Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization

Hanene Salah, Ali Gannoun and Mathieu Ribatet ()
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Hanene Salah: Laboratoire BESTMOD ISG Tunis - ISG Tunis, IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique, LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon
Ali Gannoun: IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique
Mathieu Ribatet: IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique
Authors registered in the RePEc Author Service: Christian de Peretti

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Abstract: It is known that the historical observed returns used to estimate the expected return provide poor guides to predict the future returns. Consequently, the optimal portfolio weights are extremely sensitive to the return assumptions used. Getting information about the future evolution of different asset returns, could help the investors to obtain more efficient portfolio. The solution will be reached by estimating the portfolio risk by conditional variance or conditional semivari-ance. This strategy allows us to take advantage of returns prediction which will be obtained by nonparametric univariate methods. Prediction step uses kernel estimation of conditional mean. Application on the Chinese and the American markets are presented and discussed.

Keywords: Conditional Semivariance; DownSide Risk; Conditional Variance; Kernel Method; Nonparametric Mean prediction (search for similar items in EconPapers)
Date: 2016-11-29
New Economics Papers: this item is included in nep-rmg
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Working Paper: Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization (2016)
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