Hedging with linear regressions and neural networks

Johannes Ruf and Weiguan Wang

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimize the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.

Keywords: benchmarking; Black-Scholes; data Leakage; hedging error; leverage effect; statistical hedging (search for similar items in EconPapers)
JEL-codes: C1 J1 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2022-10-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-isf, nep-ore and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Published in Journal of Business and Economic Statistics, 2, October, 2022, 40(4), pp. 1442 - 1454. ISSN: 0735-0015

Downloads: (external link)
http://eprints.lse.ac.uk/107811/ Open access version. (application/pdf)

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:ehl:lserod:107811

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().