Longevity Risk and the Econometric Analysis of Mortality Trends and Volatility

Carolyn Njenga, M. Sherris
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引用次数: 42

Abstract

Longevity risk and the modeling of trends and volatility for mortality improvement have attracted increased attention driven by ageing populations around the world and the expected financial implications. The original Lee-Carter model that was used for longevity risk assessment included a single improvement factor with differential impacts by age. Financial models that allow for risk pricing and risk management have attracted increasing attention along with multiple factor models. This paper investigates trends, including common trends through co-integration, and the factors driving the volatility of mortality using principal components analysis for a number of developed countries including Australia, England, Japan, Norway and USA. The results demonstrate the need for multiple factors for modeling mortality rates across all these countries. The basic structure of the Lee-Carter model cannot adequately model the random variation and the full risk structure of mortality changes. Trends by country are found to be stochastic. Common trends and co-integrating relationships are found across ages highlighting the benefits from modeling mortality rates as a system in a Vector-Autoregressive (VAR) model and capturing long run equilibrium relationships in a Vector Error-Correction Model (VECM) framework.
长寿风险与死亡率趋势和波动性的计量经济分析
在世界各地人口老龄化和预期的财政影响的推动下,长寿风险以及改善死亡率的趋势和波动的建模引起了越来越多的关注。最初用于寿命风险评估的Lee-Carter模型包括一个单一的改善因素,随着年龄的不同而有不同的影响。允许风险定价和风险管理的金融模型与多因素模型一起受到越来越多的关注。本文对澳大利亚、英国、日本、挪威和美国等发达国家的趋势进行了调查,包括通过协整分析的共同趋势,以及使用主成分分析的驱动死亡率波动的因素。结果表明,需要多种因素来模拟所有这些国家的死亡率。Lee-Carter模型的基本结构不能充分模拟死亡率变化的随机变化和全部风险结构。各国的趋势是随机的。在不同年龄阶段发现了共同的趋势和协整关系,突出了将死亡率作为一个系统在向量自回归(VAR)模型中建模和在向量误差校正模型(VECM)框架中捕获长期平衡关系的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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