Forecasting Natural Gas Futures Price Volatility of the United States: National versus State-Level Climate Concern Indexes

The aim of this paper is to utilize the Generalized Autoregressive Conditional Heteroscedasticity-Mixed Data Sampling (GARCH-MIDAS) framework to predict the daily volatility of natural gas futures price returns in the United States (US), based on the monthly broad metrics of overall and state-level Climate Concern Indexes (CCIs). We find that both national and the principal component of state-level CCIs tend to positively affect the natural gas price volatility. Furthermore, the GARCH-MIDAS model, supplemented by these predictors, as well as four forecast combination approaches involving the state-level CCIs, surpasses the benchmark GARCH-MIDAS model with an index of Global Economic Conditions (GARCH-MIDAS-GECON). More importantly, the combination method involving least squares weighting outperforms both the benchmark and the GARCH-MIDAS model using the overall US CCI. This superior performance is evident not only statistically but also in terms of economic (utility) gains, thus highlighting the importance of using the information content of the heterogeneous nature of climate concerns across the US states in forecasting volatility of natural gas futures prices. Our findings have important implications for investors and policymakers.