Description: Understanding the variability of survival probabilities, both between and within cohorts, is important in order to understand life-cycle decisions under uncertainty. In this paper we analyze the subjective probabilities of survival to specific target ages provided by respondents to the Survey of Health, Ageing and Retirement in Europe (SHARE). To evaluate how these probabilities compare with objective data from life tables, and avoid the problems associated with a naive use of period life tables, we construct cohort life tables from the sequence of period life tables available in the Human Mortality Database and use them to compute actuarial probabilities of survival to the same target ages. We find that male subjective survival probabilities are close to the probabilities computed from our cohort life tables, whereas female subjective probabilities are always lower. We also find that subjective survival probabilities are on average higher for more educated people, those with higher household income, and those with better health. This evidence suggests that both income and health matter for own assessments of subjective survival.

Description: Informal Discussion Transcript - Session 4B

Description: Modeling Medical Cost Trends for Advancing Age in the Long Run

Description: Genetically Informed Longevity

Description: Abstract for Genetically Informed Longevity

Description: General Session 3 discussant comments

Description: Abstract for How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Lessons from Biodemographic Analyses of Longitudinal Data

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 paper provides an overview of those cognitive biases most often responsible for flawed risk assessments and provides practical techniques to mitigate them. Through a combination of mathematical rigor, case study, metaphor, and logic, the author highlights systematic errors in our thinking about risk and provides ways to avoid them.

Description: The Market Cost of Capital approach has emerged as the standard for estimating risk margins for insurers' fair value balance sheets. This paper takes some of the ideas developed for valuing life insurance liabilities and applies them to the problem of valuing credit risky bonds. The basic idea is that credit spreads should cover the cost a) best estimate defaults plus the cost of holding capital for b) contagion risk (e.g. a credit crunch) and c) parameter risk, this risk that the best estimate is wrong and must be revised. We argue that the margins required for parameter risk can capture liquidity issues. In addition, the models developed here allow the cost of capital rate itself to be a random variable which allows credit spreads to open and close stochastically. Finally, the paper argues that it is reasonable to include something like AA best estimate default rates and liquidity spreads when valuing insurance liabilities. The main rationale for doing so is the idea that there are elements of the total credit risk issue that can be hedged between the assets and liabilities.