The impacts of COVID-19 on the dependence structure of the stock market

Jong-Min Kim and Hojin Jung

Applied Economics Letters, 2023, vol. 30, issue 4, 510-515

Abstract: This article uses Gaussian copula marginal regression and tail dependence estimation by copula to explore COVID-19’s effects on the dependence structure of the US stock market. Specifically, we investigate the dependence between S&P 500 returns and returns in eleven sectors at the mean and the tails of the joint distribution prior to and during the pandemic. We uncover strong evidence of the pandemic’s heterogeneous effects on dependence structures across sectors. Certain sectors, including information technology and health care, increase in importance as return determinants of the composite index during the pandemic. We also find that COVID-19 increases tail dependence, specifically lower tail dependence more than upper tail dependence. These findings will be useful to investors interested in managing risk, particularly during pandemics.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2021.1996526 (text/html)
Access to full text is restricted to subscribers.

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:taf:apeclt:v:30:y:2023:i:4:p:510-515

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2021.1996526

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().