Description: discussion of the ongoing developments in reserving and risk-based capital RBC requirements for variable annuities including stochastic scenario projections

Description: Presentation from the 41st Actuarial Research Conference held on August 10-12, 2006 in Montreal, Canada. Fuzzy set theory is incorporated into times series analysis, giving fuzzy parameter estimates of models such as ARMA and GARCH. Examples are given, including returns on a stock and option valuation.

Description: More Efficient Monte Carlo Simulations for Mortality Assumption Testing

Description: Discussion of the appropriate use of geometric versus arithmetic average returns to use in a stochastic interest rate generator to be equivalent to a deterministic single interest rate scenario.

Description: Transcript of the Q&A period from Session 6A: Projection and Statistical Modeling of Mortality at Late Age

Description: We consider Libor market model and calibration process. We estimate some interest rate derivatives such as Caps and Swaptions. We use Monte Carlo methods with appropriate dynamics under spot measure and forward measure. Imposing martingale discretization property, the same quantities are estimated again to see a contrast or discrepancy in light of confidence interval. Based on algorithms discussed in the paper, “Arbitrage-free discretizations of lognormal forward Libor and swap rate models” by Glasserman, P and Zhao, X 2000, the algorithms are implemented in excel VBA. Starting with current market data such as FRAs and Bond Prices, we describe steps involved to estimate the price of interest rate derivatives such as Caps and Swaptions, which would be beneficial to the practitioners in the industry.

Description: The April 2016 Issue of The Modeling Platform

Description: This article describes the key elements of a review of an economic scenario generator (ESG), including scoping, parameters, models, and model efficiency.

Description: Discussion of how to obtain tight upper and lower bounds on E[hX] for a given function h, and a random variable X with three known moments.