Improving portfolios global performance using a cleaned and robust covariance matrix estimate

Emmanuelle Jay (), Thibault Soler (), Eugénie Terreaux (), Jean-Philippe Ovarlez (), Frédéric Pascal (), Philippe de Peretti () and Christophe Chorro ()
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Emmanuelle Jay: Quanted & Europlace Institute of Finance, Fideas Capital
Thibault Soler: Fideas Capital, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Eugénie Terreaux: DEMR, ONERA, Université Paris Saclay (COmUE) [Palaiseau] - ONERA - Université Paris Saclay (COmUE)
Jean-Philippe Ovarlez: DEMR, ONERA, Université Paris Saclay (COmUE) [Palaiseau] - ONERA - Université Paris Saclay (COmUE)
Frédéric Pascal: L2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique, CentraleSupélec
Philippe de Peretti: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne
Christophe Chorro: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne

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Abstract: This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimization problems such as the Minimum Variance Portfolio. We assume that the most important information (or the latent factors) are embedded in correlated Elliptical Symmetric noise extending classical Gaussian assumptions. We propose here to focus on a recent method of model order selection allowing to efficiently estimate the subspace of main factors describing the market. This non-standard model order selection problem is solved through Random Matrix Theory and robust covariance matrix estimation. Moreover we extend the method to non-homogeneous assets returns. The proposed procedure will be explained through synthetic data and be applied and compared with standard techniques on real market data showing promising improvements.

Keywords: Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimisation; Financial Time Series; Multi-Factor Model; Elliptical Symmetric Noise; Maximum Variety Portfolio (search for similar items in EconPapers)
Date: 2019-10
New Economics Papers: this item is included in nep-ecm and nep-rmg
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02354596
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Citations: View citations in EconPapers (3)

Published in 2019

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