Using Neural Networks to Price and Hedge Variable Annuity Guarantees

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dc.contributor.author Groendyke, Chris
dc.contributor.author Doyle, Daniel
dc.date.accessioned 2021-02-09T20:36:42Z
dc.date.available 2021-02-09T20:36:42Z
dc.date.issued 2018
dc.identifier.citation Doyle, D. Groendyke, C. (2018) Using Neural Networks to Price and Hedge Variable Annuity Guarantees. Retrieved from : file:///C:/Users/ibraheem/Downloads/risks-07-00001.pdf en_US
dc.identifier.uri http://hdl.handle.net/11347/383
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Risks en_US
dc.title Using Neural Networks to Price and Hedge Variable Annuity Guarantees en_US
dc.type Article en_US


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