Description: This report studies the impact of unemployment and underemployment on insurance business. It introduces the concepts, theoretical background, and patterns of unemployment and underemployment. It also touches on economic policies that have been used to improve labor market conditions and maintain the market’s stability.

Description: A primer on extreme event modeling and mitigation for practitioners, it covers the complete cycle of extreme event risk management, from identifying experience data sources, to extreme event detection, to modeling and dependence analysis, and finally to monitoring and mitigation.

Description: The Society of Actuaries Aging & Retirement Strategic Research Program and Retirement Section are pleased to make available a report that introduces predictive analytics to pension actuaries.

Description: With the credit interest rate floor of a cash balance plan—a simple type of option identified by the Project Oversight Group—as a model, this research uses three valuation and risk analysis approaches to explore the existing techniques to value embedded options on an economic basis.

Description: Research material that developed a Liability-Driven Investment (LDI) benchmark framework.

Description: This article discusses how unemployment and underemployment information can help improve economic forecasts, insurance assumption setting and risk management. It summarizes the current modeling practices of unemployment which shows a deficiency of using unemployment/ underemployment information in actuarial works. It also gives three examples of using unemployment/underemployment information for economic assumption setting, new business growth prediction and stress scenarios related to the labor market.

Description: The asset portfolios of retirees’ serve many purposes including current income, liquidity, purchasing power, estate, tax minimization, and so on. Multiple objectives with different levels of importance lead to a complex asset allocation problem for retirees. Existing asset allocation methods normally focus on a subset of the multiple objectives of retirees in an approximate way. The simulation-based multiple objective asset allocation method objectively assesses each allocation plan against multiple objectives in a consistent way and provides a more holistic picture of possible outcomes. This information is critical for finding the optimal allocation plan. The optimization is based on the weighted performance relative to multiple objectives. At the same time, it requires more inputs and advanced modeling.

Description: Economic risk analysis has two dimensions: time and frequency. Asset return varies by time because of economic cycles and economic structural changes. Risk is higher during economic recessions and smaller during economic expansions. In addition, different economic structures may exist at different time scales. Risk measures calculated based on daily, weekly, monthly and yearly historical data can be very different. The appropriate frequency depends on the time horizon of the analysis. Therefore, it is important to measure economic risk at both the time level and the frequency level. However, time series analysis and statistical analysis that have been widely adopted in economic risk analysis focus on only one of the two dimensions. In this paper, wavelet models are used to enhance both time‐invariant analysis and time‐variant analysis. Wavelet models can systematically analyze risk by time and frequency and provide richer information than time series models. Using wavelet models, risk measurement can easily be adjusted based on time horizon in a consistent way. Equity VaR estimation is selected to demonstrate the application of wavelet analysis in risk management.

Description: The Society of Actuaries’ Committee on Finance Research is pleased to make available a research report that develops a framework for applying deep learning and reinforcement learning techniques to optimal dynamic strategic asset allocation for Liability Driven Investment (LDI).

Description: Wavelet models are used to analyze risk by time and frequency and provide richer information than time series models.