Learning sequential option hedging models from market data

Ke Nian, Thomas F Coleman and Yuying Li

Journal of Banking & Finance, 2021, vol. 133, issue C

Abstract: Following a direct data-driven approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU), GRUδ, for optimal discrete option hedging. The proposed GRUδ utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data, we demonstrate that the weekly and monthly hedging performance of the proposed GRUδ significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed GRUδ also surpasses that of MV methods based on parametric models, the kernel method, and SABR-Bartlett method.

Keywords: Option; Discrete hedging; Data-Driven model; Feature selection; Feature extraction; Machine learning; Recurrent neural network (search for similar items in EconPapers)
JEL-codes: C81 G13 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:133:y:2021:i:c:s0378426621002338

DOI: 10.1016/j.jbankfin.2021.106277

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