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14.03.2020 | Focus

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

verfasst von: Emmanuelle Jay, Thibault Soler, Eugénie Terreaux, Jean-Philippe Ovarlez, Frédéric Pascal, Philippe De Peretti, Christophe Chorro

Erschienen in: Soft Computing | Ausgabe 12/2020

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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.

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Titel: Improving portfolios global performance using a cleaned and robust covariance matrix estimate
verfasst von: Emmanuelle Jay
Thibault Soler
Eugénie Terreaux
Jean-Philippe Ovarlez
Frédéric Pascal
Philippe De Peretti
Christophe Chorro
Publikationsdatum: 14.03.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04840-9

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