The Role of Energy Market Uncertainties in Forecasting US State-Level Stock Market Volatility: A GARCH-MIDAS Approach

Abstract

Abstract In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States based on monthly metrics of oil price uncertainty (OPU) and the broader energy uncertainty index (EUI). This approach addresses the previous literature’s limitations of narrowly focusing on crude oil prices and restricted geographic coverage by offering a more comprehensive analysis of energy uncertainty’s predictability for stock market volatility across all 50 U.S. states. We find that over the daily period of (February) 1994 to (September) 2022 and various forecast horizons, in 37 out of the 50 states, the GARCH-MIDAS model with EUI outperforms the benchmark, i.e., the GARCH-MIDAS-realized volatility (RV), which, in turn, holds for at most 18 cases under OPU. This evidence is further strengthened with the detection of higher utility gains delivered for 42 states by the GARCH-MIDAS-EUI in comparison to the GARCH-MIDAS-RV. Policymakers can utilize EUI-driven high-frequency forecasts to predict state-level economic activity, enabling timely interventions to mitigate regional recessions. For investors, incorporating broader energy market uncertainty into strategies would improve risk management, portfolio allocation, and hedging decisions, potentially enhancing risk-adjusted returns.