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

Author

Listed:
  • Hanene Ben 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)

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.

Suggested Citation

  • Hanene Ben Salah & Ali Gannoun & Mathieu Ribatet, 2016. "Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization," Working Papers hal-01404752, HAL.
    • Handle: RePEc:hal:wpaper:hal-01404752
    Note: View the original document on HAL open archive server: https://inria.hal.science/hal-01404752v1
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    Cited by:

    1. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.

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