Description: This paper presents an approach to analyzing continuing care retirement community CCRC data and demonstrates the methodology by using data from a CCRC. From The Old-Age Crisis - Actuarial Opportunities - The 1996 Bowles Symposium, M-RS99-1.

Description: This paper presents an analysis of mortality in a sample of 441adults aged 55 to 85 at baseline, who were observed for eight years. The study was initiated for the purpose of analyzing age-related changes in a variety of physical variables and their impact on functional independence. The modeling approach is discussed and the factors which were most significantly related to mortality.

Description: Preview the next ARC, taking place August 8–11 at Western University.

Description: This paper examines the following three questions by reviewing literature that identifies and evaluates the tradeoffs among annuitization and self-managed drawdown strategies: 1. How DO Retirees Draw Down their Financial Savings? 2. How COULD Retirees Draw Down their Financial Savings? 3. How SHOULD Retirees Draw Down their Financial Savings?

Description: This paper presents a statistical analysis of advanced age mortality data, using extreme value models to quantify the upper tail of the distribution of human life spans. From 2005 Living to 100 and Beyond Monograph, M-LI05-1.

Description: This paper presents a model for analyzing the impact of underwriting cycles on an insurer’s surplus. The model allows the insurer to vary its security loading in response to the cycles, with a strategy parameter that indicates the extent to which the insurer follows the loading which prevails in the market. The insurer’s claim rate is also allowed to vary to reflect exposure changes that result from the insurer’s strategy. The paper analyzes ruin probabilities using both simulation and a Lundberg-type upper bound which is developed in the paper. It finds that the latter is suitable and convenient for comparing ruin probabilities under the different insurer strategies.

Description: Many quantities arising in non-life insurance depend on claim severity distributions, which are usually modeled assuming a parametric form. Obtaining good estimates of the quantities, therefore, reduces to having good estimates of the model parameters. However, the notion of 'good estimate' depends on the problem at hand. For example, the maximum likelihood estimators [MLE] are efficient, but they generally lack robustness. Since outliers are common in insurance loss data, it is therefore important to have a method that allows one to balance between efficiency and robustness. This presentation suggests a general estimation method that we call the method of trimmed moments [MTM]. This method is appropriate for various model fitting situations including those for which a close fit in one or both tails of the distribution is not required. The MTM estimators can achieve various degrees of robustness, and they also allow the decision maker to easily see the actions of the estimators on the data, which makes them particularly appealing.

Description: This is the abstract for the presentation on modeling joint lifetimes.