Enhancing Black-Scholes Delta Hedging via Deep Learning

TL;DR

This paper introduces a deep learning-based delta hedging method that learns residuals to improve option hedging accuracy, outperforming traditional approaches and reducing data requirements.

Contribution

It presents a novel residual learning framework for delta hedging using neural networks, significantly enhancing performance and data efficiency over direct learning methods.

Findings

Residual learning improves hedging performance by over 100%.

Adding market sentiment features benefits puts more than calls.

Three years of data suffice to match ten-year performance.

Abstract

This paper proposes a deep delta hedging framework for options, utilizing neural networks to learn the residuals between the hedging function and the implied Black-Scholes delta. This approach leverages the smoother properties of these residuals, enhancing deep learning performance. Utilizing ten years of daily S&P 500 index option data, our empirical analysis demonstrates that learning the residuals, using the mean squared one-step hedging error as the loss function, significantly improves hedging performance over directly learning the hedging function, often by more than 100%. Adding input features when learning the residuals enhances hedging performance more for puts than calls, with market sentiment being less crucial. Furthermore, learning the residuals with three years of data matches the hedging performance of directly learning with ten years of data, proving that our method demands less data.