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The optimal portfolio model based on multivariate t distribution with linear weighted sum method

Author

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  • Xing Yu

    (Department of Mathematics and Applied Mathematics Humanities and Science and Technology Institute of Hunan Loudi, 417000, P.R. China)

Abstract

This paper proposed the optimal portfolio model maximizing returns and minimizing the risk expressed as CvaR under the assumption that the portfolio yield is subject to the multivariate t distribution. With linear weighted sum method, we solved the multi-objectives model, and compared the model results to the case under the assumption of normal distribution return, based on the portfolio VAR through empirical research. It is showed that our max return equals to and risk is higher than M-V model. It shows that CVaR predicts the potential risk of the portfolio, which is helpful for investor’s cautious investment.

Suggested Citation

  • Xing Yu, 2012. "The optimal portfolio model based on multivariate t distribution with linear weighted sum method," E3 Journal of Business Management and Economics., E3 Journals, vol. 3(1), pages 044-047.
    • Handle: RePEc:etr:series:v:3:y:2012:i:1:p:044-047
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    References listed on IDEAS

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    1. Mittnik, Stefan & Paolella, Marc S. & Rachev, Svetlozar T., 2002. "Stationarity of stable power-GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 97-107, January.
    2. Mittnik, Stefan & Paolella, Marc S., 2003. "Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions," CFS Working Paper Series 2003/04, Center for Financial Studies (CFS).
    3. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
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