Description: When companies engage in risk management activities, they can be seen as modifying their distributions of future outcomes. Often, this takes the form of trading off a sure cost for a decrease in some potential risk of loss. Companies may carry out these activities with several goals in mind, such as reduction in earnings volatility or preservation of capital. This paper will demonstrate a method for evaluating possible strategies by dividing the effects on costs and risks into three tranches: earnings, impact and estate. The tranches can be associated with different interests of the various stakeholders. This trifurcation analysis can help to explain the risk-reward trade-offs of different risk treatment alternatives and can aid in risk-related decisionmaking.

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: A discussion of the pillars required to build the needed paradigm for insurance company risk management. From The Actuary Magazine April 2005

Description: This article discusses four approaches to managing the enterprise-wide risk: Diversification, Loss Controlling, Risk Trading and Risk Steering.

Description: We can do a better job of communicating the degree of "riskiness" that is inherent in our models of risk. The "Coefficient of Risk" is one way to present fatness of model tails in simple terms using widely understood basic concepts.

Description: Editorial for the October/November 2012 edition of The Actuary magazine discussing risk and uncertainty as two different concerns.

Description: Most reasonable risk mitigation strategies provide benefit in some circumstances, but come at a cost that adversely affects results in other scenarios. A research paper by the authors considers the view of multiple stakeholders to answer this question, resulting in a process they call Trifurcation.

Description: Describes ways to avoid the next financial crisis by following the example of those who got it right and discerning what you could be doing to prevent your firm from experiencing excessive losses in future crises.

Description: The author explores the importance of treating the modeling process as a dialogue and collaboration.