A Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims
AbstractThe 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 InfoArticle 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)
heavy-tail distributions; loss distribution model; Pareto probability distribution; Gibbs sampler;
Find related papers by JEL classification:
- C0 - Mathematical and Quantitative Methods - - General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
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- Shaun, Wang, 1995. "Insurance pricing and increased limits ratemaking by proportional hazards transforms," Insurance: Mathematics and Economics, Elsevier, vol. 17(1), pages 43-54, August.
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