Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download
Contributor : ali gannoun Connect in order to contact the contributor
Submitted on : Tuesday, November 29, 2016 - 11:18:39 AM
Last modification on : Tuesday, July 5, 2022 - 10:18:02 AM
Long-term archiving on: : Monday, March 20, 2017 - 9:16:38 PM


Files produced by the author(s)


  • HAL Id : hal-01404752, version 1


Hanene Ben Salah, Ali Gannoun, Mathieu Ribatet. Conditional Mean-Variance and Mean-Semivariance models in portfolio optimization. 2016. ⟨hal-01404752⟩



Record views


Files downloads