ResPoNet: A Residual Neural Network for Efficient Valuation of Large Variable Annuity Portfolios

Heng Xiong, Jie Xu, Rogemar Mamon () and Yixing Zhao
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Heng Xiong: Economics and Management School, Wuhan University, Wuhan 430072, China
Jie Xu: Economics and Management School, Wuhan University, Wuhan 430072, China
Rogemar Mamon: Department of Statistical and Actuarial Sciences, The University of Western Ontario, London, ON N6A 3K7, Canada
Yixing Zhao: School of Finance, Guangdong University of Foreign Studies, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 12, 1-22

Abstract: Accurately valuing large portfolios of Variable Annuities (VAs) poses a significant challenge due to the high computational burden of Monte Carlo simulations and the limitations of spatial interpolation methods that rely on manually defined distance metrics. We introduce a residual portfolio valuation network (ResPoNet), a novel residual neural network architecture enhanced with weighted loss functions, designed to improve valuation accuracy and scalability. ResPoNet systematically accounts for mortality risk and path-dependent liabilities using residual layers, while the custom loss function ensures better convergence and interpretability. Numerical results on synthetic portfolios of 100,000 contracts show that ResPoNet achieves significantly lower valuation errors than baseline neural and spatial methods, with faster convergence and improved generalization. Sensitivity analysis reveals key drivers of performance, including guarantee complexity and contract maturity, demonstrating the robustness and practical applicability of ResPoNet in large-scale VA valuation.

Keywords: neural network; machine learning; variable annuity; portfolio valuation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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