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

Hanene Ben Salah, Ali Gannoun and Mathieu Ribatet ()
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Hanene Ben Salah: IMAG - Institut Montpelliérain Alexander Grothendieck - UM - Université de Montpellier - CNRS - Centre National de la Recherche Scientifique, Laboratoire BESTMOD ISG Tunis - ISG Tunis, 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: In this paper, we consider the problem of portfolio optimization. The risk will be measured by conditional variance or semivariance. It is known that the historical returns used to estimate expected ones provide poor guides to future returns. Consequently, the optimal portfolio asset weights are extremely sensitive to the return assumptions used. Getting informations about the future evolution of different asset returns, could help the investors to obtain more efficient portfolio. The solution will be reached under conditional mean estimation and prediction. This strategy allows us to take advantage from returns prediction which will be obtained by nonparametric univariate methods. Prediction step uses kernel estimation of conditional mean. Application on Chinese and American markets are presented and discussed.

Keywords: Conditional Semivariance; Conditional Variance; DownSide Risk; Kernel Method; Nonparametric Mean prediction (search for similar items in EconPapers)
Date: 2016-04-12
Note: View the original document on HAL open archive server: https://hal.science/hal-01299566v1
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Citations: View citations in EconPapers (1)

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Working Paper: Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization (2016)
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