Data-driven Hedging of Stock Index Options via Deep Learning

• URL: http://arxiv.org/abs/2111.03477v1
• Date: Fri, 5 Nov 2021 12:53:47 GMT
• Title: Data-driven Hedging of Stock Index Options via Deep Learning
• Authors: Jie Chen, Lingfei Li
• Abstract summary: 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 performs the best in the out-of-sample test.
• Score: 6.952039070065292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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