IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i7p2309-2337.html
   My bibliography  Save this article

Equilibrium behavioral strategy for a DC pension plan with piecewise linear state-dependent risk tolerance

Author

Listed:
  • Liyuan Wang
  • Zhiping Chen
  • Peng Yang

Abstract

We investigate the equilibrium behavioral strategy for a defined contribution (DC) pension plan during the accumulation phase in this paper. We adopt a state-dependent function to describe the risk tolerance attitude of a mean-variance (MV) investor, which is a piecewise linear function of the current wealth level with the reference point being the discounted investment target. Meanwhile, the stochastic labor income is taken into account, whose risk sources arise from both financial and non-financial markets. The extended Hamilton-Jacobi-Bellman (HJB) system of equations for our problem is presented. Explicit expressions for the suboptimal equilibrium behavioral strategy and its corresponding equilibrium value function are obtained in the suboptimal sense by the stochastic control technique. We find that the suboptimal equilibrium behavioral strategy takes a piecewise linear feedback form of the current wealth level with respect to the discounted investment target, and it also depends on the current labor income. Finally, some numerical analyses are provided to illustrate the effects of model parameters on the suboptimal equilibrium behavioral strategy and the variance-expectation curve, which shed light on our theoretical results.

Suggested Citation

  • Liyuan Wang & Zhiping Chen & Peng Yang, 2023. "Equilibrium behavioral strategy for a DC pension plan with piecewise linear state-dependent risk tolerance," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(7), pages 2309-2337, April.
  • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:7:p:2309-2337
    DOI: 10.1080/03610926.2021.1952265
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1952265
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2021.1952265?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:52:y:2023:i:7:p:2309-2337. 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.