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

Afees Salisu, Wenting Liao (), Rangan Gupta and Oguzhan Cepni
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Wenting Liao: School of Finance, Renmin University of China, Beijing, People's Republic of China

No 202323, Working Papers from University of Pretoria, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C32 C53 E32 E66 G10 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2023-08
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202323

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