Description: The article discusses that drawbacks of the HJM Heath-Jarrow-Morton class of stochastic interest models and suggests the use of the Longstaff-Schwartz string model. It describes the use and benefits of the Lonstaff-Schwartz string model.

Description: Feature article discussing how insurers have reflected volatility in asset return assumptions when determining capital requirements, but have largely disregarded the impact of mortality volatility on their liability assumptions when performing stochastic analysis.

Description: Explains considerations for stochastic modeling of credit spreads, inflation and exchange rate risk.

Description: As stochastic modeling has evolved, it has divided down two paths—real-world and risk-neutral. This article discusses this division and shows how these paths may be re-united through the use of deflators.

Description: Are Your Scenarios on Target?From Risks and Rewards Newsletter, August 2005, Issue No. 47. Actuaries are faced with the dilemma of how to incorporate advances in insurance products and modeling technology into their work. Increasingly, this means looking at a large number of potential outcomes using stochastic simulations.

Description: A discussion of methods used to determine the liability of segregated fund or separate account investment guarantees.

Description: The Risk Management Task Force polled the Risks & Rewards readers and asked the following questions related to Monte Carlo models: 1 How many scenarios do you run? 2 How did you determine the number? and 3 What confidence interval does your result have? The results are summarized in this article.

Description: Feature article discussing the use of stochastic volatility models in the pricing of investments and options.

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: Provides a metric based on the concept of "entropy" from information theory/signal processing, for modifying a stochastic scenario set to meet pre-defined targets on average across the set. Explains the concept and develops it in simple examples. Illustrates why an optimal adjustment to the scenario set is one that maximizes entropy. Provides guidelines to limit use of the technique appropriately and avoid invalid application of the tool.