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

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Abstract: This paper presents how the use of a cleaned and robust covariance matrix estimate can improve significantly the overall performance of maximum variety and minimum variance portfolios. We assume that the asset returns are modelled through a multi-factor model where the error term is a multivariate and correlated elliptical symmetric noise extending the classical Gaussian assumptions. The factors are supposed to be unobservable and we focus on a recent method of model order selection, based on the random matrix theory to identify the most informative subspace and then to obtain a cleaned (or de-noised) covariance matrix estimate to be used in the maximum variety and minimum variance portfolio allocation processes. We apply our methodology on real market data and show the improvements it brings if compared with other techniques especially for non-homogeneous asset returns.

Keywords: Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimization; Financial Time Series; Multi-Factor Model; Elliptical Symmetric Noise; Maximum Variety; Portfolio (search for similar items in EconPapers)
Date: 2020
Note: View the original document on HAL open archive server: https://hal.science/hal-02508748v1
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Published in Soft Computing, 2020, 24, pp.8643-8654. ⟨10.1007/s00500-020-04840-9⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:hal-02508748

DOI: 10.1007/s00500-020-04840-9

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