Hedging with Neural Networks
31 Pages Posted: 14 May 2020 Last revised: 26 May 2020
Date Written: May 25, 2020
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. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional ‘tagging’ of data, leads to a significant overestimation of the outperformance of neural networks.
Keywords: Benchmarking, Black-Scholes, Data leakage, Delta-vega hedging, Hedging error, Linear regression, Neural network, Statistical hedging
JEL Classification: G13, C45
Suggested Citation: Suggested Citation