Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information

2024-07-30

arXiv.org

AI Summary

This paper addresses the suboptimal performance of dynamic option hedging under transaction costs. To tackle this, we propose a deep reinforcement learning (DRL) hedging framework that explicitly incorporates forward-looking dynamics of the implied volatility surface (IVS). Methodologically, we design a hybrid neural network architecture to encode temporal IVS features and employ a proximal policy optimization (PPO) variant for end-to-end policy learning. The framework is rigorously validated via joint Monte Carlo simulation and historical backtesting. Our key contribution lies in being the first to explicitly integrate IVS forward dynamics into the deep hedging decision process—enhancing both model stability and out-of-sample generalization. Empirical evaluation on S&P 500 options demonstrates that our strategy reduces hedging error by 18.7% and tail risk by 23% relative to conventional delta hedging and smile-calibrated delta hedging, while maintaining robustness and practical implementability.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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, with a novel hybrid neural network architecture improving the training performance. 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.

Problem

• Enhance deep hedging using implied volatility surface dynamics
• Optimize hedging strategy via deep reinforcement learning
• Outperform traditional benchmarks under transaction costs

Innovation

• Dynamic hedging with implied volatility surface
• Deep policy gradient reinforcement learning
• Outperforms conventional delta hedging benchmarks