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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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Identification Number: 10.1080/07350015.2021.1931241


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:
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: 17 Oct 2022 07:45

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