Prediction of mortality rates using a model with stochastic parameters

Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths

Prediction of mortality rates using augmented data

Prediction of future mortality rate is of significant priority in the insurance industry today as insurers face challenging tasks in providing retirement benefits to a population with increasing life expectancy. A time series model based on multivariate power-normal distribution has been used in the literature on the United States (US) mortality data in the years 1933 to 2000 to predict the future mortality rates in the years 2001 to 2010. To improve the predictive ability, the US mortality data is augmented to include more variables such as death rates by gender and death rates of other countries with similar demographics. Apart from having good ability to cover the observed future mortality rate, the prediction intervals based on the augmented data performed better because they also tend to have shorter interval lengths