Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2023

Abstract

Hedging is a common trading activity to manage the risk of engaging in transactions that involve derivatives such as options. Perfect and timely hedging, however, is an impossible task in the real market that characterizes discrete-time transactions with costs. Recent years have witnessed reinforcement learning (RL) in formulating optimal hedging strategies. Specifically, different RL algorithms have been applied to learn the optimal offsetting position based on market conditions, offering an automatic risk management solution that proposes optimal hedging strategies while catering to both market dynamics and restrictions. In this article, the author provides a comprehensive review of the use of RL techniques in hedging derivatives. In addition to highlighting the main streams of research, the author provides potential research directions on this exciting and emerging field.

Keywords

Reinforcement learning, hedging, optimization

Discipline

Categorical Data Analysis | Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Quantitative Finance

Publication

Journal of Financial Data Science

First Page

1

Last Page

10

ISSN

2640-3943

Identifier

10.3905/jfds.2023.1.124

Publisher

Portfolio Management Research

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.3905/jfds.2023.1.124

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