A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities

TL;DR

This paper introduces a neural network-based spatial interpolation method to efficiently value large portfolios of variable annuities, outperforming traditional techniques in accuracy and computational speed.

Contribution

The paper proposes a novel neural network approach for spatial interpolation that improves accuracy, efficiency, and granularity in valuing large variable annuity portfolios.

Findings

Neural network approach outperforms traditional interpolation methods.

Significant reduction in computational time for intraday valuation.

Enhanced accuracy and granularity in risk metric estimation.

Abstract

Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.