Description: Three papers use sophisticated actuarial methods to develop a straightforward retirement income strategy that can be implemented in virtually any IRA or 401(k).

Description: Latest summary of LEARN presentations to regulators about reinsurance

Description: Three papers use sophisticated actuarial methods to develop a straightforward retirement income strategy that can be implemented in virtually any IRA or 401(k).

Description: Three papers use sophisticated actuarial methods to develop a straightforward retirement income strategy that can be implemented in virtually any IRA or 401(k).

Description: The article discusses the sustainability of the ACA individual market from a public entitlement standpoint. A key point of measuring sustainability is the reliance on market forces rather than federal budget constraints. The article explains why an actuarial viewpoint of sustainability is necessary to a greater extent than other public assistance and social insurance programs. The article challenges actuaries to be a bold, objective voice in a space that is full of opinionated but often uninformed viewpoints.

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.