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Data-driven Hedging of Stock Index Options via Deep Learning

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  • Jie Chen
  • Lingfei Li

Abstract

We develop deep learning models to learn the hedge ratio for S&P500 index options directly from options data. We compare different combinations of features and show that a feedforward neural network model with time to maturity, Black-Scholes delta and a sentiment variable (VIX for calls and index return for puts) as input features performs the best in the out-of-sample test. This model significantly outperforms the standard hedging practice that uses the Black-Scholes delta and a recent data-driven model. Our results demonstrate the importance of market sentiment for hedging efficiency, a factor previously ignored in developing hedging strategies.

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  • Jie Chen & Lingfei Li, 2021. "Data-driven Hedging of Stock Index Options via Deep Learning," Papers 2111.03477, arXiv.org.
    • Handle: RePEc:arx:papers:2111.03477
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    References listed on IDEAS

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    3. Ruimeng Hu, 2020. "Deep learning for ranking response surfaces with applications to optimal stopping problems," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1567-1581, September.
    4. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    5. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    6. Jay Cao & Jacky Chen & John Hull, 2020. "A neural network approach to understanding implied volatility movements," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1405-1413, September.
    7. Bing Han, 2008. "Investor Sentiment and Option Prices," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 387-414, January.
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