IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v46y2025i2p353-377.html
   My bibliography  Save this article

Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations

Author

Listed:
  • Marc S. Paolella
  • Paweł Polak
  • Patrick S. Walker

Abstract

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.

Suggested Citation

  • Marc S. Paolella & Paweł Polak & Patrick S. Walker, 2025. "Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations," Journal of Time Series Analysis, Wiley Blackwell, vol. 46(2), pages 353-377, March.
  • Handle: RePEc:bla:jtsera:v:46:y:2025:i:2:p:353-377
    DOI: 10.1111/jtsa.12792
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12792
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jtsa.12792?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
    ---><---

    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:bla:jtsera:v:46:y:2025:i:2:p:353-377. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.