Description: This research paper is intended to be 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. This paper explores extreme events from the perspective of a global insurance company, with an emphasis on market risk, credit risk, insurance risk, liquidity risk, and business risk. Extreme risk concepts and models are introduced from a practitioner’s perspective without unnecessary theoretical details. This report is not intended to be all-encompassing, but rather to focus on the key aspects of extreme risk analysis and its application. The input data and R programs underlying the numerical examples herein are available in the supporting files accompanying this report

Description: This is the R programs used to develop the examples for the research report “Extreme Events for Insurers: Correlation, Models, and Mitigation by Kailan Shang and Marc Vincelli

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

Description: This Effective Enterprise Risk Management (ERM) Stakeholder Engagement report examines practices for gaining acceptance of a proposed ERM strategy. The researcher has identified the factors, processes and practices that lead to both poor and strong levels of stakeholder acceptance.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: Article describing the most recent Risk Management Section research updates

Description: A discussion about how many models can address the over fitting issues caused by too many variables by regularization, variable selection is still meaningful.

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: 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: 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.