Title

High-Frequency News Flow and States of Asset Volatility

Document Type

Book Chapter

Publisher

Elsevier

Place of Publication

Amsterdam

Editor(s)

Gregoriou, G.N.

Faculty

Faculty of Business and Law

School

School of Business

RAS ID

20510

Comments

This chapter was originally published as: Ho, K., Shi, Y., & Zhang, Z. (2015) High-Frequency News Flow and States of Asset Volatility. In Gregoriou, G.N. (Ed.), The handbook of high frequency trading (pp359-383). Amsterdam: Elsevier. Original chapter available here

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.

DOI

10.1016/B978-0-12-802205-4.00021-X

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