Description: This session at the SOA 2003 Washington, DC Spring Meeting focuses on the issues involved in building a scenario generator. It covers the data set used to develop and calibrate the generator as well as a discussion of different approaches and their features. Implementation is addressed along with potential pitfalls.

Description: 1988 Valuation Actuary Symposium. The session discussed the C-3 risk, not the C-1 risk in the title. It explored the question: For testing purposes, how does one create the investment scenarios to which you expose your assets and liabilities?

Description: 2011 SOA IFRS Seminar, Hong Kong. This is an Excel spreadsheet associated with Session 40 on 'Economic Scenario Generators' of the 2011 SOA IFRS Seminar held in Hong Kong. The spreadsheet facilitated the learning objectives of this workshop about ESG.

Description: 1988 Valuation Actuary Symposium, In this session, the presenter suggests the value of the Reg 126 testing is a function of the attitude of the company toward the process. He also explored the differences in results by product of using deterministic versus stochastic scenarios.

Description: The primary focus of this 1997 presentation from the 32nd Actuarial Research Conference is the adaptation of the representative scenario method such that the distribution of the primary rates used for product pricing, are consistent, regardless of the initial random number seed. A preliminary question is how many stochastically generated scenarios are required for consistency. This presentation looks at the implications of: the use of antithetic random normal deviates upon the scenario process, ways of modifying the candidate list, and the choice function within the representative scenario process.

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.