Hedging with Neural Networks
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| Authors | Weiguan Wang, Johannes Ruf |
| Journal/Conference Name | arXiv preprint |
| Paper Category | Artificial Intelligence |
| Paper 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 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. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks. |
| Date of publication | 2020 |
| Code Programming Language | Jupyter Notebook |
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