Pricing equity-linked life insurance contracts with multiple risk factors by neural networks

TL;DR

This paper develops a neural network-based method for pricing complex equity-linked life insurance contracts with multiple risk factors, accounting for hedging and risk margins, and demonstrates its effectiveness through numerical analysis.

Contribution

It introduces a neural network approach to solve non-linear PDEs and BSDEs for pricing multi-factor equity-linked insurance, incorporating risk margins and hedging strategies.

Findings

Neural networks accurately approximate prices of complex insurance contracts.

The method effectively handles multiple stochastic risk factors.

Sensitivity analysis shows robustness of the pricing model.

Abstract

This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.