Hedging With Linear Regressions and Neural Networks
研究神经网络作为非参数估计工具用于期权对冲，设计HedgeNet网络直接输出对冲策略，在标普500和欧洲斯托克50期权数据上显著降低对冲误差，但简单线性回归也能达到类似效果。
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.