IDEAS home Printed from https://ideas.repec.org/a/ids/ijbder/v3y2017i1p71-92.html
   My bibliography  Save this article

Long memory forecasting of yield spreads using a fractionally integrated ARMA model and its application in Islamic capital market

Author

Listed:
  • Issam Bousalam
  • Moustapha Hamzaoui

Abstract

In this paper, we used a modified rescaled range analysis (MRS) to investigate the presence of long memory in three series of absolute yield spreads (AYS) of the Dow Jones Sukuk Indexes from March 1, 2011 to March 1, 2016. The estimated Hurst exponents for the three series are significant and smaller than one providing strong evidence that long range dependence exists in Sukuk's AYS and these can become stationary with fractional differencing. Based on these results, we fitted three ARFIMA models to Sukuk's AYS and found that they have better explanatory power compared to the first-order ARIMA models. Furthermore, our 260 steps-ahead dynamic forecasting results show that the ARFIMA models are better for predicting future yield spreads. Such findings suggest to account for long memory in investing decisions and projecting future yields and spreads. Our results should be useful to Sukuk market participants whose success depends on the ability to forecast Sukuk's yield spreads movements, and anticipate the prospective default risk.

Suggested Citation

  • Issam Bousalam & Moustapha Hamzaoui, 2017. "Long memory forecasting of yield spreads using a fractionally integrated ARMA model and its application in Islamic capital market," International Journal of Bonds and Derivatives, Inderscience Enterprises Ltd, vol. 3(1), pages 71-92.
    • Handle: RePEc:ids:ijbder:v:3:y:2017:i:1:p:71-92
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=83952
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijbder:v:3:y:2017:i:1:p:71-92. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=405 .

    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.