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arXiv · 2407.21138

Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

Pascal François·Geneviève Gauthier·Frédéric Godin·Carlos Octavio Pérez Mendoza
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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. 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. The outperformance is more pronounced in the presence of transaction costs.

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Pascal François, Geneviève Gauthier, Frédéric Godin, Carlos Octavio Pérez Mendoza. 2024-07-30. Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information. https://arxiv.org/abs/2407.21138

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