Description: Discusses the RAND function in Excel and alternatives for generating random numbers.

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: The purpose of this paper is to understand characteristics of a household that drive life insurance demand with more sophisticated analytical techniques. The analysis is based on the Survey of Consumer Finances data, a probability sample of the U.S. population. Household demand of two types of insurance, term life insurance and whole life insurance, is examined jointly. We model both the frequency and the severity of demand for insurance building on the work of Lin and Grace 2007 by using covariates that they developed.

Description: Constant maturity credit default swaps [CMCDS] are useful as hedging tools. In intensity model approach, the default time is defined as the first arrival time of the Poisson process. From the market quotes of CDS forward rates and bonds, we are able to numerically compute the default probabilities. Approximating CMCDS price depends largely on CDS forward rates’ volatilities and their correlations. This paper implements the price algorithm based on Brigo’s work [2006]. Starting with current market data such as CDS forward rates and nondefaultable bond prices, the paper describes steps involved to obtain the price CMCDS. The paper demonstrates the impact of convexity on the CMCDS price structure.

Description: We derive formulas for calculating the premiums that should be charged on policies in a discounted cash flow model with tax reserves and required assets that are determined by regulation. We also determine the unique division of required assets into “reserves” and “capital” that allows the product profitability to be correctly evaluated. That is, the profit after capital charges is zero if the product achieves the return assumed in pricing. We illustrate the concepts using whole life insurance and guaranteed minimum death benefit examples.

Description: Authored by a MIB Solutions team of Tom Rhodes, Stuart Klugman, Marianne Purushotham and Stacy Gill, the report summarizes the results of a study on the application and adoption of credibility theory within the life insurance and annuity industry. In addition to the report, numerical examples related to the application of credibility to company mortality and lapse data are provided in the four Excel files. This excel spreadsheet is a numerical example relating to the application of Credibility for lapses by policy for limited fluctuation. Please see the SOA website research section for the full report on Credibility Theory Practices.

Description: Complex models can provide a false sense of security, hiding the evidence that the entire range of indications may hinge on one or two key assumptions. Use data-driven assumptions, making sure the time series includes stressed environments when possible. If a model of underwriting risk indicates that the probability of accident year combined ratios experienced from 1998 to 2000 is remote, it is not a realistic model

Description: We will discuss how climate change risk represents radical uncertainty and the challenges this presents in integrating climate risk analysis and traditional investment risk analysis. The session will then explain and illustrate why deterministic, narrative-based, scenarios form a suitable basis for analyzing the potential impact of climate change on investment portfolios and strategies. At the conclusion of this session, attendees will understand deterministic and stochastic approaches to integrating climate risk with economic scenarios and how these can be applied in investment management. Attendees will also understand the challenges in creating a realistic integration of climate and economic scenarios.

Description: Session 4B: Impact of GLP-1 Drugs on Health Care Cost Trend and Clinical Outcomes