Hedging With Linear Regressions and Neural Networks

Johannes Ruf and Weiguan Wang

Journal of Business & Economic Statistics, 2022, vol. 40, issue 4, 1442-1454

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.

Date: 2022
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DOI: 10.1080/07350015.2021.1931241

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