Option Pricing by Neural Stochastic Differential Equations: A Simulation-Optimization Approach

Shoudao Wang, L. Jeff Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Classical option pricing models rely on prior assumptions made on the dynamics of the underlying assets. While empirical evidence showed that these models may partially explain the option prices, their performance may be poor when the actual situation deviates from the assumptions. Neural network models are capable of learning the underlying relationship through the data. However, to avoid over-fitting, these models require massive amount of data, which are not available for option pricing problems. We propose a new model by integrating neural networks to a classical option pricing model, thus increasing the model flexibility while requiring a reasonable amount of data. We show that the training of the model, also known as the calibration, may be formulated into a simulation optimization problem, and it may be solved in a way that is compatible to the training of neural networks. Preliminary numerical results show that our approach appears to work well.

Original languageEnglish (US)
Title of host publication2021 Winter Simulation Conference, WSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433112
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 Winter Simulation Conference, WSC 2021 - Phoenix, United States
Duration: Dec 12 2021Dec 15 2021

Publication series

NameProceedings - Winter Simulation Conference
Volume2021-December
ISSN (Print)0891-7736

Conference

Conference2021 Winter Simulation Conference, WSC 2021
Country/TerritoryUnited States
CityPhoenix
Period12/12/2112/15/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

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