Description: This paper identifies some of the special ERM considerations that a corporation which has employees who are members of a multi-employer pension plan, MEPP, should include in its ERM program. The focus of the paper is on the MEPP as an entity and tools to be used in its ERM. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

Description: This paper presents an advanced approach to enterprise risk management that, according to the author, significantly improves upon current approaches. The two fundamental elements are a focus on the management of strategic plans and a heavy reliance upon probability theory.

Description: This is Table 2 from a March 2011 presentation at the 2011 Enterprise Risk Management Symposium entitled Capital Allocation in the Property-Liability Insurance Industry.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This is the abstract of the entitled paper.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. In this paper, a multivariate quasi-negative binomial distribution is proposed to model frequency dependence among different risk types. The operational risk diversification effect is illustrated through frequency dependency modeled by the bivariate quasi-negative binomial distribution under a framework of Monte Carlo simulation.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. This is the abstract of the referenced paper.

Description: The article provides an introduction to the Stock Value Approach to enterprise risk management (ERM). A clear path toward implementation of comprehensive and pragmatic ERM is shown. The framework is underpinned by a company's goals as they relate to growth of the value of the company stock price.

Description: This report presents the major findings from the eleventh annual Emerging Risks survey.

Description: This is the full research report for the thirteenth Emerging Risk Survey which summarizes the results of the survey.

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.