Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

TL;DR

This paper introduces a deep reinforcement learning-based dynamic hedging strategy for S&P 500 options that leverages implied volatility surface feedback to improve performance over traditional methods, especially with transaction costs.

Contribution

It develops a novel deep policy gradient reinforcement learning approach that incorporates implied volatility surface dynamics into option hedging strategies.

Findings

Outperforms conventional delta hedging methods in simulations and backtests.

Shows significant improvement in hedging performance when accounting for transaction costs.

Utilizes forward-looking implied volatility information to enhance hedge effectiveness.

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.