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Managing and hedging the risks associated with Variable Annuity (VA) productsrequire intraday valuation of key risk metrics for these products. The complexstructure of VA products and computational complexity of their accurateevaluation have compelled insurance companies to adopt Monte Carlo (MC)simulations to value their large portfolios of VA products. Because the MCsimulations are computationally demanding, especially for intraday valuations,insurance companies need more efficient valuation techniques. Recently, aframework based on traditional spatial interpolation techniques has beenproposed that can significantly decrease the computational complexity of MCsimulation (Gan and Lin, 2015). However, traditional interpolation techniquesrequire the definition of a distance function that can significantly impacttheir accuracy. Moreover, none of the traditional spatial interpolationtechniques provide all of the key properties of accuracy, efficiency, andgranularity (Hejazi et al., 2015). In this paper, we present a neural networkapproach for the spatial interpolation framework that affords an efficient wayto find an effective distance function. The proposed approach is accurate,efficient, and provides an accurate granular view of the input portfolio. Ournumerical experiments illustrate the superiority of the performance of theproposed neural network approach compared to the traditional spatialinterpolation schemes.

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