Description: Because of the growing significance of Enterprise Risk Management, it is critical to consider how ERM responds and relates to circumstances and conditions. In particular, one megatrend currently impacting our world and our understanding of the economy is the emergence of a behavioral economics perspective. This paper describes the nature of behavioral economics, and considers the potential impact of such a perspective on ERM. From Actuarial Research Clearing House, 2012, Vol. 1.

Description: Summary of April 2020 Investment Section Town Hall and come additional commentary.

Description: This is the key findings research report for the sixteenth Emerging Risk Survey.

Description: This paper provides a summary of the Dec. 2012 summit of the Natural Resource Sustainability Group and invited experts.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This paper explores cognitive and behavioral theories that can be applied to explain why emerging risk is often overlooked, downplayed or ignored. It also proposes an innovative framework for managing emerging risk within an overall enterprise risk management program.

Description: The interactive visuals provided a compelling demonstration for Allstate's leadership team to see the effects of changes to Allstate's risk profile.

Description: The CIA, SOA and Joint Risk Management Section of the CAS, CIA, and the SOA sponsored a whitepaper on the application of ERM at a federal government level.

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: This report looks at identifying drivers of systemic risk in China’s insurance industry and develops a measurement method for systemic risk as well as systemic risk dependence between the insurance industry and other industries.