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
Citation
LIU, Peng.
A review on derivative hedging using reinforcement learning. (2023). Journal of Financial Data Science. 1-10.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7195
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3905/jfds.2023.1.124
Included in
Categorical Data Analysis Commons, Finance and Financial Management Commons, Portfolio and Security Analysis Commons