Description: In December 2007, Goldman Sachs launched a product called QxX index swap, which is designed to allow market participants to hedge or gain exposure to longevity and mortality risks. This presentation offers a quantitative analysis of this brand new financial innovation. First of all, the presentation sets up a risk-neutral framework to price QxX index swaps. This framework, which is based on the dynamics of death rates under a two-factor stochastic mortality model in a risk-adjusted probability measure, yields prices [spreads] that are fairly close to the spreads that Goldman Sachs currently offers. The presentation then explores the uncertainty involved in this model-based pricing framework. Specifically, it studies parameter risk by using Markov Chain Monte Carlo [MCMC] and model risk by examining structural changes in mortality dynamics. The results indicate that both model risk and parameter risk are significant. Actuaries should therefore be aware of these issues when placing a value on a longevity index swap.

Description: A discussion of the possible effort required and impacts resulting from the implementation of C3 Phase II for establishing risk-based capital for variable annuities. This article will describe practical considerations gleaned from actual valuation calculations recently performed by a consulting firm.

Description: This is the User's Guide that accompanies the Inflation Generator Excel spreadsheet which is associated with the research report entitled 'The Effect of Deflation or High Inflation on the Insurance Industry.'

Description: The Financial Reporting and Product Development Sections, along with the Committee on Life Insurance Research are pleased to make the following report available. Prepared by Bruce Rosner of Ernst & Young, the report summarizes the findings of a stochastic modeling efficiency study. With the help of twelve life insurance and annuity companies, the study tested six techniques for reducing the number of model points or scenarios required to achieve a given level of precision in stochastic actuarial modeling.

Description: This session from the 1995 SOA Boston Meeting discusses constructing and using a comprehensive, credible risk management system with a concentration on C-3 risk. It also covers an example of option adjusted spread analysis for a single premium deferred annuity SPDA. From the Record of the Society of Actuaries, Vol. 21, No. 4b.

Description: Panelists at this session of the SOA 1998 Maui II Spring Meeting present methods for scenario generation and establishing stochasitc models for pension plans.

Description: These are Charts 5-8 from a presentation entitled 'Stochastic Immunization'. From a session at a meeting of the Society of Actuaries held in San Diego, CA, June 22-23, 2000

Description: In the present paper, we propose and investigate a numerical method of computing the probability distribution of S. Inversion of the Laplace transform moment generating function is shown to be a better approach than the existing recursive method for find the distribution of sum of several independent random variables. In the continuous case, in many situations, numerical inversion needs to be used to compute the probability distribution function of the sum.

Description: This paper addresses the stochastic modeling for managing asset liability process. We start with developing a jump-diffusion process for evaluating of the liabilities of the insurance company in general. We then formulate the ALM process into a stochastic control problem. With this approach, we present a Bel|man-Dreyfus Fundamental type formula for ALM process in terms of the solution of a system of algebraic equations and partial differential equations.

Description: One of the most important problems in Finance is the valuation of financial securities written on underlying assets whose prices are subject to uncertainty. This paper presents a technique to estimate the unknown stochastic volatilities by solving the inverse problem associated with the parabolic partial differential equation governing risk neutral derivative security prices. This technique can be applied in a very general multi-factor setting and numerical examples are provided.