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

Altmetrics Information
.

Other information
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
Year2023
Month7
Volume Number51
Issue Number4
PublisherElsevier
Pages408 - 413
ISSN0167-6377
eISSN1872-7468
LanguagesEnglish-United States
KeywordsHedging, Data-driven, Deep learning, Market sentiment, Index options

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