Trading and non-trading period Internet information flow and intraday return volatility

Dehua Shen, Wei Zhang, Xiong Xiong, Xiao Li and Yongjie Zhang

Physica A: Statistical Mechanics and its Applications, 2016, vol. 451, issue C, 519-524

Abstract: This paper employs the news appeared in Baidu News as the proxy for Internet information flow, separates them into trading period and non-trading period information and provides alternative evidence for the Mixture of Distribution Hypothesis (MDH). The empirical results show that the contemporary information can effectively reduce the volatility persistence; meanwhile, the lead information and the aggregate information also show some explanatory power. Some future directions are pointed out in the concluding remarks.

Keywords: Internet information; Mixture of Distribution Hypothesis; Trading and non-trading period information; Volatility persistence; GARCH (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437116001503
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:451:y:2016:i:c:p:519-524

DOI: 10.1016/j.physa.2016.01.086

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().