Pricing Options and Computing Implied Volatilities using Neural Networks

Pricing Options and Computing Implied Volatilities using Neural Networks

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optim...

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Bibliographic Details
Main Authors: Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohte
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Risks
Subjects:
machine learning
neural networks
computational finance
option pricing
implied volatility
GPU
Black-Scholes
Heston
Online Access:https://www.mdpi.com/2227-9091/7/1/16

Internet

https://www.mdpi.com/2227-9091/7/1/16

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