Description: Panelists discuss the methods HMOs employ to measure their providers' quality of care, outcomes, compliance with best practice guidelines, and cost control. From the Record of the Society of Actuaries, Vol. 25, No. 2.

Description: This is the abstract of the paper Quasi-Monte Carlo Methods in Numerical Finance. This paper introduces and illustrates a new version of the Monte Carl method that has attractive properties for the numerical valuation of derivatives. The traditional Monte Carlo method has proven to be a powerful and flexible tool for many types of derivatives calculations.

Description: A recent Society of Actuaries study calculates safety and stability for current Long Term Care Insurance premiums.

Description: An Overview of Stochastic Simulation modeling techniques and usages

Description: Abstract of Research Project on Interest Rate Risk

Description: Excerpts from a conversation with Alan Mills regarding his work in complexity science and agent-based modeling.

Description: In 2003, the American Academy of Actuaries Life Capital Adequacy Subcommittee LCAS published a set of 10,000 stochastic scenarios that was intended to be used to support the publication, “Recommended Approach for Setting Regulatory Risk-Based Capital Requirements for Variable Products with Guarantees Excluding Index Guarantees.” The primary purpose of this article is to illustrate statistical measures that can be used to evaluate stochastic simulations.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.