Research Progress in Option Pricing Methods

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Abstract
In recent years, option pricing has garnered significant attention as a critical topic in financial engineering. Classical models, such as Black-Scholes, exhibit limitations in accurately capturing the dynamic changes in complex market environments. Consequently, machine learning techniques and neural network methods have emerged as pivotal research directions in option pricing. This paper provides a systematic review of the advances in option pricing based on optimization methods, machine learning techniques, and neural network models. Optimization methods, incorporating techniques like genetic algorithms and particle swarm optimization, significantly enhance the adaptability and accuracy of classical models. Machine learning methods, such as support vector machines and decision trees, demonstrate their advantages in nonlinear data modeling. Neural networks and deep learning models, with their robust nonlinear fitting capabilities, have become central tools in pricing complex financial products. Additionally, this review summarizes the application of hybrid models and evaluates the strengths and weaknesses of various approaches in practical applications. Finally, the paper discusses future research directions, including the integration of reinforcement learning techniques, improvement in model interpretability, and deep fusion of heterogeneous multi-source data. This work aims to provide theoretical references and technical support for further research in the field of option pricing.

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