Deep Reinforcement Learning for Option Replication and Hedging

Jiayi Du, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang and Bofei Zhang, 'Deep Reinforcement Learning for Option Replication and Hedging.' The Journal of Financial Data Science 2.4 (2020).

Posted: 21 Oct 2020

See all articles by Jiayi Du

Jiayi Du

New York University (NYU) - Center for Data Science

Muyang Jin

New York University (NYU) - Center for Data Science

Petter N. Kolm

New York University (NYU) - Courant Institute of Mathematical Sciences

Gordon Ritter

New York University (NYU) - Courant Institute of Mathematical Sciences; City University of New York (CUNY) - Weissman School of Arts and Sciences; Columbia University - Department of Mathematics; Cornell University - Cornell Tech NYC; University of Chicago - Department of Mathematics

Yixuan Wang

New York University (NYU) - Center for Data Science

Bofei Zhang

New York University (NYU) - Center for Data Science

Date Written: August 19, 2020

Abstract

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 training 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 P&L, training time and amount of data needed for training.

Keywords: Financial Machine Learning, Hedging, Deep Q-learning, Deep Reinforcement Learning, Deep Neural Networks, Option Replication, Proximal Policy Optimization

JEL Classification: G11, C61

Suggested Citation

Du, Jiayi and Jin, Muyang and Kolm, Petter N. and Ritter, Gordon and Wang, Yixuan and Zhang, Bofei, Deep Reinforcement Learning for Option Replication and Hedging (August 19, 2020). Jiayi Du, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang and Bofei Zhang, 'Deep Reinforcement Learning for Option Replication and Hedging.' The Journal of Financial Data Science 2.4 (2020). , Available at SSRN: https://ssrn.com/abstract=3677201

Jiayi Du

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Muyang Jin

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Petter N. Kolm (Contact Author)

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY 10012
United States

Gordon Ritter

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

New York University
251 Mercer Street
New York, NY 10012
United States

City University of New York (CUNY) - Weissman School of Arts and Sciences ( email )

One Bernard Baruch Way
New York, NY 10010
United States

Columbia University - Department of Mathematics ( email )

New York, NY
United States

Cornell University - Cornell Tech NYC ( email )

111 8th Avenue #302
New York, NY 10011
United States

University of Chicago - Department of Mathematics ( email )

5734 S. University
Chicago, IL 60637
United States

Yixuan Wang

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

Bofei Zhang

New York University (NYU) - Center for Data Science ( email )

726 Broadway
7th Floor
New York, NY 10003
United States

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