Hedging With Linear Regressions and Neural Networks
Hedging With Linear Regressions and Neural Networks
复制标题
使用线性回归和神经网络进行对冲
DOI：
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发表时间：
2020
影响因子：
3
通讯作者：
Weiguan Wang
中科院分区：
文献类型：
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作者：
J. Ruf;Weiguan Wang
摘要我们研究了神经网络作为期权套期保值的非参数估计工具。为此，我们设计了一个名为HedgeNet的网络，它直接输出对冲策略。该网络经过训练以最小化对冲误差而不是定价误差。应用于标准普尔500指数和欧洲斯托克50指数期权的日终和报价，该网络能够显着降低Black-Scholes基准的均方对冲误差。然而，一个类似的好处出现了简单的线性回归，包括杠杆效应。
Abstract 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 minimize 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.