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

Author

Listed:
  • 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
    https://centredeconomiesorbonne.univ-paris1.fr)

  • Christophe Chorro

    (Centre d'Economie de la Sorbonne - Université Paris 1 Panthéon-Sorbonne
    https://centredeconomiesorbonne.univ-paris1.fr)

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

Suggested Citation

  • Emmanuelle Jay & Thibault Soler & Jean-Philippe Ovarlez & Philippe De Peretti & Christophe Chorro, 2019. "Robust covariance matrix estimation and portfolio allocation: the case of non-homogeneous assets," Documents de travail du Centre d'Economie de la Sorbonne 19023, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:19023
    as

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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2019/19023.pdf
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    References listed on IDEAS

    as
    1. M. Potters & J. P. Bouchaud & L. Laloux, 2005. "Financial Applications of Random Matrix Theory: Old Laces and New Pieces," Papers physics/0507111, arXiv.org.
    2. repec:dau:papers:123456789/4688 is not listed on IDEAS
    3. Liusha Yang & Romain Couillet & Matthew R. McKay, 2015. "A Robust Statistics Approach to Minimum Variance Portfolio Optimization," Papers 1503.08013, arXiv.org.
    4. Plerou, V. & Gopikrishnan, P. & Rosenow, B. & Amaral, L.A.N. & Stanley, H.E., 2001. "Collective behavior of stock price movements—a random matrix theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 175-180.
    5. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    6. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe De Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Documents de travail du Centre d'Economie de la Sorbonne 19022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02354596, HAL.
    8. Emmanuelle Jay & Thibault Soler & Eugénie Terreaux & Jean-Philippe Ovarlez & Frédéric Pascal & Philippe de Peretti & Christophe Chorro, 2019. "Improving portfolios global performance using a cleaned and robust covariance matrix estimate," Post-Print halshs-02354596, HAL.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Robust Covariance Matrix Estimation; Model Order Selection; Random Matrix Theory; Portfolio Optimization; Elliptical Symmetric Noise;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:

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