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Hedging With Linear Regressions and Neural Networks

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  • Johannes Ruf
  • Weiguan Wang

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.

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  • Johannes Ruf & Weiguan Wang, 2022. "Hedging With Linear Regressions and Neural Networks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1442-1454, October.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:4:p:1442-1454
    DOI: 10.1080/07350015.2021.1931241
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    Cited by:

    1. Xia, Kun & Yang, Xuewei & Zhu, Peng, 2023. "Delta hedging and volatility-price elasticity: A two-step approach," Journal of Banking & Finance, Elsevier, vol. 153(C).
    2. Chunhui Qiao & Xiangwei Wan, 2024. "Enhancing Black-Scholes Delta Hedging via Deep Learning," Papers 2407.19367, arXiv.org, revised Aug 2024.
    3. Roberto Daluiso & Marco Pinciroli & Michele Trapletti & Edoardo Vittori, 2023. "CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning," Papers 2312.14044, arXiv.org.

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