Economic drivers of commodity volatility: The case of copper. (October 2021) - Díaz, Juan D.; Hansen, Erwin; Cabrera, Gabriel
Abstract: This paper examines whether economic variables provide useful information with which to forecast monthly copper price volatility. Prior literature regarding equity markets has discussed this question extensively, but less is known about these variables' predictive power in the context of commodity markets. We focus on copper, which is a non-renewable commodity that plays a significant role in several economies and markets. To shed new light on this topic, we employ a Bayesian Model Averaging (BMA) approach to account for both parameter and model uncertainty. Our empirical results show that several economic variables have significant forecasting power when compared against an autoregressive benchmark model, and that predictability varies across the business cycle. Highlights: We forecast monthly copper price volatility. We assess predictors relating to the economic fundamentals of copper, uncertainty variables, and financial variables. We use a Bayesian Model Averaging approach to account for model uncertainty. We show that several economic variables have significant forecasting power. Our analysis reveals that predictability varies across the business cycle.