保险公司长寿风险度量

本文梳理了几种重要的动态死亡率预测模型,给出了长寿风险度量的三种方法,选取了保险公司及国家统计局公布的人口死亡率数据,度量了保险公司的两类长寿风险,对不同方法下长寿风险的度量结果进行比较,并分析了极限年龄与折现率变动对长寿风险影响的敏感性。研究发现：基于保险公司经营稳健性的视角,第一类长寿风险的度量应采用随机模拟法,第二类长寿风险的度量应采用标准公式法;极限年龄的变动对长寿风险的影响较小,保险公司无上调经验生命表极限年龄的必要;长寿风险对折现率的变动较为敏感,提高折现率,保险公司长寿风险显著降低。     

Bootstrap方法在死亡模型中的应用

 由于不同国家死亡率改善现象不同,世界各国所使用的死亡率模型皆不尽相同,而且不同年龄段的死亡率模型也不同。实际中,我们常常采用Gompertz模型、Makeham模型、Weibull模型等拟合高年龄段人口的死亡率,但是因高年龄段人口的死亡数据资料不够充分,较少有人以统计的观点给出模型适合性的检验过程。因此本研究提出利用Bootstrap方法检验死亡模型假设的方法,包括模型适合性的检验、参数估计、参数假设检验等。最后,本文应用中国1997－2007年65－89岁人口的粗死亡率数据,提出适合的死亡模型,然后给出利用Bootstrap方法进行死亡模型检验的全过程。     

Adaptive autoregressive priors for area and time structured mortality data

Mortality counts by age and area are relevant to obtaining small area life tables and summary statistics such as life expectancy. A Bayesian approach to small area life tables is proposed here based on the principle of smoothing (or “pooling strength”) over adjacent ages or areas. Several schemes have been suggested to reflect dependence between age categories x or areas i  , such as conditional autoregressive priors based on the principle of local smoothing, determined by adjacency of age groups or spatial proximity. It is argued here that a more flexible approach is to allow a mix of local and global smoothing over age groups and areas, as determined by the data and additional parameters κ∈[0,1]$\kappa \in [0,1]$ and λ∈[0,1]$\lambda \in [0,1]$ for age and area, respectively. An extension is also proposed to reflect the fact that the appropriate mix between local and global smoothing may not be constant across age bands or across the region being studied. For example, local spatial smoothing will not be appropriate if an area is disparate from its neighbours (e.g. in terms of social distance), and so area specific mixing parameters _λi${\lambda }_i$ are introduced. The _λi${\lambda }_i$ may be modelled by logit regression on observed sources of disparity between neighbouring areas. The application considers small area life tables for males over 625 small areas (electoral wards) in London over 2003–2005.     

宗教参与对我国高龄老人死亡风险的影响分析

根据中国高龄老人健康长寿跟踪调查数据(1998～2005年),利用Cox比例风险模型考察宗教参与对高龄老人死亡风险的影响机制,深入分析人口学特征、肌体健康、心理健康、健康行为及社会经济支持这五类因素在宗教参与和死亡风险关系中的交互作用,以此对西方文献中相关的4种理论模型进行实证检验。结果表明,宗教参与对死亡风险存在一定程度的影响,但是控制其他因素则会削弱这种影响,其中肌体健康因素所代表的"替代性机制"能够将两者关系的显著作用完全消除。     

Pricing longevity-linked derivatives using a stochastic mortality model

Abstract

We propose a 2-factor MBMM model with exponential Lévy process to develop a stochastic mortality process. The two components are fitted by two independent NIG distributions. Compared to Lee–Carter model or 1-factor MBMM model, our mortality model explains more variation and improves the goodness of fit by including the second time component. Based on the improved model, we price three longevity-linked financial instruments, namely the longevity bond, q-forward and s-forward. The pricing is demonstrated on English and Welsh males aged 65 in 2013. Results indicate that the 2-factor MBMM model gives the highest price for mortality-related type of contract.     

两人群引力模型框架下的中国死亡率预测①--结合美国相应数据

Lee－Carter系列模型是对一个人群的死亡率动态建模和预测的模型。由于中国死亡率抽样数据的质量问题导致模型预测的效果不如国外文献所反映的那么精确。本文在两人群引力模型框架下结合中国和美国同期死亡率数据建模，并将结果与相应的单人群模型比较。研究表明，引力模型与APC模型相结合取得了最好的效果，在此基础上本文预测2025年老年抚养比会急剧上升到23．32％，2030年的婚配男性人口超出女性约2079万，婴幼青少年20年间累计死亡人数约293万。     

我国人口转变模式及其形成机制

通过对我国近 5 0年人口出生率、死亡率、自然增长率统计数据的分析认为 ,我国已经完成了由传统的人口再生产类型向现代的人口再生产类型的人口转变过程。又通过对我国 5 0年来的人口转变模式的分析 ,探讨了其转变机制 ,指出社会经济发展和计划生育政策是人口转变的根本原因     

基于均衡关系的中国人口死亡率预测模型

人口死亡率反映了人口的死亡程度,准确预测死亡率是人口科学及人口经济学研究的重点之一,同时也是长寿风险测量的重要数据基础。基于Lee-Carter模型,探索中国大陆与台湾地区死亡率的相关性,通过协整分析考虑两地死亡率的长期均衡关系,创新性地建立基于相关性的向量误差修正模型(VECM),克服传统自回归移动平均模型(ARIMA)使用有限数据进行预测的局限性;均方预测误差作为检验标准,结果表明:基于VECM模型的预测效果比传统的预测效果更佳;基于中国大陆地区和台湾地区的死亡率长期均衡关系,可以为两地联合长寿债券的定价提供重要参考。     

Youth and Well-Being: A South African Case Study

This paper was a result of an analysis from various data sources with a purpose to develop a better understanding of the level of socio-economic well being of young people in South Africa. Such understanding is aimed at enabling government to plan and implement well-structured and integrated development programmes that are relevant to the socio-economic needs of the youth and that will enable them to fully participate in all aspects of society. Two main sources of data were used for this analysis. The first is the Status of the Youth (SYR) data set. The second data set used in this study is the South African Social Attitudes Survey (SASAS) which is a national representative sample of 5,000 households. The study shows that the quality of life among majority of young people remains low, reflecting the historical racial cleavages of South African society. This is mainly due to the emergence of the AIDS pandemic in South Africa, and the fact that many young people remain outside the labour market.     

Demographic pressure,economic development,and social engineering: An assessment of fertility declines in the second half of the twentieth century

Population Research and Policy Review - Concerns over the prospects of explosive demographic growth led to concerted efforts to engineer fertility reductions in the developing world, while skeptics...