Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

Pascal Fran\c{c}ois, Genevi\`eve Gauthier, Fr\'ed\'eric Godin and Carlos Octavio P\'erez 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, 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.

Date: 2024-07
New Economics Papers: this item is included in nep-big
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