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: 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: 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: 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 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.

Description: Summary of the SOA's research report Modern Deterministic Scenarios for Interest Rates, which developed interest rate scenarios for use in cash flow testing.

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: Read the November 2019 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: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.