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

Afees A. Salisu, Wenting Liao, Rangan Gupta, Oguzhan Cepni

Research output: Working paperResearch

Abstract

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 to 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 percent of the states. Our findings have important implications for investors and policymakers.
Original languageEnglish
Place of PublicationPretoria
PublisherUniversity of Pretoria
Number of pages23
Publication statusPublished - Aug 2023
SeriesWorking Paper Series / Department of Economics. University of Pretoria
Number2023-23

Keywords

  • Weekly economic conditions index
  • DFM-SV
  • Local and national factors
  • Daily state-level stock returns volatility
  • GARCH-MIDAS
  • Predictions

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