Description: The paper argues that Risks Evaluation at its core is a psychological process that can lead to the special type of chaos described in modern Chaos Theory. It will argue that quantification and scientific modeling does not exempt risk assessment from the potential for deep biases. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

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: 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 outlines a risk measurement framework in which risk measures are derived by qualifying cash-flow-based risk measures on the state of the firm's debt capacity. This risk framework overcomes some of the weaknesses of the popular 'at-risk' framework. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

Description: Enterprise risk management ERM is for the measurement and the management of all significant risks of the business holistically irrespective of types and sources. The study offers significant advances in the current debate on the performance of enterprise risk management, in particular, minimizing the existing gap between the academic understanding and practitioners’ experience on the topic.

Description: The intent of this paper is the analysis of the influence of risk that took place during winter and summer 2006 on economic results of utilities selling power to final customers. Efficient risk management requires the restructuring of the approach to company management. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

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: There have been continual advances in the modeling of financial series but most are aimed at the pricing of derivatives. Different criteria are needed for development of scenarios for risk management. Some recent methods will be reviewed with an eye on risk-management applications, including using the simulated method of moments to parameterize multifactor models, fractional differencing and other methods to model series with persistent autocorrelation, and models to flatten out the volatility smile, such as jump-diffusion models. These methods will be illustrated with applications to inflation, interest rates, equity prices and exchange rates.

Description: This paper sets out to demonstrate that the economic capital, risk drivers, and business model are intricately integrated, and that this has broad implications on enterprise risk management for financial institutions. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

Description: This study presents a new framework which can measure integrated marked risk and credit risk for portfolios consisting of various assets through continuous time horizons. In a simulation, the study shows that asset type, initial rating, time horizon and asset return distribution assumptions are all significant factors to influence the portfolio value distributions and hence the integrated VaRs. From 2007 Enterprise Risk Management Symposium, M-AS07-1.