Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model

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 state‐level stock returns in the United States (US), based on the weekly metrics from the corresponding broad economic conditions indexes (ECIs). In light of the importance of a common factor in explaining a large proportion of the total variability in the state‐level economic conditions, we first apply a dynamic factor model with stochastic volatility (DFM‐SV) to filter out the national factor from the local components of weekly state‐level ECIs. We find that both the local and national factors of the ECI generally tend to affect state‐level volatility negatively. Furthermore, the GARCH‐MIDAS model, supplemented by these predictors, surpasses the benchmark GARCH‐MIDAS model with realized volatility (GARCH‐MIDAS‐RV) in a majority of states. Interestingly, the local factor often assumes a more influential role overall, compared with the national factor. Moreover, when the stochastic volatilities associated with the local and national factors are integrated into the GARCH‐MIDAS model, they outperform the GARCH‐MIDAS‐RV in over 80% of the states. Our findings have important implications for investors and policymakers.