Data-driven Hedging of Stock Index Options via Deep Learning
This work addresses the challenge of improving hedging efficiency for stock index options traders by incorporating market sentiment, though it is incremental as it builds on existing data-driven methods.
The authors tackled the problem of hedging S&P500 index options by developing a deep learning model that directly learns hedge ratios from options data, which significantly outperformed standard Black-Scholes delta hedging and a recent data-driven model in out-of-sample tests.
We develop deep learning models to learn the hedge ratio for S&P500 index options directly from options data. We compare different combinations of features and show that a feedforward neural network model with time to maturity, Black-Scholes delta and a sentiment variable (VIX for calls and index return for puts) as input features performs the best in the out-of-sample test. This model significantly outperforms the standard hedging practice that uses the Black-Scholes delta and a recent data-driven model. Our results demonstrate the importance of market sentiment for hedging efficiency, a factor previously ignored in developing hedging strategies.