Risk-Adjusted Pricing Models in Commodities Markets Using AI and Econometric Techniques

The proper pricing of commodities during market uncertainty and structural incompleteness is a primary challenge in financial economics. Although traditional econometric models are interpretable and theoretically rigorous, they tend to fail to capture non-linear dynamics and adjust to regime changes. On the other hand, there is a predictive power of artificial intelligence (AI) methods, which are limited in their interpretability and the ability to combine economic theory. The review focuses on the intersection of AI and econometric models to develop risk-adjusted commodity market pricing models. It provides the development of pricing models, a hybrid theoretical framework, and an evaluation of recent literature on their application. Areas with major gaps, such as the absence of standardization, data quality concerns, and real-time adaptability, are also identified. The paper also concludes with research suggestions to enhance accuracy, transparency, and applicability in various market environments.