Year: 2025
Type: article
Abstract: The global derivatives market is experiencing significant growth, and precise pricing of derivatives is essential for optimizing their financial utilization. However, conventional derivative pricing methodologies, such as the Black-Scholes option pricing model, are predicated on strict assumptions, rendering them challenging to apply accurately in practice. In light of advancements in financial technology, the application of machine learning techniques for derivative pricing has emerged as a prominent area of scholarly inquiry. This article seeks to conduct a comprehensive literature review of machine learning-based derivative pricing methods, aiming to assess the current state of academic research in this domain and to elucidate how existing literature employs machine learning approaches for derivative pricing. The paper will initially concentrate on the methodologies associated with machine learning in derivative pricing, utilizing specific studies as illustrative examples of practical applications. Subsequently, it will compile instances where machine learning techniques have been employed for hedging and risk management in relation to derivatives. Finally, the paper will provide a synthesis of findings and offer insights into the future trajectory of machine learning applications in derivative pricing. The machine learning pricing methodology, which eschews reliance on traditional models in favor of extensive historical data analysis, holds the potential for more accurate pricing of derivative products and represents a promising direction for future development.
Author: Houde Liang
Institution: Guangdong University of Technology
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409619916