Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

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: This paper first derives the fair participation rate based on a fair value of the equity-indexed annuity EIA. Then using risk measures, a new participation rate is obtained, based on the tail loading of the hedging error distribution. Risk management strategies reducing risk related to the hedging error are also presented. A detailed numerical analysis is performed for a point-to-point EIA.

Description: Discussion of the movement toward principle-based reserves for LTC insurance.

Description: Discussion of Capital Market Modeling techniques

Description: This article explains why stochastic modeling is becoming more pervasive, and describes a specific stochastic application with metrics to measure results.

Description: The behavior of extreme paths in the usual stochastic interest rate models is not nearly so plausible as the behavior of their expected values and variances. This paper proposes a new class of models that deliver more plausible extreme paths while preserving the usual expected value and variance behavior. Along the way we derive the closed form solution for the traditional mean-reverting lognormal process, including the drift compensation.

Description: This paper analyzes the optimal retention level with a portfolio of insurance risks, and premiums invested in risk-free and risky assets. Optimality is defined here as maximizing expected utility of terminal payout.