Description: In this article the authors work out the expected discounted penalty function for Levy processes. Unlike the classical compound Poisson case that models the individual claim size distribution and obtains from it the aggregate distribution, here the aggregate claims distribution is known. From Actuarial Research Clearing House, 2006.

Description: In the paper, the authors implement the empirical version of a robust Kalman filter credibility estimator with a data set, and compare its sensitivity to large claims with that of other credibility estimators in the literature.

Description: In an effort to build a bridge between epidemiological and actuarial modeling, this paper analyzes possible financial arrangements made against expenses resulted from medical treatments given to insured patients. Based on classical compartment models, the first part of this paper designs insurance policies for susceptible participants facing the risk of infection and formulates the financial obligations of both parties using actuarial methodology. For practical purposes, the second part employs a variety of numerical methods for calculating premiums and reserves. The last part illustrates the methods by designing insurance products for the Great Plague in Eyam and the SARS Epidemic in Hong Kong.

Description: This research was funded by the Natural Sciences and Engineering Research Council of Canada [NSERC] Discovery Grant 36860–06. Loss reserving is one of the most challenging tasks facing actuaries. Numerous approaches have been developed to give reasonable estimates. Generalized linear models [GLMs] are becoming quickly popular statistical analysis methods to estimate loss reserves. However, most of these models are aggregate reserving methods based on loss development triangles, without using information with regard to the actual claims processes. This paper establishes a more sophisticated structural reserving method incorporating more detailed information, such as the premium exposure emergence pattern, the loss emergence pattern and the loss development pattern, within the framework of GLMs.

Description: This abstract describes a paper that investigates multivariate risk processes which may be useful in studying ruin problems for insurance companies handling dependent classes of business.

Description: This paper derives limited fluctuations credibility results for the Generalized Linear Model GLM and the extended case of generalized linear mixed model GLMM. It shows how credibility depends on the sample size, the distribution of covariates, and the link function. This provides a mechanism to obtain confidence intervals for the GLM and GLMM estimators.

Description: The main objective of this study is to analyze and control a surplus dependent risk process, where larger surpluses allow for a wider underwriting policy. We focus here mostly on the diffusion approximation of the risk process. The results are derived in two papers Brownian motion in Risk Theory, submitted for publication, and Surplus dependent risk processes defined by stochastic differential equations, in preparation. We only include here a long summary of these papers.

Description: In this paper some general properties of compound nonstationary Poisson processes are discussed as well as the possible shapes of the intensity function. A decomposition of the accumulated claims as a sum of independent compound Poisson sums is shown. An embedded discrete risk model is introduced. It is shown that ruin probabilities and maximal losses for the nonstationary risk model find lower bounds with the embedded discrete model. From Actuarial Research Clearing House 1994 VOL. 1.

Description: The minimum distance estimator proposed allows for the selection of the best fitting family member without prior distributional assumptions. It is shown that this estimator is consistent and can be made either robust or asymptotically efficient. Its asymptotic distribution is derived and and illustrated with automobile insurance data. From the Actuarial Research Clearing House 1990 Vol. 1