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Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
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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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Is Deep Hedging Reinforcement Learning?
Deep hedging is a Monte Carlo, actor-only, pathwise-gradient policy-gradient method, and therefore falls under the standard reinforcement learning umbrella.
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