A Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims

Wan-Kai Pang, Shui-Hung Hou, Marvin D. Troutt, Wing-Tong Yu and Ken W. K. Li
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Wan-Kai Pang: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
Shui-Hung Hou: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
Marvin D. Troutt: Department of Management and Information Systems, Kent State University, U.S.A.
Wing-Tong Yu: School of Accounting and Finance, The Hong Kong Polytechnic University, Hong Kong
Ken W. K. Li: Department of Information and Communications Technology, The Hong Kong Institute of Vocational Education, Hong Kong

International Journal of Business and Economics, 2007, vol. 6, issue 3, 225-236

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.

Keywords: heavy-tail distributions; loss distribution model; Pareto probability distribution; Gibbs sampler (search for similar items in EconPapers)
JEL-codes: C0 C1 G22 (search for similar items in EconPapers)
Date: 2007
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