Artikel

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 optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.

Language
Englisch

Bibliographic citation
Journal: Risks ; ISSN: 2227-9091 ; Volume: 7 ; Year: 2019 ; Issue: 1 ; Pages: 1-22 ; Basel: MDPI

Classification
Wirtschaft
Subject
machine learning
neural networks
computational finance
option pricing
implied volatility
GPU
Black-Scholes
Heston

Event
Geistige Schöpfung
(who)
Liu, Shuaiqiang
Oosterlee, Cornelis Willebrordus
Bohte, Sander M.
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2019

DOI
doi:10.3390/risks7010016
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Liu, Shuaiqiang
  • Oosterlee, Cornelis Willebrordus
  • Bohte, Sander M.
  • MDPI

Time of origin

  • 2019

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