Neural networks for option pricing and hedging: a literature review

Johannes Ruf and Weiguan Wang

Journal of Computational Finance

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.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-computational-fina ... -a-literature-review (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ0:7659611

Access Statistics for this article

More articles in Journal of Computational Finance from Journal of Computational Finance
Bibliographic data for series maintained by Thomas Paine ().