Pricing cryptocurrency options with machine learning regression for handling market volatility

Abstract

Pricing cryptocurrency options, crucial for risk management and market stabilization, presents unique challenges due to specific underlying dynamics like the inversion of the leverage effect. Classical option pricing models like Black–Scholes and Heston struggle to address these dynamics due to their set of assumptions. This study introduces machine learning models for options pricing, specifically regression-tree methods. A data-driven machine learning model can incorporate high-frequency volatility estimators into the input set to enhance pricing accuracy. By integrating these estimators, machine learning models can capture the complex dynamics of cryptocurrency markets more effectively than classical pricing approaches. The comparative analysis reveals that equity options are easier to price, clearly indicating inefficiencies in the cryptocurrency option market, which confirms the challenges in achieving accurate pricing. Our results highlight the effectiveness of machine learning models in adapting to the unique characteristics of emerging asset classes, suggesting a shift towards more data-oriented pricing methodologies