Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
This addresses hedging inefficiencies for financial practitioners, though it is incremental as it builds on existing deep hedging methods with added volatility information.
The paper tackled dynamic hedging of S&P 500 options by integrating implied volatility surface dynamics into a deep reinforcement learning algorithm, resulting in outperformance over conventional benchmarks like delta hedging, especially with transaction costs.
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.