Hedging with Linear Regressions and Neural Networks
29 Pages Posted: 14 May 2020 Last revised: 26 Oct 2020
Date Written: October 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. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.
Keywords: Benchmarking; Black-Scholes; Data Leakage; Hedging error; Leverage effect; Statistical hedging
JEL Classification: G13, C45
Suggested Citation: Suggested Citation