Longevity risk calculation is central to both public pension plans and life insurance companies. The essential work in quantifying longevity risk is to model the dynamics of mortality rates. Based on the goodness-of-fit with respect to the available mortality rates at various ages, the dynamic mortality rate models mainly deal with two issues, namely, age extrapolation and trend prediction. In addressing the gaps in the literature, this paper integrates the extreme modeling method for oldest-old mortality rate and the hierarchical modeling technique, and proposes a dynamic mortality rate modeling approach. The paper further demonstrates the dynamic evolutions of old-age and oldest-old mortality rates by place of residence and gender from 2000 to 2010 in China, with better measurement of the tail risks of the life span distribution, and thus improves the quantitative method on China's longevity risk research.

Attempt has been made in this research to apply generalized linear models in graduating China′s life table mortality rates.Using demographic data of deaths by age and gender from China Population Statistical Yearbooks 1995-2006 and Statistical Yearbooks of China′s Population and Employment 2007-2010,the relationships between mortality and age and between mortality and year are explored by fitting death rates at ages from 0 to 89 using Poisson regression and negative binomial regression.Upon comparison of the fitting effects of the two models,the paper proposes to use B-spline function to smooth the death rates.Implications of this study are discussed for constructing China′s empirical life tables,providing theoretical foundation and practical reference for mortality analysis by China Insurance Regulatory Commission,and achieving market-oriented rates of life insurance and scientific management of the life insurance industry in China.