A Markov chain Monte Carlo approach to estimate the risks of extremely large insurance claims
Studies, Pareto optimum, Monte Carlo simulation, Insurance claims, Probability distribution
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.
International Journal of Business and Economics
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 http://repository.vtc.edu.hk/ive-it-sp/15