Option Pricing Model

Option pricing models aim to determine the fair value of financial derivatives, primarily options, by considering factors like underlying asset price, time to expiration, and volatility. Current research heavily emphasizes the use of artificial neural networks, including deep learning architectures and multi-task learning approaches, to improve pricing accuracy and efficiency, particularly for complex options like American and Bermudan options, and in situations with limited data. These advancements address limitations of traditional models like Black-Scholes, which often fail to capture market complexities such as jumps and stochastic volatility, leading to more robust and accurate pricing in various financial markets. The resulting improvements have significant implications for risk management, portfolio optimization, and algorithmic trading strategies.