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: 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 presentation from Session 90 of the SOA June 2005 New Orleans Health/Pension Spring Meeting focuses on consulting about and communicating retirement plan risks, as well as risk management and mitigation strategies, to plan sponsors. From the Record of the Society of Actuaries, Vol. 31, No. 2.

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: 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: Committee members discuss the objectives of COVARA, areas of current research, summary of findings about C-1 Risk, C-2 Risk, C-3 Risk, and Combination of Risks, and future areas of committee interest. From the 1987 Record of Society of Actuaries, Vol. 13, No. 4B.

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

Description: Presented at August 2011 46th Actuarial Research Conference. Develops the Multi-threshold Compound Poisson surplus process with interest which is useful for pricing. Then develops the Gerber-Shiu function for MAP processes featuring interest which allow claims to be factored in. Finally develops closed-form solutions of the absolute ruin probability for Erlang IAT and exponential claims.