英文标题：
《Efficient Valuation of SCR via a Neural Network Approach》
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作者：
Seyed Amir Hejazi, Kenneth R. Jackson
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最新提交年份：
2016
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英文摘要：
  As part of the new regulatory framework of Solvency II, introduced by the European Union, insurance companies are required to monitor their solvency by computing a key risk metric called the Solvency Capital Requirement (SCR). The official description of the SCR is not rigorous and has lead researchers to develop their own mathematical frameworks for calculation of the SCR. These frameworks are complex and are difficult to implement. Recently, Bauer et al. suggested a nested Monte Carlo (MC) simulation framework to calculate the SCR. But the proposed MC framework is computationally expensive even for a simple insurance product. In this paper, we propose incorporating a neural network approach into the nested simulation framework to significantly reduce the computational complexity in the calculation. We study the performance of our neural network approach in estimating the SCR for a large portfolio of an important class of insurance products called Variable Annuities (VAs). Our experiments show that the proposed neural network approach is both efficient and accurate.
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中文摘要：
作为欧盟推出的偿付能力II新监管框架的一部分，保险公司需要通过计算一个称为偿付能力资本要求（SCR）的关键风险指标来监控其偿付能力。官方对SCR的描述并不严格，导致研究人员开发了自己的计算SCR的数学框架。这些框架很复杂，很难实现。最近，Bauer等人提出了一种嵌套蒙特卡罗（MC）模拟框架来计算SCR。但是，即使对于一个简单的保险产品，提出的MC框架在计算上也很昂贵。在本文中，我们建议将神经网络方法合并到嵌套模拟框架中，以显著降低计算中的计算复杂性。我们研究了神经网络方法在估计一类重要的保险产品（称为可变年金（VAs））的大型投资组合的SCR时的性能。我们的实验表明，所提出的神经网络方法是有效和准确的。
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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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