Efficient valuation of SCR via a neural network approach

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• 作者：Seyed Amir Hejazi ; ^{amir@cs.toronto.edu" class="auth_mail" title="E-mail the corresponding author} ; Kenneth R. Jackson ^{krj@cs.toronto.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Variable annuity ; Spatial interpolation ; Neural network ; Portfolio valuation ; Solvency Capital Requirement (SCR)
• 刊名：Journal of Computational and Applied Mathematics
• 出版年：2017
• 出版时间：15 March 2017
• 年：2017
• 卷：313
• 期：Complete
• 页码：427-439
• 全文大小：1041 K

文摘

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.