Applying Reinforcement Learning to Option Pricing and Hedging

TL;DR

This paper reviews reinforcement learning methods, especially the Q-Learning Black Scholes approach, for model-free option pricing and hedging, demonstrating robust performance across various market conditions and incorporating transaction costs.

Contribution

It introduces a reinforcement learning framework for option pricing and hedging that is data-driven, model-free, and adaptable to different market scenarios, extending traditional models.

Findings

Accurate estimation of option prices under varying volatility and hedging frequency.

Robust performance across different moneyness levels.

Incorporation of transaction costs affecting profit and loss.

Abstract

This thesis provides an overview of the recent advances in reinforcement learning in pricing and hedging financial instruments, with a primary focus on a detailed explanation of the Q-Learning Black Scholes approach, introduced by Halperin (2017). This reinforcement learning approach bridges the traditional Black and Scholes (1973) model with novel artificial intelligence algorithms, enabling option pricing and hedging in a completely model-free and data-driven way. This paper also explores the algorithm's performance under different state variables and scenarios for a European put option. The results reveal that the model is an accurate estimator under different levels of volatility and hedging frequency. Moreover, this method exhibits robust performance across various levels of option's moneyness. Lastly, the algorithm incorporates proportional transaction costs, indicating diverse impacts on profit and loss, affected by different statistical properties of the state variables.