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: Read the December 2017 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Read the August 2016 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Comments on Robert North’s paper, a real case study of disclosing MVL in a public employee retirement system.

Description: An exploration of Canada’s federal regulator of financial institutions (OSFI) and its more formal assessment of the actuarial function of insurers.

Description: Decomposes guaranteed minimum maturity benefit option into a basket of components, and values each component with both a Black-Scholes model and a regime-switching GARCH model. Performs sensitivity analysis on dynamic lapse assumption.

Description: An exploration of risk management challenges in the Middle East, including historical overcapitalization, inadequate data and governance issues.

Description: Statistics were invented to describe heterogeneity and are now applied to tackle alea. It may not be appropriate. Let's clarify their field of relevance.

Description: Essays advocating for incentive compensation linked to the level of risk in an organization.