Exploring the Techniques for Option Pricing: An Empirical Study and Performance Assessment

The accurate pricing of financial options is crucial for effective risk management and decision-making in financial markets. Traditional option pricing models, such as the Black-Scholes model, rely on simplifying assumptions that may not fully capture the complexities of real-world market dynamics. This study aims at exploring machine learning (ML) and deep learning (DL) models in different scenarios and evaluating their performances against the conventional methods. The methodology harnesses the power of neural networks within deep learning models to discern and learn intricate patterns and relationships inherent in historical market data. By training on a diverse dataset comprising stock prices, strike prices, time to expiration, and volatility, DL models aim to capture the nuanced interactions among these variables, thus enabling more accurate and robust option pricing compared to traditional methods. Moreover, the analysis is extended to incorporate ML models, which complement DL techniques by leveraging a variety of algorithms to discern patterns and make predictions based on historical data. Through the evaluation and comparison of both DL and ML models, we aim to ascertain the efficacy and suitability of each approach for option pricing tasks. Among the models evaluated, the Multi-Layer Perceptron (MLP) demonstrated superior performance in option pricing tasks, achieving high accuracy and robustness across synthetic and real-world datasets. Additionally, ensemble learning techniques such as XGBoost and Gradient Boosting Tree Regression (GBTR) exhibited strong predictive capabilities, offering valuable insights into option pricing dynamics with R2 values of 0.9694 and 0.9474 in S&P 500 dataset respectively. Overall, the comparative analysis highlights the efficacy of machine learning and deep learning models in option pricing, with MLP emerging as a standout performer.