High-frequency news flow and states of asset volatility

Kin Yip Ho*, Yanlin Shi, Zhaoyong Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter examines the relationship between high-frequency news flow and the states of asset return volatility. To estimate asset return volatility and smoothing probability, we first apply the Markov Regime-Switching Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Second, the different states of asset return volatility are identified by comparing the previously generated smoothing probability with certain thresholds. Subsequently, we employ discrete choice models to investigate the impact of high-frequency news flow on the volatility states of hourly returns of the constituent stocks in the Dow Jones Composite Average (DJN 65). Our dataset for high-frequency news flows is constructed from the new RavenPack Dow Jones News Analytics database that captures >1200 types of firm-specific and macroeconomic news releases at high frequencies. Estimated results show that different types of news flows have varying significant effects on the likelihood of volatility states of intraday asset returns.

Original languageEnglish
Title of host publicationHandbook of high frequency trading
EditorsGreg N. Gregoriou
Place of PublicationLondon
Number of pages25
ISBN (Electronic)9780128023624
ISBN (Print)9780128022054
Publication statusPublished - 4 Feb 2015
Externally publishedYes


  • Discrete choice model
  • High-frequency volatility dynamics
  • Markov regime-switching GARCH
  • News flows


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