人寿保险偿付能力计算的神经网络模型
A neural network model for solvency calculations in life insurance
AI总结：
本文提出用神经网络作为人寿保险偿付能力计算的代理模型，解决复制组合中特征工程与基函数选择难题，并支持风险中性定价以整合入市场风险框架。
摘要：
保险公司在资本和偿付能力模型中广泛使用蒙特卡洛模拟。为克服蒙特卡洛模拟带来的计算问题，大多数大型人寿保险公司使用代理模型，如复制组合。本文基于一个可变年金保证的实例，展示了从业者在构建复制组合时面临的主要挑战：特征工程步骤及随后的基函数选择问题。我们描述了如何将神经网络用作代理模型，以及如何在神经网络上应用风险中性定价，以将该模型整合到市场风险框架中。所提出的模型自然地解决了复制组合的特征工程和特征选择问题。
英文摘要：
Insurance companies make extensive use of Monte Carlo simulations in their capital and solvency models. To overcome the computational problems associated with Monte Carlo simulations, most large life insurance companies use proxy models such as replicating portfolios. In this paper, we present an example based on a variable annuity guarantee, showing the main challenges faced by practitioners in the construction of replicating portfolios: the feature engineering step and subsequent basis function selection problem. We describe how neural networks can be used as a proxy model and how to apply risk-neutral pricing on a neural network to integrate such a model into a market risk framework. The proposed model naturally solves the feature engineering and feature selection problems of replicating portfolios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
