Abstract
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 language | English |
|---|---|
| Title of host publication | Handbook of High Frequency Trading |
| Publisher | Elsevier Inc. |
| Pages | 359-383 |
| Number of pages | 25 |
| ISBN (Electronic) | 9780128023624 |
| ISBN (Print) | 9780128022054 |
| DOIs | |
| Publication status | Published - 4 Feb 2015 |