Papers › Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Pascal François, Geneviève Gauthier, Frédéric Godin, Carlos Octavio Pérez Mendoza
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.
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