英文标题：
《Mortality data reliability in an internal model》
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作者：
Fabrice Balland, Alexandre Boumezoued, Laurent Devineau, Marine
  Habart, Tom Popa
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最新提交年份：
2018
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英文摘要：
  In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued (2016) for countries for which the method applies (France and Italy), and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0,5% longevity improvement can be extracted, allowing for the calculation of the Solvency Capital Requirement (SCR). More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.
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中文摘要：
在本文中，我们讨论了一些死亡率数据异常对Solvency 2框架中捕捉长寿风险的内部模型的影响。特别是，我们关注异常队列效应，例如1919和1920代的队列效应，人类死亡率数据库提供的周期表分别显示了低死亡率和高死亡率。为了为三个感兴趣的国家（法国、意大利和西德）提供更正的表格，我们使用Boumezoued（2016）为该方法适用的国家（法国和意大利）开发的方法，并为西德提供该方法的扩展，因为每月生育率历史不足以覆盖感兴趣的几代人。这些死亡率表是预测未来情景的随机死亡率模型的重要输入，从中可以提取0.5%的极端寿命改善，从而计算偿付能力资本要求（SCR）。更准确地说，为了评估Solvency II框架中此类异常的影响，我们使用了一个基于三种常见随机模型的简化内部模型，结合老年人的闭合表方法预测未来的死亡率。纠正这一偏差明显提高了死亡率输入的数据质量，这在今天至关重要，并略微降低了资本要求。总体而言，寿命风险评估以及随机死亡率模型的选择保持稳定。作为这种数据质量改进的附带收益，更定期的估计参数允许对长寿风险进行新的见解和精确的评估。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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