Business applications and state-level stock market realized volatility: A forecasting experiment

Abstract

We analyze the predictive value of (the surprise component of) state-level business applications, as a proxy of local investor sentiment, for the state-level realized US stock-market volatility. We use high-frequency data for the period from September 2011 to October 2021 to compute realized volatility. Using an extended version of the popular heterogeneous autoregressive realized volatility model and accounting for the possibility that users of forecasts have an asymmetric loss function, we show that business applications tend to have predictive value for realized state-level stock-market volatility, as well as for upside (“good”) and downside (“bad”) realized volatility, for users of forecasts who suffer a larger loss from an underprediction of realized volatility than from an overprediction of the same (absolute) seize, after controlling for realized moments (realized skewness, realized kurtosis, realized jumps, and realized tail risks). We also highlight that the COVID-19 period is a major driver of our empirical results.