COVID-19: tail risk and predictive regressions.
File(s)
Published version
Author(s)
Distaso, Walter
Ibragimov, Rustam
Semenov, Alexander
Skrobotov, Anton
Type
Journal Article
Abstract
The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.
Date Issued
2022-12-01
Date Acceptance
2022-09-16
Citation
PLoS One, 2022, 17 (12), pp.1-13
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
13
Journal / Book Title
PLoS One
Volume
17
Issue
12
Copyright Statement
Copyright: © 2022 Distaso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0275516
Publication Status
Published
Date Publish Online
2022-12-01