Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

ArXiv ID: 2407.21138 “View on arXiv”

Authors: Unknown

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: Reinforcement learning, Policy gradient, Volatility surface, Delta hedging, S&P 500 options, Equity Options

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including stochastic calculus, partial differential equations for the JIVR model, and deep reinforcement learning (policy gradient algorithms) for a complex hedging problem. Empirically, it is highly rigorous with backtesting on real S&P 500 option data from OptionMetrics over 25 years, includes transaction costs, and provides reproducible code via GitHub.

  flowchart TD
    Goal["Research Goal: Enhance Options Hedging with Volatility Surface Info"]
    --> Input["Data: S&P 500 Options & Volatility Surface Dynamics"]
    
    Input --> Method["Methodology: Deep Policy Gradient RL"]
    Method --> Process["Computational Process: Dynamic Delta Hedging with Vol. Surface Feedback"]
    
    Process --> Outcome["Key Findings: Outperforms Benchmarks & Handles Transaction Costs"]