IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1111.2091.html
   My bibliography  Save this paper

Performance-based regularization in mean-CVaR portfolio optimization

Author

Listed:
  • Noureddine El Karoui
  • Andrew E. B. Lim
  • Gah-Yi Vahn

Abstract

We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the available log-return data is iid, and detail the approach for two cases: nonparametric and parametric (the log-return distribution belongs in the elliptical family). The nonparametric PBR method penalizes portfolios with large variability in mean and CVaR estimations. The parametric PBR method solves the empirical Markowitz problem instead of the empirical mean-CVaR problem, as the solutions of the Markowitz and mean-CVaR problems are equivalent when the log-return distribution is elliptical. We derive the asymptotic behavior of the nonparametric PBR solution, which leads to insight into the effect of penalization, and justification of the parametric PBR method. We also show via simulations that the PBR methods produce efficient frontiers that are, on average, closer to the population efficient frontier than the empirical approach to the mean-CVaR problem, with less variability.

Suggested Citation

  • Noureddine El Karoui & Andrew E. B. Lim & Gah-Yi Vahn, 2011. "Performance-based regularization in mean-CVaR portfolio optimization," Papers 1111.2091, arXiv.org, revised Mar 2012.
  • Handle: RePEc:arx:papers:1111.2091
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1111.2091
    File Function: Latest version
    Download Restriction: no

    Citations

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


    Cited by:

    1. Jun-Ya Gotoh & Keita Shinozaki & Akiko Takeda, 2013. "Robust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 13(10), pages 1621-1635, October.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1111.2091. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.