Predicting credit default swap prices with financial and pure data-driven approaches

Yalin Gündüz and Marliese Uhrig-Homburg

Quantitative Finance, 2011, vol. 11, issue 12, 1709-1727

Abstract: The increasing popularity of credit default swaps (CDSs) necessitates understanding their various features. In this study, we analyse the capability of CDSs in predicting CDS prices of other companies in the same risk class or CDS prices of further time horizons. In doing so, we employ the basic forms of structural (the Merton model) and reduced-form (constant intensity) models in a cross-sectional and a time series setup. By utilizing a credit default swap dataset exclusively for estimation and out-of-sample prediction, our study also serves as a comparison between the basic forms of credit risk models. Finally, it contrasts the results with the performance of a new supervised learning forecasting technique, the Support Vector Machines Regression. We show that although the Merton and the constant intensity models handle default timing and interest rates differently, the prediction performance in cross-sectional and time series analyses is, on average, similar. In one-, five-, and 10-step-ahead predictions of time series, the machine learning algorithm significantly outperforms financial models.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2010.531041 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:11:y:2011:i:12:p:1709-1727

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2010.531041

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().