Applying Reinforcement Learning to Option Pricing and Hedging
This addresses the problem of model-free option pricing for financial practitioners, but it is incremental as it builds on existing reinforcement learning methods in finance.
This paper applies reinforcement learning to option pricing and hedging, specifically using the Q-Learning Black Scholes approach, and finds it accurately estimates prices under varying volatility, hedging frequency, and moneyness levels, with robust performance including proportional transaction costs.
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.