论文标题
具有线性回归和神经网络的对冲
Hedging with Linear Regressions and Neural Networks
论文作者
论文摘要
我们将神经网络作为非参数估计工具研究，用于套期保值。为此，我们设计了一个名为Hedgenet的网络，该网络直接输出对冲策略。该网络经过训练，以最大程度地减少对冲误差而不是定价误差。该网络应用于标准普尔500和Euro STOXX 50选项的tick价格，该网络能够显着减少Black-Scholes基准的平均平方对冲误差。但是，通过包含杠杆作用的简单线性回归产生了类似的好处。
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.
