. Fazlollah Soleymani & Mahdi Vasighi, 2022. "Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3679-3693, July.

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