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: Financial regulators, rating agencies and many commentators have blamed weak Risk Culture for many of the large losses and financial company failures of the past decade. But their exposition regarding a strong Risk Culture only goes as far as describing a few of the risk management practices of an organization and falls far short of describing the beliefs and motivations that are at the heart of any culture. This discussion will present thinking about how the fundamental beliefs of Neo Classical Economics clash with the recommended risk practices and how the beliefs that underpin Enterprise Risk Management are fundamentally consistent with the recommended risk management practices but differ significantly from Neo Classical Economics beliefs.

Description: This article shows how market participants’ view of risk impacts not just their own choices, but also how they impact on everyone else's choices as well.