IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02612262.html

Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning

Author

Listed:
  • Christian Bongiorno

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay)

  • Damien Challet

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay)

Abstract

We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices. We then apply this method to global minimum variance portfolios for various values of $k$ and compare their performance with other state-of-the-art methods. Generally, we find that our method yields better Sharpe ratios after transaction costs than competing filtering methods, despite requiring a larger turnover.

Suggested Citation

  • Christian Bongiorno & Damien Challet, 2021. "Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning," Post-Print hal-02612262, HAL.
    • Handle: RePEc:hal:journl:hal-02612262
    DOI: 10.1080/1351847X.2021.1963301
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

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


    Cited by:

    1. is not listed on IDEAS
    2. Christian Bongiorno & Efstratios Manolakis & Rosario Nunzio Mantegna, 2025. "End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning," Papers 2507.01918, arXiv.org, revised Jul 2025.
    3. Bongiorno, Christian & Challet, Damien, 2023. "Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimization," Finance Research Letters, Elsevier, vol. 52(C).
    4. Bongiorno, Christian & Lamrani, Lamia, 2025. "Quantifying the information lost in optimal covariance matrix cleaning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).

    More about this item

    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:journl:hal-02612262. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.