Hedging with Linear Regressions and Neural Networks
This work addresses hedging inefficiencies for financial practitioners, but it is incremental as similar benefits were achieved with simple linear regressions.
The authors tackled the problem of hedging options by designing HedgeNet, a neural network trained to minimize hedging error, which significantly reduced the mean squared hedging error compared to the Black-Scholes benchmark on S&P 500 and Euro Stoxx 50 options data.
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.