Implied Severity Density Estimation: An Extended Semiparametric Method to Compute Credit Value at Risk

J. Baixauli () and Susana Alvarez

Computational Economics, 2012, vol. 40, issue 2, 115-129

Abstract: This paper focuses on estimating implied severity, which does not rely on historical data and can be used especially for low default companies. We perform an extended semiparametric estimation method based on a mixture start to estimate it. We carry out an empirical analysis and our results show that our method allows us to capture the observed multimodal behaviour of severity better than the commonly used single beta distribution assumption. Futhermore, we highlight the relevance of this modeling approach by focusing on its role for credit VaR. Copyright Springer Science+Business Media, LLC. 2012

Keywords: Credit default swaps; Implied severity; Semiparametric estimation (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-011-9290-y (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:kap:compec:v:40:y:2012:i:2:p:115-129

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-011-9290-y

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().