Description: European insurance regulation pretends to capture and rely on the 1-year 1-in-200 VaR. But there cannot exist any scientific calibration of such measure. Why?

Description: Presented at August 2011 46th Actuarial Research Conference. This paper focuses on mainly focus on the case in which the risk variable follows a distribution function from a max-domain of attraction.

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: The editors provide highlights of the articles included in the April 2016 issue of Risk Management.

Description: This article highlights some of OSFI’s expectations regarding the ORSA process, and provides general feedback and observations from OSFI’s reading of 2014 ORSA reports (including KMR) and discussions with insurers.

Description: Highlight of the ERM Symposium

Description: A discussion of risk-relevant resources available at the Society of Actuaries Risk Management Task Force Web site