Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62915
Title: A Markov chain Monte Carlo approach to estimate the risks of extremely large insurance claims
Authors: Pang, WK
Hou, SH
Troutt, MD
Yu, WT
Li, WK
Issue Date: 2007
Publisher: College of Business, Feng Chia University
Source: International journal of business and economics, 2007, v. 6, no. 3, p. 225-236 How to cite?
Journal: International journal of business and economics 
Abstract: The Pareto distribution is a heavy-tailed distribution often used in actuarial models. It is important for modeling losses in insurance claims, especially when we used it to calculate the probability of an extreme event. Traditionally, maximum likelihood is used for parameter estimation, and we use the estimated parameters to calculate the tail probability Pr(X > c) where c is a large value. In this paper, we propose a Bayesian method to calculate the probability of this event. Markov Chain Monte Carlo techniques are employed to calculate the Pareto parameters.
URI: http://hdl.handle.net/10397/62915
ISSN: 1607-0704
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