IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v64y2024i3d10.1007_s10614-023-10481-5.html
   My bibliography  Save this article

Stochastic Default Risk Estimation Evidence from the South African Financial Market

Author

Listed:
  • Mesias Alfeus

    (Stellenbosch University
    National Institute for Theoretical and Computational Sciences (NITheCS))

  • Kirsty Fitzhenry

    (Stellenbosch University)

  • Alessia Lederer

    (Stellenbosch University)

Abstract

This paper provides empirical studies of the estimation of defaultable bonds in the South African financial markets. The key objective is to estimate the unobservable factors affecting bond yields for South African major banks. The maximum likelihood approach is adopted for the estimation methodology. Multi-dimensional Cox–Ingersoll–Ross (CIR)-type factor models are considered and compared. Extended Kalman filtering techniques are employed in order to tackle the situation that the factors cannot be observed directly. We find empirical evidence that default risk varies with the business cycle, increased sharply in the South African financial market during COVID-19 and the $$\alpha$$ α -CIR model performs better than the classical CIR model.

Suggested Citation

  • Mesias Alfeus & Kirsty Fitzhenry & Alessia Lederer, 2024. "Stochastic Default Risk Estimation Evidence from the South African Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1715-1756, September.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:3:d:10.1007_s10614-023-10481-5
    DOI: 10.1007/s10614-023-10481-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10481-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10481-5?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.

    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:kap:compec:v:64:y:2024:i:3:d:10.1007_s10614-023-10481-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.