Journal of Computational Finance
ISSN:
1460-1559 (print)
1755-2850 (online)
Editor-in-chief: Christoph Reisinger
Need to know
- Comprehensive literature review on the use of artificial neural networks for option pricing and hedging.
- Comparison in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets.
- Discussion of related work (calibration, solving PDEs, etc) and regularization techniques.
Abstract
Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s, with far more than a hundred papers having been published on the topic. This paper provides a comprehensive review of the field, comparing articles in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets. Further, related work and regularization techniques are discussed.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
