RT Journal Article
SR Electronic
T1 Deep Reinforcement Learning for Option Replication and Hedging
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 44
OP 57
DO 10.3905/jfds.2020.1.045
VO 2
IS 4
A1 Jiayi Du
A1 Muyang Jin
A1 Petter N. Kolm
A1 Gordon Ritter
A1 Yixuan Wang
A1 Bofei Zhang
YR 2020
UL https://pm-research.com/content/2/4/44.abstract
AB The authors propose models for the solution of the fundamental problem of option replication subject to discrete trading, round lotting, and nonlinear transaction costs using state-of-the-art methods in deep reinforcement learning (DRL), including deep Q-learning, deep Q-learning with Pop-Art, and proximal policy optimization (PPO). Each DRL model is trained to hedge a whole range of strikes, and no retraining is needed when the user changes to another strike within the range. The models are general, allowing the user to plug in any option pricing and simulation library and then train them with no further modifications to hedge arbitrary option portfolios. Through a series of simulations, the authors show that the DRL models learn similar or better strategies as compared to delta hedging. Out of all models, PPO performs the best in terms of profit and loss, training time, and amount of data needed for training.TOPICS: Big data/machine learning, options, risk management, simulationsKey Findings• The authors propose models for the replication of options over a whole range of strikes subject to discrete trading, round lotting, and nonlinear transaction costs based on state-of-the-art methods in deep reinforcement learning including deep Q-learning and proximal policy optimization.• The models allow the user to plug in any option pricing and simulation library and then train them with no further modifications to hedge arbitrary option portfolios.• A series of simulations demonstrates that the deep reinforcement learning models learn similar or better strategies as compared to delta hedging.• Proximal policy optimization outperforms the other models in terms of profit and loss, training time, and amount of data needed for training.