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

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Author Info

  • 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)

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    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.

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    Bibliographic Info

    Article provided by College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan in its journal International Journal of Business and Economics.

    Volume (Year): 6 (2007)
    Issue (Month): 3 (December)
    Pages: 225-236

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    Handle: RePEc:ijb:journl:v:6:y:2007:i:3:p:225-236

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    Related research

    Keywords: heavy-tail distributions; loss distribution model; Pareto probability distribution; Gibbs sampler;

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