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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2020. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02508748, HAL.
    • Handle: RePEc:hal:cesptp:hal-02508748
    DOI: 10.1007/s00500-020-04840-9
    Note: View the original document on HAL open archive server: https://hal.science/hal-02508748v1
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    Cited by:

    1. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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