IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v48y2019i14p3530-3548.html
   My bibliography  Save this article

Optimal investment-consumption-insurance strategy in a continuous-time self-exciting threshold model

Author

Listed:
  • Hao Wang
  • Rongming Wang
  • Jiaqin Wei
  • Shaosheng Xu

Abstract

In this paper, we consider an optimal investment-consumption-insurance purchase problem for a wage earner. We assume that the price of the risky asset is governed by a continuous-time, finite state self-exciting threshold model. In this model, the state space of the price of the risky asset is partitioned by a set of thresholds and the parameters depend on the region which the current value of the price falls in. The wage earner’s objective is to find the optimal investment-consumption-insurance strategy that maximizes the expected discounted utilities. The optimal strategy for power utility function is derived by the martingale approach and the dynamic programming approach. Numerical examples are also provided to illustrate the effect of the thresholds.

Suggested Citation

  • Hao Wang & Rongming Wang & Jiaqin Wei & Shaosheng Xu, 2019. "Optimal investment-consumption-insurance strategy in a continuous-time self-exciting threshold model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(14), pages 3530-3548, July.
  • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:14:p:3530-3548
    DOI: 10.1080/03610926.2018.1477161
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2018.1477161
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2018.1477161?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:48:y:2019:i:14:p:3530-3548. 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.