Hedging with linear regressions and neural networks

Ruf, Johannes and Wang, Weiguan (2022) Hedging with linear regressions and neural networks. Journal of Business and Economic Statistics, 40 (4). 1442 - 1454. ISSN 0735-0015

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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.

Item Type:	Article
Official URL:	https://www.tandfonline.com/toc/ubes20/current
Additional Information:	© 2021 The Authors
Divisions:	Mathematics
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
Date Deposited:	09 Dec 2020 11:15
Last Modified:	09 May 2024 17:51
URI:	http://eprints.lse.ac.uk/id/eprint/107811

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