Robust covariance matrix estimation and portfolio allocation: the case of non-homogeneous assets

Emmanuelle Jay (), Thibault Soler (), Jean-Philippe Ovarlez (), Philippe de Peretti () and Christophe Chorro ()
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Emmanuelle Jay: Fideas Capital, Quanted & Europlace Institute of Finance
Thibault Soler: Fideas Capital, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Jean-Philippe Ovarlez: DEMR, ONERA, Université Paris Saclay (COmUE) [Palaiseau] - ONERA - Université Paris Saclay (COmUE)
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 most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. Our study is based on the case of the Maximum Variety Portfolio and may be obviously extended to other classical frameworks with analogous results. We focus on the fact that the assets should preferably be classified in homogeneous groups before applying the proposed methodology which is to whiten the data before estimating the covariance matrix using the robust Tyler M-estimator and the Random Matrix Theory (RMT). The proposed procedure is applied and compared to standard techniques on real market data showing promising improvements.

Keywords: Elliptical Symmetric Noise; Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimization (search for similar items in EconPapers)
Date: 2019-10
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02372443
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Published in 2019

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