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Hedging with linear regressions and neural networks

Author

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  • Ruf, Johannes
  • Wang, Weiguan

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.

Suggested Citation

  • Ruf, Johannes & Wang, Weiguan, 2022. "Hedging with linear regressions and neural networks," LSE Research Online Documents on Economics 107811, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:107811
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    File URL: http://eprints.lse.ac.uk/107811/
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    Citations

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    Cited by:

    1. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Hedging option books using neural-SDE market models," Papers 2205.15991, arXiv.org.
    2. Xiaofei Shi & Daran Xu & Zhanhao Zhang, 2021. "Deep Learning Algorithms for Hedging with Frictions," Papers 2111.01931, arXiv.org, revised Dec 2022.
    3. 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).

    More about this item

    Keywords

    benchmarking; Black-Scholes; data Leakage; hedging error; leverage effect; statistical hedging; Taylor & Francis deal;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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