News sentiment and states of stock return volatility: evidence from long memory and discrete choice models

This paper examines the relationship between the states of firm-level return volatility and public news sentiment. To incorporate structural breaks into modelling the long memory property of stock return volatility, we develop FIGARCH models that allow the constant in the conditional variance to vary with time. Following this train of thought, we firstly propose a Markov Regime-Switching FIGARCH (MRS-FIGARCH) model that allows the constant term to switch between low and high volatility states. This model is shown to outperform the Adaptive FIGARCH and Time-Varying FIGARCH models with respect to model comparison criteria. It is subsequently used to estimate the smoothing probability and the conditional variance. Second, states of firm-level return volatility are identified by comparing the previously generated smoothing probability with certain thresholds. Then, we employ discrete choice models to investigate the impact of public news sentiment on the volatility states of hourly returns of constituent stocks in the Dow Jones Composite Average (DJN 65). Our news dataset is constructed from the new RavenPack Dow Jones News Analytics database that captures over 1200 types of firm-specific and macroeconomic news releases and their sentiment scores at high frequencies. Estimated results show that news sentiment has various significant effects on the likelihood of volatility states of intraday stock return.