英文标题：
《Forecasting Leading Death Causes in Australia using Extended
  CreditRisk$+$》
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作者：
Pavel V. Shevchenko, Jonas Hirz and Uwe Schmock
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最新提交年份：
2015
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英文摘要：
  Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk$^+$ methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. These common factors introduce dependence between policyholders in annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. Moreover this framework allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include improvement in health treatments or better medication. In this paper, using publicly available data for Australia, we estimate the model using Markov chain Monte Carlo method to identify leading death causes across all age groups including long term forecast for 2031 and 2051. On top of general reduced mortality, the proportion of deaths for certain certain causes has changed massively over the period 1987 to 2011. Our model forecasts suggest that if these trends persist, then the future gives a whole new picture of mortality for people aged above 40 years. Neoplasms will become the overall number-one death cause. Moreover, deaths due to mental and behavioural disorders are very likely to surge whilst deaths due to circulatory diseases will tend to decrease. This potential increase in deaths due to mental and behavioural disorders for older ages will have a massive impact on social systems as, typically, such patients need long-term geriatric care.
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中文摘要：
最近，我们在Hirz等人（2015）中开发了一个新框架，使用扩展的CreditRisk$^+$方法对随机死亡率进行建模，这与之前用于死亡率建模的传统时间序列方法非常不同。在这个框架中，死亡是由常见的潜在随机风险因素驱动的，这些因素可能被解释为肿瘤、循环系统疾病或特异性成分等死亡原因。这些共同因素导致年金投资组合中的投保人之间或人群中的死亡事件之间存在依赖关系。该框架可用于构建基于死亡率预测的生命表。此外，该框架允许进行压力测试，因此可以深入了解某些健康状况如何影响保险人的年金支付。这种情况可能包括改善健康治疗或更好的药物治疗。在本文中，我们使用澳大利亚的公开数据，使用马尔可夫链蒙特卡罗方法估计模型，以确定所有年龄组的主要死因，包括2031年和2051年的长期预测。在总体死亡率下降的基础上，1987年至2011年期间，某些原因导致的死亡比例发生了巨大变化。我们的模型预测表明，如果这些趋势持续下去，那么未来将为40岁以上人群的死亡率提供一个全新的画面。肿瘤将成为头号死亡原因。此外，由于精神和行为障碍导致的死亡人数很可能激增，而由于循环系统疾病导致的死亡人数将趋于减少。老年人因精神和行为障碍而死亡的潜在增加将对社会系统产生巨大影响，因为这类患者通常需要长期的老年护理。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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