Data-driven hedging of stock index options via deep learning
Publication in refereed journal

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AbstractWe develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging.
All Author(s) ListJie Chen, Lingfei Li
Journal nameOperations Research Letters
Volume Number51
Issue Number4
Pages408 - 413
LanguagesEnglish-United States
KeywordsHedging, Data-driven, Deep learning, Market sentiment, Index options

Last updated on 2024-12-01 at 12:45