Description: This session from the 1995 SOA Boston Meeting covers a general overview of basic interest rate models, the meaning of an arbitrage free model, the considerations in choosing an appropriate interest rate scenario generator, the uses, alternative approaches, the double mean reverting process model - DMRP, and how good the results are in terms of historical rates. From the Record of the Society of Actuaries, Vol. 21, No. 4b.

Description: In this article the author cautions against the temptation to extend stochastic modeling into areas of unpredictable parameters and probabilities. He challenges actuaries to rely on their own judgment.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. In this Teaching Session the instructor discussed the importance of making sure the correct inferences were drawn from stochastic testing exercises. Topics included: 1. Applying the assumption of a normal distribution. 2. Drawing inferences about, and creating confidence intervals around, a population mean. 3. Drawing inferences about, and creating confidence intervals around, tail percentiles. 4. Certifying anticipated mortality under XXX in a mathematically concise way.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. This panel discussion provided an overview of the current risk-based capital RBC requirements for life insurers as well as insight into topical issues and potential changes to RBC. Discussion topics included: 1. Recap of RBC law and RBC calculation rules. 2. Modeling potential impact of alternative actions. 3. The tax impact of the factors. 4. Stochastic capital requirements for variable products. 5. The role of reinsurance and offshore solutions.

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.

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: 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: 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 Individual Life Experience Committee is holding a competition encouraging actuaries and other interested parties to predict 2017 industry mortality based on a data set containg 2009 - 2016 data. The competition is not only about predicting mortality, but also about explaining and presenting the results.