How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches

Document Type

Journal Article




Faculty of Business and Law


School of Business/Finance, Economics, Markets and Accounting Research Centre




This article was originally published as: Kin-Yip, H., Yanlin, S., & Zhang, Z. (2013). How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches. The North American Journal of Economics and Finance, 26(1), 436-456. Original article available here


This paper examines the dynamic relationship between firm-level return volatility and public news sentiment. By using the new RavenPack News Analytics - Dow Jones Edition database that captures over 1200 types of firm-specific and macroeconomic news releases and their sentiment scores at high frequencies, we investigate the circumstances in which public news sentiment is related to the intraday volatility of the constituent stocks in the Dow Jones Composite Average (DJN 65). Two different conditionally heteroskedastic models are employed: the Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedastic (FIGARCH) and the two-state Markov Regime-Switching GARCH (RS-GARCH) models. For most of the DJN 65 stocks, our results confirm the significant impact of firm-specific news sentiment on intraday volatility persistence, even after controlling for the potential effects of macroeconomic news. Compared with macroeconomic news sentiment, firm-specific news sentiment apparently accounts for a greater proportion of overall volatility persistence. Moreover, negative news has a greater impact on volatility than positive news. Furthermore, the results from the RS-GARCH model indicate that news sentiment accounts for a greater proportion of volatility persistence in the high-volatility regime (turbulent state) than in the low-volatility regime (calm state). In-sample forecasting performance and residual diagnostic tests suggest that FIGARCH generally outperforms RS-GARCH.