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Hedging with linear regressions and neural networks

Ruf, Johannes and Wang, Weiguan (2020) Hedging with linear regressions and neural networks. Journal of Business and Economic Statistics. ISSN 0735-0015 (In Press)

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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 minimise 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: © 2020 American Statistical Association
Divisions: Mathematics
Statistics
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HA Statistics
Date Deposited: 09 Dec 2020 11:15
Last Modified: 09 Mar 2021 00:33
URI: http://eprints.lse.ac.uk/id/eprint/107811

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