PT  - JOURNAL ARTICLE
AU  - Jiayi (Nicole) Du
AU  - Muyang Jin
AU  - Petter N. Kolm
AU  - Gordon Ritter
AU  - Yixuan Wang
AU  - Bofei Zhang
TI  - Practical Applications of Deep Reinforcement Learning for Option Replication and Hedging
AID  - 10.3905/pa.9.1.436
DP  - 2021 Jul 31
TA  - Practical Applications
PG  - 1--8
VI  - 9
IP  - 1
4099  - https://pm-research.com/content/9/1/1.10.short
4100  - https://pm-research.com/content/9/1/1.10.full
AB  - If a computer model using machine learning and artificial intelligence like Google’s DeepMind can beat the world’s best human player of the ancient Chinese game of “Go,” can a similar approach help solve the challenge of hedging and replicating option portfolios? Deep Reinforcement Learning for Option Replication and Hedging, from the Fall 2020 issue of The Journal of Financial Data Science, takes a “deep” dive into reinforcement learning approaches for option portfolios. It explores ways of training a computer “agent” by a clever form of trial and error to learn to replicate and hedge an option portfolio directly from data. It’s a positive step forward in the use of modern mathematical approaches to portfolio hedging, and the authors are extending their research in deep reinforcement learning (DRL) to real-world problems in finance, including trading, portfolio management, and hedging.TOPICS: Big data/machine learning, options, risk management, simulations