IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v28y2012i1p60-72.html
   My bibliography  Save this article

Asset allocation under threshold autoregressive models

Author

Listed:
  • Na Song
  • Tak Kuen Siu
  • Wa‐Ki Ching
  • Howell Tong
  • Hailiang Yang

Abstract

We discuss the asset allocation problem in the important class of parametric non‐linear time series models called the threshold autoregressive model in (J. Roy. Statist. Soc. Ser. A 1977; 140:34–35; Patten Recognition and Signal Processing. Sijthoff and Noordhoff: Netherlands, 1978; and J. Roy. Statist. Soc. Ser. B 1980; 42:245–292). We consider two specific forms, one self‐exciting (i.e. the SETAR model) and the other smooth (i.e. the STAR) model developed by Chan and Tong (J. Time Ser. Anal. 1986; 7:179–190). The problem of maximizing the expected utility of wealth over a planning horizon is considered using a discrete‐time dynamic programming approach. This optimization approach is flexible enough to deal with the optimal asset allocation problem under a general stochastic dynamical system, which includes the SETAR model and the STAR model as particular cases. Numerical studies are conducted to demonstrate the practical implementation of the proposed model. We also investigate the impacts of non‐linearity in the SETAR and STAR models on the optimal portfolio strategies. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • Na Song & Tak Kuen Siu & Wa‐Ki Ching & Howell Tong & Hailiang Yang, 2012. "Asset allocation under threshold autoregressive models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(1), pages 60-72, January.
    • Handle: RePEc:wly:apsmbi:v:28:y:2012:i:1:p:60-72
    DOI: 10.1002/asmb.897
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.897
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.897?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cathy W.S. Chen & Toshiaki Watanabe, 2019. "Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 747-765, May.

    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:wly:apsmbi:v:28:y:2012:i:1:p:60-72. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.