Many insurance loss data are known to be heavy-tailed. In this article we study the class of Log phase-type (LogPH) distributions as a parametric alternative in fitting heavy tailed data. Transformed from the popular phase-type distribution class, the LogPH introduced by Ramaswami exhibits several advantages over other parametric alternatives. We analytically derive its tail related quantities including the conditional tail moments and the mean excess function, and also discuss its tail thickness in the context of extreme value theory. Because of its denseness proved herein, we argue that the LogPH can offer a rich class of heavy-tailed loss distributions without separate modeling for the tail side, which is the case for the generalized Pareto distribution (GPD). As a numerical example we use the well-known Danish fire data to calibrate the LogPH model and compare the result with that of the GPD. We also present fitting results for a set of insurance guarantee loss data.
Bibliographical noteFunding Information:
The authors thank two anonymous referees for their constructive comments. Joseph Kim is grateful for the support of the Natural Sciences and Engineering Research Council of Canada . Soohan Ahn was supported by the 2011 sabbatical year research grant of the University of Seoul . Soohan Ahn also greatly appreciate the hospitality of the Department of Statistics, University of Toronto, during his sabbatical year.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Economics and Econometrics
- Statistics, Probability and Uncertainty