The Optimal Portfolio Model Based on Mean-CVaR
Xing Yu, Hongguo Sun, Guohua Chen
DOI: 10.4236/jmf.2011.13017   PDF         5,317 Downloads   10,522 Views   Citations


This paper proposed the optimal portfolio model maximizing returns and minimizing the risk expressed as CvaR under the assumption that the portfolio yield subject to heavy tail. We use fuzzy mathematics method to solve the multi-objectives model, and compare the model results to the case under the normal distribution yield assumption based on the portfolio VAR through empirical research. It is showed that our return is approximate to M-V model but risk is higher than M-V model. So it is illustrated that CVaR predicts the potential risk of the portfolio, which will help investors to cautious investment.

Share and Cite:

X. Yu, H. Sun and G. Chen, "The Optimal Portfolio Model Based on Mean-CVaR," Journal of Mathematical Finance, Vol. 1 No. 3, 2011, pp. 132-134. doi: 10.4236/jmf.2011.13017.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] H. M. Markowitz, “Portfolio Selection,” Journal of Fi- nance, Vol. 7, No. 1, 1952, pp. 77-91. doi:10.2307/2975974
[2] B. Mandelbrot, “The Variation of Certain Speculative Prices,” Journal of Business, Vol. 26, No. 4, 1963, pp. 394-419. doi:10.1086/294632
[3] E. Fama, “The Behavior of Stock Market Pricess,” Journal of Business, Vol. 38, No. 1, 1965, pp. 34-105. doi:10.1086/294743
[4] S. Mittnik, M. Paolella and S. Rachev, “Stationarity of Stable Power-GARCH Processes,” Journal of Econome- trics, Vol. 106, No. 1, 2002, pp. 97-107. doi:10.1016/S0304-4076(01)00089-6
[5] S. Mittnik and M. Paolella, “Prediction of Fnancial down- side-Risk with Heavy-Tailed Conditional Distributions,” In: S. T. Rachev, Ed., Handbook of Heavy Tailed Distributions in Finance, Elsevier, New York, 2003, pp. 385- 404. doi:10.1016/B978-044450896-6.50011-X
[6] R. T. Rockafellar and S. Uryasev, “Optimization of Conditional Value-at-Risk,” Journal of Risk, Vol. 2, 2000, pp. 21-41.
[7] G. Ch. Pflug, “Some Remarks on the Val-ue-At-Risk and the Conditional Value-At-Risk,” In: S. Uryasev, Ed., Pro- babilistic Constrained Optimization: Methodology and Applications, Kluwer Academic Publishers, Dordre- cht, 2000.
[8] W. Ogryczak and A. Ruszczynski, “Dual stochastic domi- nance and quantile risk measures,” International Trans- actions in Operational Research, Vol. 9, No. 5, 2002, pp. 661-680. doi:10.1111/1475-3995.00380
[9] S. Uryasev, “Condi-tional Value-at-Risk: Optimiza-tion Algorithms and Ap-plications,” Proceedings of the IEEE/- IAFE/INFORMS 2000 Conference on Computational Intelligence for Fi-nancial Engineering, New York, 26-28 March 2000, pp. 49-57.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.