Practical Applications of Deep Reinforcement Learning for Option Replication and Hedging

Abstract

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? <b><i>Deep Reinforcement Learning for Option Replication and Hedging</i></b>, from the Fall 2020 issue of <b><i>The Journal of Financial Data Science</i></b>, 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. <b>TOPICS:</b>Big data/machine learning, options, risk management, simulations