IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i23p5530-5553.html
   My bibliography  Save this article

Minimum probability function of crossing the upper regulatory threshold for asset-liability management

Author

Listed:
  • De-Lei Sheng
  • Danping Li
  • Peilong Shen

Abstract

In this paper, a stochastic model of asset-liability multiple is considered. To avoid the unbearable investment risk of asset price collapse, an upper regulatory threshold constraint is imposed on the asset-liability multiple. A Hamilton-Jacobi-Bellman (HJB) equation is established using the stochastic optimal control technique. The explicit minimum probability function and the optimal investment strategy are obtained, meanwhile, a verification theorem is also proved. Numerical examples illustrate the effectiveness of our results, which indicates that the current level and the upper regulatory threshold have significant influences on the minimum probability function.

Suggested Citation

  • De-Lei Sheng & Danping Li & Peilong Shen, 2021. "Minimum probability function of crossing the upper regulatory threshold for asset-liability management," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(23), pages 5530-5553, December.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:23:p:5530-5553
    DOI: 10.1080/03610926.2020.1734824
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1734824
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1734824?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:50:y:2021:i:23:p:5530-5553. 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.