Description: This paper develops credibility using a longitudinal data framework. In a longitudinal data framework, one might encounter data from a cross-section of risk classes towns with a history of insurance claims available for each risk class. For the marginal claims distributions, we use generalized linear models, an extension of linear regression that also encompasses Weibull and Gamma regressions. Copulas are used to model the dependencies over time specifically, this paper is the first to propose using a t-copula in the context of generalized linear models. The t-copula is the copula associated with the multivariate t-distribution like univariate t-distributions, it seems especially suitable for empirical work. Moreover, we show that the t-copula gives rise to easily computable predictive distributions that we use to generate credibility predictors. Like Bayesian methods, our copula credibility prediction methods allow us to provide an entire distribution of predicted claims, not just a point prediction.

Description: This paper investigates the use of models of dependent mortality for determining annuity values. We discuss a broad class of parametric models using a bivariate survivorship function called a copula. Using data from a large insurance company to illustrate our methods, we calculate maximum likelihood estimates to calibrate the rnodel.

Description: An article in the Expanding Horizons newsletter, Issue 26, published in April 2003 that discusses the Annual Prize to the author or authors writing the best paper to appear in the North American Actuarial Journal, the leading scientific publication of the Society. It announces the winners of the award, Mary R. Hardy for A Regime–Switching Model of Long-Term Stock Returns,and Phelim P. Boyle, Adam W. Kolkiewicz and Ken Seng Tan, for Valuation of the Reset Options Embedded in Some Equity–Linked Insurance Products.