Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5077550 | Insurance: Mathematics and Economics | 2007 | 11 Pages |
Abstract
This paper presents Bayesian graduation models of mortality rates, using Markov chain Monte Carlo (MCMC) techniques. Graduated annual death probabilities are estimated through the predictive distribution of the number of deaths, which is assumed to follow a Poisson process, considering that all individuals in the same age class die independently and with the same probability. The resulting mortality tables are formulated through dynamic Bayesian models. Calculation of adequate reserve levels is exemplified, via MCMC, making use of the value at risk concept, demonstrating the importance of using “true” observed mortality figures for the population exposed to risk in determining the survival coverage rate.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
César da Rocha Neves, Helio S. Migon,