IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v49y2020i12p2831-2868.html
   My bibliography  Save this article

Time-consistent strategies for multi-period mean-variance portfolio optimization with the serially correlated returns

Author

Listed:
  • Helu Xiao
  • Zhongbao Zhou
  • Tiantian Ren
  • Yanfei Bai
  • Wenbin Liu

Abstract

In this paper, we discuss several different styles of multi-period mean-variance portfolio optimization problems under the serially correlated returns. We derive the time-consistent strategies for the classical multi-period mean-variance optimization with and without risk-free asset using a backward induction approach. We also propose an alternative multi-period mean-variance model, and the corresponding time-consistent strategies are derived. Whereafter, we provide some portfolio evaluation indexes and perform extensive empirical studies based on real data, aiming to provide useful advice for investors. To a large extent, the empirical results answer one important and practical question: in actual investment situations, which strategy is preferred by different investors?

Suggested Citation

  • Helu Xiao & Zhongbao Zhou & Tiantian Ren & Yanfei Bai & Wenbin Liu, 2020. "Time-consistent strategies for multi-period mean-variance portfolio optimization with the serially correlated returns," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(12), pages 2831-2868, June.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:12:p:2831-2868
    DOI: 10.1080/03610926.2019.1636999
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1636999
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1636999?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:lstaxx:v:49:y:2020:i:12:p:2831-2868. 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/lsta .

    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.