Description: Presented at June 2002 Spring Meeting. A noted expert with a degree from the graduate program in Studies of the Future at the University of Houston-Clear Lake discusses long-term forecasting and planning, the future of information technology and technology transfer, strategic planning and the development of future scenarios.

Description: Presented at May 2002 Spring Meeting. Presenters discuss a method of mortality projection on various scenarios about terrorism that have been written by futurists given the events of Sept. 11. Attendees learn to use a futurism technique scenarios to address actuarial problems.

Description: Read the April 2020 issue of The Modeling Platform, published by the Modeling Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This article discusses scenario generators to price or value insurance products.

Description: View the complete issue for the August 2014 Reinsurance News newsletter.

Description: The article discusses under which conditions it is appropriate to use the true probability distribution or the risk-neutral probability distribution for interest rate scenario generation. It addresses which should be used for valuation purposes and strategy development.

Description: This paper identifies several implicit assumptions underlying the use of the lognormal distribution for modeling the periodic change in the interest rate of a given maturity, tests those assumptions against historical interest rates, and presents additional information from those tests. A discussion of the paper follows. From Transactions of Society of Actuaries 1991, Vol. 43.

Description: The April 2016 Issue of The Modeling Platform

Description: This article is the first installment of a three-part series that aims to provide an overview of ESGs and will focus on the general motivation for stochastic modeling, its advantages and its limitations. Future articles will further break down the key factors of ESGs and the application of these factors to better understand the Academy Interest Rate Generator (AIRG).

Description: This 1997 presentation from the 32nd Actuarial Research Conference develops a stochastic generator for the generation of scenarios of the S&P 500 index, dividend yield, consumer price index, and U.S. Treasury yields. The authors first create a set of 'stylized facts' for these series. They estimate statistical models for these series. These in-sample statistical models are themselves not suitable for generation of scenarios for decision making, but instead are additional 'stylized facts' that assist in model development. The 'best' statistical model according to standard statistical model selection criteria can easily lead to a model that is highly unsuitable for generation of scenarios for decision making. The authors develop a stochastic generator that is suitable for decision making under uncertainty.