Comprehensive Econometric Modeling of Commodity and Cryptocurrency Impacts on the S&P 500 Index

This study proposes an empirical econometric approach that emphasizes the integration of diverse financial assets to enhance market trend prediction. While the regression method employed is based on the conventional Ordinary Least Squares (OLS) technique, the novelty of this work lies in the strategic combination of key global commodities oil, gold, silver (SLV), and natural gas (GAS) with Bitcoin (BTC), a major cryptocurrency rarely examined jointly with traditional assets. By incorporating these variables as predictors of the S&P 500 index (SP), the study delivers a more comprehensive perspective on cross-market interactions. The model is further evaluated through tests for serial autocorrelation, correlation structure, specification errors, and predictive accuracy. Empirical results reveal that oil, gold, and Bitcoin significantly boost the S&P 500 index, while silver negatively impacts it, reflecting its safe-haven role. Natural gas shows no significant effect. The model’s strong explanatory power (R² = 0.93) validates the Comprehensive Econometric Modeling for explaining index dynamics.