Title: A new approach in non-parametric estimation of returns in mean-downside risk portfolio frontier
Authors: Hanene Ben Salah; Ali Gannoun; Mathieu Ribatet
Addresses: BESTMOD Laboratory, ISG 41 Rue de la Liberté, Cité Bouchoucha 2000 Le Bardo, Tunisia; Institut de Science Financiére et d'Assurance 69366 Lyon cedex 07, France; IMAG, Univ Montpellier, CNRS, 34095 Montpellier, France ' IMAG, Univ Montpellier, CNRS, 34095 Montpellier, France ' IMAG, Univ Montpellier, CNRS, 34095 Montpellier, France
Abstract: The downside risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymmetry of returns and the risk perception of investors. This optimisation model deals with a positive definite matrix that is endogenous with respect to the portfolio weights and hence yields to a non-standard optimisation problem. In this paper we develop a new method and an algorithm to solve this optimisation problem which typically yields to a smoother portfolio frontier. Our proposal is based on non-parametric estimation, using kernel methods of mean and median. An application to the French and Brazilian stock markets is given.
Keywords: downside risk; kernel method; non-parametric estimation; semivariance; portfolio allocation.
International Journal of Portfolio Analysis and Management, 2018 Vol.2 No.2, pp.169 - 197
Received: 26 Sep 2017
Accepted: 04 Dec 2017
Published online: 14 Jun 2018 *