A review on derivative hedging using reinforcement learning

• Main Author: LIU, Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Reinforcement learning; hedging; optimization; Categorical Data Analysis; Finance and Financial Management; Portfolio and Security Analysis
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7195; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8194/viewcontent/jfds.2023.1.124.full_pv.pdf
• Institution: Singapore Management University

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.