Detecting speculative bubbles in metal prices: Evidence from GSADF test and machine learning approaches

Abstract

The importance of metal prices to real economic activity and financial markets has increased the focus on detecting price bubbles in precious and industrial metals. Several studies looked at the influence of macroeconomic factors in the formation of a single metal bubble and tried to identify bubble dates. Our study extends the literature and analyzes monthly gold, platinum, palladium, rhodium, silver, and aluminum, copper, lead, nickel, steel, tin prices over 1980M1-2019M12, and contributes to the literature in two ways: First, the analysis incorporates the Generalized Supremum Augmented Dickey-Fuller (GSADF) test to detect potential bubbles. Second, the study evaluates the impact of potential financial, real, and speculative factors in the likelihood of price bubbles using the random forest method. Our findings indicate that financial factors are more critical in predicting precious metal price bubbles. The monetary policy rate and the production index are important to predict bubbles in industrial metal prices. However, our findings suggest that speculative activity may not adequately predict metal price bubbles.