Firm-specific information and systemic risk
Adam Clements and
Y. Liao
Economic Modelling, 2020, vol. 90, issue C, 480-493
Abstract:
Although there is substantial literature linking news to the asset return volatility of a single asset, little attention has been paid to how news influences the relationships between firms. This paper addresses this issue by examining how firm-specific scheduled and unscheduled news arrivals influence the systemic risk of individual firms based on a sample of 47 US financial institutions. Whereas negative surprises from scheduled news announcements and a higher rate of unscheduled news both increase the systemic risk of a firm, positive news surprises decrease this systemic risk. In addition, negative scheduled news and a higher rate of unscheduled news across the sector increases the total connectedness or systemic risk across the sector as a whole. These effects are magnified when the market is already in distress. The results indicate that regulators should consider more than volatility and pay attention to the news flow when monitoring systemic risk.
Keywords: Information flow; Volatility connectedness; Network; Information uncertainty (search for similar items in EconPapers)
JEL-codes: C22 G00 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999319314300
Full text for ScienceDirect subscribers only
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:ecmode:v:90:y:2020:i:c:p:480-493
DOI: 10.1016/j.econmod.2019.11.031
Access Statistics for this article
Economic Modelling is currently edited by S. Hall and P. Pauly
More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().