Climate Risks and Predictability of Commodity Returns and Volatility: Evidence From Over 750 Years of Data

Abstract

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.