A Bayesian analysis of clusters of extreme losses

Beatriz Vaz de Melo Mendes

Applied Stochastic Models in Business and Industry, 2006, vol. 22, issue 2, 155-167

Abstract: Occasional short range dependence in a claim generating process, probably induced by some subordinated process, may result in clusters of extreme losses. Under such circumstances, the iid assumption for the exceedances may not hold anymore. For a given value of the retention level, we overcome this difficulty by applying an empirical rule for cluster definition and aggregating the excesses within clusters. This modelling strategy is compared to the classical random sum of excess losses model based on the iid assumption. The usual discrete probability models and an extreme value distribution from the Pareto family are assumed, respectively, for the counting processes and the severities. Bayesian techniques are then used to obtain a predictive distribution of the annual excess claim amount. Maximum likelihood estimates are also computed and compared. We illustrate using a fire insurance claims data. The Bayesian approach provided more conservative point and interval estimates for the statistical premium. Copyright © 2006 John Wiley & Sons, Ltd.

Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1002/asmb.625

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:wly:apsmbi:v:22:y:2006:i:2:p:155-167

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().