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Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

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  • Pascal Franc{c}ois
  • Genevi`eve Gauthier
  • Fr'ed'eric Godin
  • Carlos Octavio P'erez Mendoza

Abstract

We present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The optimal hedging strategy is obtained through a deep policy gradient-type reinforcement learning algorithm, with a novel hybrid neural network architecture improving the training performance. The favorable inclusion of forward-looking information embedded in the volatility surface allows our procedure to outperform several conventional benchmarks such as practitioner and smiled-implied delta hedging procedures, both in simulation and backtesting experiments.

Suggested Citation

  • Pascal Franc{c}ois & Genevi`eve Gauthier & Fr'ed'eric Godin & Carlos Octavio P'erez Mendoza, 2024. "Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information," Papers 2407.21138, arXiv.org.
    • Handle: RePEc:arx:papers:2407.21138
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    References listed on IDEAS

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