Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGRACH Mode

We investigate the ability of textual analysis-based metrics of physical or transition risks associated with climate change in forecasting the daily volume of trade contracts of gold. Given the count-valued nature of gold volume data, our econometric framework is a loglinear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) model with a particular climate change-related covariate. We detect a significant predictive power for gold volume at 5- and 22-day-ahead horizons when we extend our model using physical risks. Given the underlying positively evolving impact of such risks on the trading volume of gold, as derived from a full-sample analysis using a time-varying INGARCH model, we can say that gold acts as a hedge against physical risks at 1-week and 1-month horizons. Such a characteristic is also detected for platinum, and to a lesser extent, for palladium, but not silver. Our results have important investment implications.