IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v51y2024i1p34-52.html
   My bibliography  Save this article

A new method for estimating Sharpe ratio function via local maximum likelihood

Author

Listed:
  • Wenchao Xu
  • Hongmei Lin
  • Tiejun Tong
  • Riquan Zhang

Abstract

The Sharpe ratio function is a commonly used risk/return measure in financial econometrics. To estimate this function, most existing methods take a two-step procedure that first estimates the mean and volatility functions separately and then applies the plug-in method. In this paper, we propose a direct method via local maximum likelihood to simultaneously estimate the Sharpe ratio function and the negative log-volatility function as well as their derivatives. We establish the joint limiting distribution of the proposed estimators, and moreover extend the proposed method to estimate the multivariate Sharpe ratio function. We also evaluate the numerical performance of the proposed estimators through simulation studies, and compare them with existing methods. Finally, we apply the proposed method to the three-month US Treasury bill data and that captures a well-known covariate-dependent effect on the Sharpe ratio.

Suggested Citation

  • Wenchao Xu & Hongmei Lin & Tiejun Tong & Riquan Zhang, 2024. "A new method for estimating Sharpe ratio function via local maximum likelihood," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(1), pages 34-52, January.
    • Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:34-52
    DOI: 10.1080/02664763.2022.2114431
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2022.2114431
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2022.2114431?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:japsta:v:51:y:2024:i:1:p:34-52. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.