@techreport{defa5659c2714e12932e0d6bac658023,
title = "Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH-MIDAS Model ",
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.",
keywords = "Weekly economic conditions index, DFM-SV, Local and national factors, Daily state-level stock returns volatility, GARCH-MIDAS, Predictions, Weekly economic conditions index, DFM-SV, Local and national factors, Daily state-level stock returns volatility, GARCH-MIDAS, Predictions",
author = "Salisu, {Afees A.} and Wenting Liao and Rangan Gupta and Oguzhan Cepni",
year = "2023",
month = aug,
language = "English",
series = "Working Paper Series / Department of Economics. University of Pretoria ",
publisher = "University of Pretoria",
number = "2023-23",
address = "South Africa",
type = "WorkingPaper",
institution = "University of Pretoria",
}