IDEAS home Printed from https://ideas.repec.org/p/hal/cesptp/hal-02508748.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://hal.science/hal-02508748v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1007/s00500-020-04840-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. M. Potters & J. P. Bouchaud & L. Laloux, 2005. "Financial Applications of Random Matrix Theory: Old Laces and New Pieces," Papers physics/0507111, arXiv.org.
    3. repec:dau:papers:123456789/4688 is not listed on IDEAS
    4. Joel Bun & Romain Allez & Jean-Philippe Bouchaud & Marc Potters, 2015. "Rotational invariant estimator for general noisy matrices," Papers 1502.06736, arXiv.org, revised Oct 2016.
    5. Hachem, Walid & Loubaton, Philippe & Mestre, Xavier & Najim, Jamal & Vallet, Pascal, 2013. "A subspace estimator for fixed rank perturbations of large random matrices," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 427-447.
    6. 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.
    7. 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.
    8. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    9. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    10. Joël Bun & Jean-Philippe Bouchaud & Marc Potters, 2017. "Cleaning large correlation matrices: tools from random matrix theory," Post-Print hal-01491304, HAL.
    11. Cambanis, Stamatis & Huang, Steel & Simons, Gordon, 1981. "On the theory of elliptically contoured distributions," Journal of Multivariate Analysis, Elsevier, vol. 11(3), pages 368-385, September.
    12. Couillet, Romain, 2015. "Robust spiked random matrices and a robust G-MUSIC estimator," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 139-161.
    13. Serge Darolles & Patrick Duvaut & Emmanuelle Jay, 2013. "Multi-factor models and signal processing techniques: application to quantitative finance," Post-Print hal-01632892, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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," Post-Print hal-02508748, HAL.
    2. 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.
    3. 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.
    4. 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.
    5. Yan Zhang & Jiyuan Tao & Zhixiang Yin & Guoqiang Wang, 2022. "Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    6. Mishra, Anil V., 2016. "Foreign bias in Australian-domiciled mutual fund holdings," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 101-123.
    7. Mishra, Anil V., 2015. "Measures of equity home bias puzzle," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 293-312.
    8. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
    9. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2014. "$L_p$ regularized portfolio optimization," Papers 1404.4040, arXiv.org.
    10. Vincent Tan & Stefan Zohren, 2020. "Estimation of Large Financial Covariances: A Cross-Validation Approach," Papers 2012.05757, arXiv.org, revised Jan 2023.
    11. 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.
    12. Nhat Minh Nguyen & Trung Duc Nguyen & Eleftherios I. Thalassinos & Hoang Anh Le, 2022. "The Performance of Shrinkage Estimator for Stock Portfolio Selection in Case of High Dimensionality," JRFM, MDPI, vol. 15(6), pages 1-12, June.
    13. Sumanjay Dutta & Shashi Jain, 2023. "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation," Papers 2305.11298, arXiv.org.
    14. Christian Bongiorno & Damien Challet, 2023. "The Oracle estimator is suboptimal for global minimum variance portfolio optimisation," Post-Print hal-03491913, HAL.
    15. Xi Luo, 2011. "Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation," Papers 1111.1133, arXiv.org, revised Mar 2013.
    16. An Pham Ngoc Nguyen & Marija Bezbradica & Martin Crane, 2025. "Community-level Contagion among Diverse Financial Assets," Papers 2509.15232, arXiv.org.
    17. Yan, Cheng & Zhang, Huazhu, 2017. "Mean-variance versus naïve diversification: The role of mispricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 61-81.
    18. Alex Shkolnik & Alec Kercheval & Hubeyb Gurdogan & Lisa R. Goldberg & Haim Bar, 2025. "Portfolio selection revisited," Annals of Operations Research, Springer, vol. 346(1), pages 137-155, March.
    19. Long Zhao & Deepayan Chakrabarti & Kumar Muthuraman, 2019. "Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio," Operations Research, INFORMS, vol. 67(4), pages 965-983, July.
    20. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2019. "Exponent of Cross-sectional Dependence for Residuals," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 46-102, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:hal-02508748. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.