IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v1y2017i1p112-120.html
   My bibliography  Save this article

Portfolio optimisation using constrained hierarchical bayes models

Author

Listed:
  • Jiangyong Yin
  • Xinyi Xu

Abstract

It is well known that traditional mean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors. Constrained solutions, such as no-short-sale-constrained and norm-constrained portfolios, can usually achieve much higher ex post Sharpe ratio. Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions. In this paper, we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model. We show that such constructed portfolios are well diversified with superior out-of-sample performance. Our proposed model is tested on a number of Fama–French industry portfolios against the naïve diversification strategy and Chevrier and McCulloch’s (2008) economically motivated prior (EMP) strategy. On average, our model outperforms Chevrier and McCulloch’s (2008) EMP strategy by over 15% and outperform the ‘1/N’ strategy by over 50%.

Suggested Citation

  • Jiangyong Yin & Xinyi Xu, 2017. "Portfolio optimisation using constrained hierarchical bayes models," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 1(1), pages 112-120, January.
  • Handle: RePEc:taf:tstfxx:v:1:y:2017:i:1:p:112-120
    DOI: 10.1080/24754269.2017.1347310
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2017.1347310
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/24754269.2017.1347310?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:tstfxx:v:1:y:2017:i:1:p:112-120. 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/tstf .

    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.