Deep learning algorithms for hedging with frictions

Xiaofei Shi (), Daran Xu () and Zhanhao Zhang ()
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Xiaofei Shi: University of Toronto
Daran Xu: Columbia University
Zhanhao Zhang: Columbia University

Digital Finance, 2023, vol. 5, issue 1, No 6, 113-147

Abstract: Abstract This work studies the deep learning-based numerical algorithms for optimal hedging problems in markets with general convex transaction costs. Our main focus is on how these algorithms scale with the length of the trading time horizon. Based on the comparison results of the FBSDE solver by Han, Jentzen, and E (2018) and the Deep Hedging algorithm by Buehler, Gonon, Teichmann, and Wood (2019), we propose a Stable-Transfer Hedging (ST-Hedging) algorithm, to aggregate the convenience of the leading-order approximation formulas and the accuracy of the deep learning-based algorithms. Our ST-Hedging algorithm achieves the same state-of-the-art performance in short and moderately long time horizon as FBSDE solver and Deep Hedging, and generalize well to long time horizon when previous algorithms become suboptimal. With the transfer learning technique, ST-Hedging drastically reduce the training time, and shows great scalability to high-dimensional settings. This opens up new possibilities in model-based deep learning algorithms in economics, finance, and operational research, which takes advantage of the domain expert knowledge and the accuracy of the learning-based methods.

Keywords: Portfolio optimization; Transaction costs; Deep learning; Transfer learning (search for similar items in EconPapers)
JEL-codes: C63 G11 G12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s42521-023-00075-z

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