Valuing clustering in catastrophe derivatives

Sebastian Jaimungal and Yuxiang Chong

Quantitative Finance, 2014, vol. 14, issue 2, 259-270

Abstract: The role that clustering in activity and/or severity plays in catastrophe modeling and derivative valuation is a key aspect that has been overlooked in the recent literature. Here, we propose two marked point processes to account for these features. The first approach assumes the points are driven by a stochastic hazard rate modulated by a Markov chain while marks are drawn from a regime-specific distribution. In the second approach, the points are driven by a self-exciting process while marks are drawn from an independent distribution. Within this context, we provide a unified approach to efficiently value catastrophe options--such as those embedded in catastrophe bonds--and show that our results are within the 95% confidence interval computed using Monte Carlo simulations. Our approach is based on deriving the valuation PIDE and utilizes transforms to provide semi-analytical closed-form solutions. This contrasts with most prior works where the valuation formulae require computing several infinite sums together with numerical integration.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2013.799775 (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:14:y:2014:i:2:p:259-270

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

DOI: 10.1080/14697688.2013.799775

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 ().