Description: The author constructed the efficient frontier of new business sales by modeling the embedded value and economic capital against a universe of possible growth rates of the three lines of business in a typical three-year planning horizon. The ERM function can use the efficient frontier of new business to provide a more rigorous analysis of the impact of growth rates on the firm’s overall capital efficiency and value creation, hence forming a feedback loop with the businesses in the planning process to optimize the growth strategy.

Description: The author constructed the efficient frontier of new business sales by modeling the embedded value and economic capital against a universe of possible growth rates of the three lines of business in a typical three-year planning horizon. The ERM function can use the efficient frontier of new business to provide a more rigorous analysis of the impact of growth rates on the firm’s overall capital efficiency and value creation, hence forming a feedback loop with the businesses in the planning process to optimize the growth strategy.

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 takes the position that the U.K. actuarial profession has still not come to a single view on financial economics, and the major divisions that remain continue to hamstring its policy development. From Current Pension Actuarial Practice in Light of Financial Economics Symposium, M-RS04-1.

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.