Housing market variables and predictability of state-level stock market volatility of the United States : fundamentals versus sentiments in a mixed-frequency framework

Abstract

This paper utilizes the generalized autoregressive conditional heteroscedasticity–mixed data sampling (GARCH‑MIDAS) approach to predict the daily volatility of state‑level stock returns in the United States (US) from monthly state and national housing price returns. We find that housing price returns generally have a negative effect on state‑level volatility. More importantly, the GARCH‑MIDAS model augmented with these predictors significantly outperforms the benchmark GARCH‑MIDAS model with realized volatility (GARCH‑MIDAS‑RV) over short‑, medium‑, and long‑term forecasting horizons for 90 % of the states; the performance of state and national housing returns is virtually indistinguishable. These superior forecasting results persist when housing price returns are replaced with housing permits and housing‑market media‑attention indexes, suggesting an overwhelming role for housing‑market variables—both traditional and behavioral—in forecasting state‑level stock‑return volatility. Our findings have important implications for investors and policymakers.

HIGHLIGHTS • Housing price returns predict state-level stock volatility in the US. • GARCH-MIDAS-HPR outperforms benchmark models across most states. • Predictive gains hold for short, medium, and long forecasting horizons. • Housing permits and media indexes also forecast volatility effectively. • Findings inform investor strategies and guide state-level policy action.