Analysis of a Generalized Penalty Function in a Semi-Markovian Risk Model
Eric Cheung and
David Landriault
North American Actuarial Journal, 2009, vol. 13, issue 4, 497-513
Abstract:
In this paper an extension of the semi-Markovian risk model studied by Albrecher and Boxma (2005) is considered by allowing for general interclaim times. In such a model, we follow the ideas of Cheung et al. (2010b) and consider a generalization of the Gerber-Shiu function by incorporating two more random variables in the traditional penalty function, namely, the minimum surplus level before ruin and the surplus level immediately after the second last claim prior to ruin. It is shown that the generalized Gerber-Shiu function satisfies a matrix defective renewal equation. Detailed examples are also considered when either the interclaim times or the claim sizes are exponentially distributed. Finally, we also consider the case where the claim arrival process follows a Markovian arrival process. Probabilistic arguments are used to derive the discounted joint distribution of four random variables of interest in this risk model by capitalizing on an existing connection with a particular fluid flow process.
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
http://hdl.handle.net/10.1080/10920277.2009.10597571 (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:uaajxx:v:13:y:2009:i:4:p:497-513
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uaaj20
DOI: 10.1080/10920277.2009.10597571
Access Statistics for this article
North American Actuarial Journal is currently edited by Kathryn Baker
More articles in North American Actuarial Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().