Description: This paper demonstrates that extreme value theory (EVT) can be used as a tool to model policyholder behavior in the extreme tail. This paper applies EVT to the study of variable annuity dynamic lapse behavior in the extreme tail. It illustrates a process whereby a company can fully model policyholder behavior, including the extreme tail, with existing data. No judgment about the extremal behavior is necessary.

Description: This paper presents the output of in-house research on an emerging area of risk appetite management and our global implementation best practices. It intends to provide readers with detail implementation of risk appetite framework and assessment across an organization. It provides the conceptual risk appetite framework based model- developed by the author, implementation plan, and global best practices of a risk appetite assessment across an organization. It identifies the challenges and exceptions in the risk appetite assessment with an approach to manage it effectively, and helps organizations with cost-benefit analysis. It also includes an illustrative case study.

Description: Abstract: The U.K. Pension Protection Fund (PPF) was established in April 2005 to protect the pensions of members of U.K. private sector defined benefit pension schemes that have insufficient assets and whose corporate sponsor fails. The Fund takes over the pension scheme assets and assumes responsibility for the payment of compensation to the former members of the scheme. The PPF is funded by a levy on the population of eligible schemes. To improve its ERM framework, the PPF has undertaken a reverse stress testing exercise. This analysis provided the organization with new insights into its risk profile, the scenarios leading to the PPF’s potential “failure,” and interactions between these scenarios. This paper discusses the principles and objectives of reverse stress testing in the special case of the PPF and then illustrates the application of complex system science techniques for the purpose of reverse stress testing.

Description: This paper examines the potential impact of increasing negative commentary by social media users in multiple forums, which can erode the “reputation capital” of a utility. It also offers an approach for successfully addressing that risk and integrating social media risk management practices into existing utility enterprise risk management (ERM) programs.

Description: Project portfolio risk management and risk analysis form one of the critical components of enterprise risk management. Organizations measure and analyze risks associated with projects, project portfolios, and programs. The important step of the process is quantitative risk analysis of project schedules using event chain methodology (ECM). ECM is a stochastic modeling technique for schedule risk analysis. All risks, including schedule and nonschedule-related risks, are assigned to a particular project and within this project to the particular activity or resource. Further, ECM allows one to model the relationship between project risks by defining risks that cause or trigger other risks. All risks and relationships between them will be presented on the project or portfolio Gantt charts using event chain diagrams. After risks are assigned to project and portfolio schedules, Monte Carlo simulation of the project schedule is performed. ECM simplifies complex risk analysis process.

Description: This paper provides a description of a system using statistical spatial methods for measuring, analysis, and managing ERM losses. An example of its application to a delinquency rate in a residential mortgage portfolio analysis demonstrates how this methodology can help decrease a company’s exposure in high-risk areas and increase exposure in low-risk areas.

Description: People naturally observe risk in the form of the range of experienced gains and losses which is usually represented in statistical terms by standard deviation. In most cases, there are not enough observed values to reliably discern the true amount of losses that are possible in extreme situations. Statistical techniques that have long been applied to insurance company risks are being used to extrapolate from observed risk to extreme tails of the distribution of gains and losses. These processes are essentially an extrapolation from the “known” risk of volatility near the mean to “unknown” risk of extreme losses. “Fat tails” is a term often applied to risks where the likely losses in rare situations significantly exceeds the predictions of a Gaussian (“thin tailed”) distribution. This paper will look at the relationship between the observed risk and the extreme risk in statistical insurance r isk models and discuss how we might create a discussion among model users about the appropriateness of that relationship for specific types of risks.

Description: This paper provides an overview of those cognitive biases most often responsible for flawed risk assessments and provides practical techniques to mitigate them. Through a combination of mathematical rigor, case study, metaphor, and logic, the author highlights systematic errors in our thinking about risk and provides ways to avoid them.

Description: State regulators' review of predominant LTC Risks after review of year-end AG51 reports.

Description: Review of The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, by prominent statistician David J. Hand.