Using Neural Networks to Price and Hedge Variable Annuity Guarantees

Daniel Doyle and Chris Groendyke
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Daniel Doyle: VA Hedging & Forecasting, Talcott Resolution, Windsor, CT 06095, USA
Chris Groendyke: Department of Mathematics, Robert Morris University, Moon Township, PA 15108, USA

Risks, 2018, vol. 7, issue 1, 1-19

Abstract: 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.

Keywords: variable annuities; GMxB; hedging; neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:7:y:2018:i:1:p:1-:d:192723

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