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Using Neural Networks to Price and Hedge Variable Annuity Guarantees

VA Hedging & Forecasting, Talcott Resolution, Windsor, CT 06095, USA
Department of Mathematics, Robert Morris University, Moon Township, PA 15108, USA
Author to whom correspondence should be addressed.
Received: 1 November 2018 / Revised: 28 November 2018 / Accepted: 20 December 2018 / Published: 23 December 2018
(This article belongs to the Special Issue Young Researchers in Insurance and Risk Management)
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This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations that are required for the fair value of these liabilities to converge. This computational requirement worsens when Greeks must be calculated to hedge the liabilities of these guarantees. A feedforward neural network is a universal function approximator that is proposed as a useful machine learning technique to interpolate between previously calculated values and avoid running a full simulation to obtain a value for the liabilities. We propose methodologies utilizing neural networks for both the tasks of pricing as well as hedging four different varieties of variable annuity guarantees. We demonstrated a significant efficiency gain using neural networks in this manner. We also experimented with different error functions in the training of the neural networks and examined the resulting changes in network performance. View Full-Text
Keywords: variable annuities; GMxB; hedging; neural networks variable annuities; GMxB; hedging; neural networks

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Doyle, D.; Groendyke, C. Using Neural Networks to Price and Hedge Variable Annuity Guarantees. Risks 2019, 7, 1.

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