Late to the Party or Insider Trading? Exploring Channels for the Rise of Volatility Before News

Klaus Ackermann (), Ashley Andrews, Bonsoo Koo and  Wei  Wei

No 21/24, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We introduce the tardy news hypothesis as a new channel that complements information leakage to explain why volatility rises before public news. Through a novel information processing framework, we show that both channels can lead to bias and inconsistency in the estimated relationship between news and volatility, and we establish bounds for the true relationship. Using a dataset that combines realized volatility withRavenPack news analytics for S&P 500 constituents, we find that the type of events anews item covers is related to the likelihood of a rise in pre-news volatility, and the sizeof the firm is related to the magnitude of the rise. This heterogeneity from event andfirm types is consistent with the tardy news hypothesis.

Keywords: Information Leakage; Tardy News; Public News; Realized Volatility (search for similar items in EconPapers)
JEL-codes: C51 G14 (search for similar items in EconPapers)
Pages: Â 50
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.monash.edu/business/ebs/research/publications/ebs/2024/WP21-2024.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2024-21

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().