Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Risk Management
2025-08-14 v2 Machine Learning
Computational Finance
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.
Keywords
Cite
@article{arxiv.2407.21138,
title = {Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information},
author = {Pascal François and Geneviève Gauthier and Frédéric Godin and Carlos Octavio Pérez Mendoza},
journal= {arXiv preprint arXiv:2407.21138},
year = {2025}
}