A Review on Derivative Hedging using Reinforcement Learning

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. In particular, 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 paper, we provide a comprehensive review of the use of RL techniques in hedging derivatives. Besides highlighting the main streams of research, we also provide potential research directions on this exciting and emerging field