IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v70y2022i1p23-37.html
   My bibliography  Save this article

Time-Varying Risk Aversion and Dynamic Portfolio Allocation

Author

Listed:
  • Haitao Li

    (Cheung Kong Graduate School of Business, Beijing 100738, China)

  • Chongfeng Wu

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Chunyang Zhou

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

Abstract

We study the implications of time-varying risk aversion for dynamic portfolio allocation under the framework of regime-switching models. In our model, both asset returns and investor risk aversion are regime dependent: In a bull regime, asset return is high, volatility is low, and risk aversion is low, and the opposite happens in a bear regime. We develop an efficient dynamic programming algorithm that overcomes the challenges imposed by regime-dependent preference in obtaining time-consistent portfolio policies. Empirically, we show that CBOE Volatility Index (VIX) is an important predictor of regime shifts and investors with regime-dependent risk aversion achieve better investment performance than those with constant risk aversion.

Suggested Citation

  • Haitao Li & Chongfeng Wu & Chunyang Zhou, 2022. "Time-Varying Risk Aversion and Dynamic Portfolio Allocation," Operations Research, INFORMS, vol. 70(1), pages 23-37, January.
  • Handle: RePEc:inm:oropre:v:70:y:2022:i:1:p:23-37
    DOI: 10.1287/opre.2020.2095
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2020.2095
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.2020.2095?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
    ---><---

    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:inm:oropre:v:70:y:2022:i:1:p:23-37. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.