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 ()
Additional contact information
Emmanuelle Jay: Fidéas Capital, Quanted & Europlace Institute of Finance
Thibault Soler: Fidéas Capital et Centre d'Economie de la Sorbonne
Eugénie Terreaux: DEMR, ONERA - Université Paris-Saclay
Jean-Philippe Ovarlez: DEMR, ONERA - Université Paris-Saclay
Frédéric Pascal: L2S, Centrale Supélec - 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. 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. Morepver 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 Optimization; Financial Time Series; Multi-Factor Model; Elliptical Symmetric Noise; Maximum Variety Portfolio (search for similar items in EconPapers)
JEL-codes: C5 G11 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2019-10
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:19022

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