Data-driven Hedging of Stock Index Options via Deep Learning

2021/11/05 by Jie Chen, Chen, Jie, Lingfei Li +1
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Statistical Finance (q-fin.ST) #q-fin.ST #stat.ML

paper · pdf · doi:10.48550/arxiv.2111.03477

arxiv created 2021/11/05 · arxiv updated 2021/11/08

Abstract

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.

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