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: Fidéas Capital, Quanted & Europlace Institute of Finance
Thibault Soler: Fidéas Capital et Centre d'Economie de la Sorbonne
Jean-Philippe Ovarlez: DEMR, ONERA - Université Paris-Saclay
Philippe De Peretti: Centre d'Economie de la Sorbonne - Université Paris 1Panthéon-Sorbonne
Christophe Chorro: Centre d'Economie de la Sorbonne - Université Paris 1 Panthéon-Sorbonne, https://sites.google.com/view/chorro-christophe/

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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: Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimization; Elliptical Symmetric Noise (search for similar items in EconPapers)
JEL-codes: C5 G11 (search for similar items in EconPapers)
Pages: 6 pages
Date: 2019-10
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:19023

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