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arXiv · 2004.08891

Hedging with Linear Regressions and Neural Networks

Johannes Ruf·Weiguan Wang
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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.

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BibTeXRIS

Johannes Ruf, Weiguan Wang. 2020-04-19. Hedging with Linear Regressions and Neural Networks. https://doi.org/10.1080/07350015.2021.1931241

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