A Markov chain Monte Carlo approach to estimate the risks of extremely large insurance claims

Authors

Wan Kai Pang, Hong Kong Polytechnic University
Shui Hung Hou, Hong Kong Polytechnic University
Marvin D. Troutt
Wing Tong Yu, Hong Kong Polytechnic University
Wing Kin Ken Li, Hong Kong Institute of Vocational Education (Tsing Yi), Vocational Training CouncilFollow

Document Type

Journal Article

Publication Date

2007

Keywords

Studies, Pareto optimum, Monte Carlo simulation, Insurance claims, Probability distribution

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.

Source Publication

International Journal of Business and Economics

Volume Number

6

Issue Number

3

ISSN

1607-0704

First Page

225

Last Page

236

Recommended Citation

Pang, W.,Hou, S.,Troutt, M.,Yu, W.,& Li, W. (2007). A Markov chain Monte Carlo approach to estimate the risks of extremely large insurance claims. International Journal of Business and Economics, 6 (3), 225-236. Retrieved from https://repository.vtc.edu.hk/ive-it-sp/15