Description: Odd patterns in mortality curves can be caused by bad data. The author hypothesizes that the negatively sloped mortality rates at the highest ages for U.S. males are caused by overstating age at death, and the low mortality rates for Japanese females by failure to report deaths. The paper upholds the first hypothesis but soundly defeats the second. The paper also demonstrates the impact of mishandling incurred but unreported deaths.

Description: Abstract: Odd patterns in mortality curves can be caused by bad data. The author hypothesizes that the negatively sloped mortality rates at the highest ages for U.S. males are caused by overstating age at death, and the low mortality rates for Japanese females by failure to report deaths. The paper upholds the first hypothesis but soundly defeats the second. The paper also demonstrates the impact of mishandling incurred but unreported deaths.

Description: Abstract: Despite a considerable accumulation of empirical and theoretical evidence in the last two decades in favor of a slowdown in the rate of mortality increase with age, the veracity of mortality deceleration observed at very old ages among humans was recently called into question on the basis of: (1) inaccurate data, (2) invalid assumptions, and/or (3) inadequate mixture of birth cohorts with different mortality experiences. In this article, we use a highly reliable set of individual-level data on French-Canadians centenarians born between 1870 and 1896 and who died between 1970 and 2009 to compute new estimates of the age-trajectory of mortality at ages 100 and over. Our results suggest that the rate of mortality increase with age tends to slow down at very old ages, even when special care is taken to avoid issues (1) to (3) outlined above.

Description: Abstract for Sex Differences in Predictors of Health Decline: Results from a 16-Year Longitudinal Cohort Study

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.

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 is the informal discussion transcript from Session 5A: Mortality and Longevity Research from the January 4-6, 2017 Living to 100 Symposium in Orlando.