Description: This article list descriptions of the sessions that are either sponsored or co–sponsored by the Education and Research Section.

Description: Abstract for the 2008 ERM Monograph paper, “Estimating Mortality Risk Using Predictive Modeling”

Description: As a life reinsurer, understanding mortality risk is mission critical. Superior models of mortality risk lead to more competitive pricing in profitable market segments and more efficient use of capital. Surprisingly, modern statistical tools such as predictive modeling have not previously been used to model mortality data to gain this edge. In this paper, we present the use of a predictive modeling technique called projection pursuit regression PPR to model mortality data. We first describe the process of creating a PPR model using inter-company mortality data collected by the Individual Life Experience Committee of the Society of Actuaries, and present the resulting model. We then discuss the process of using this model as a starting point for creating a more detailed PPR model of internal company mortality data.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This presentation discusses how real world Grey Swan scenarios are excellent choices for stress and resiliency testing. In addition, an example is provided of a set of Grey Swan scenarios which correspond to the RBC C-3, Phase II.

Description: This paper demonstrates the use of Quantile Regression in the development and understanding of conditional value at risk (VaR) models for enterprise risk management.

Description: This article presents an introduction and overview of the R language. From Expanding Horizons, April 2007.

Description: By the use of resampling and extreme value statistics we will develop a method to reduce the time and costs of testing insurance company insolvency. Most ruin models require assumptions about the surplus distribution and/or assumptions about the claims count and severity distributions. The actuary may not be comfortable in making these ruin assumptions. Instead, he or she may generate such distributions by running extensive computer simulations of corporate models, I introduce a method to reduce the number of simulations necessary' to estimate the 1 and 0.1 percentiles of the surplus distribution. This approach 'cuts off' the tail at approximately the tenth percentile of the sample distribution and generates new tails for the distribution by resampling. Averages of the new order statistics approximate the I and 0.1 percentiles.

Description: This paper defines Distributable Earnings as the generalized pricing and valuation framework for financial contracts containing the requirement to hold capital or reserves in support of the obligations. It examines the Market Value paradigm and its foundation arising from the pricing of fixed income securities. It reconciles these two methods, showing that market value methods ignore mandatory capital and reserve requirements, which carry both risk and cost. The paper makes the case for the Distributable Earnings framework, not Market Value, as the true basis for economic value for these financial contracts. Finally, it concludes that this framework must be employed in risk-management processes, else fail to recognize a significant source of risk.

Description: Description of the scheduled sessions that are either sponsored or co-sponsored by the E&R Section.