 Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemicJorge Caiado and Francisco Lúcio The North American Journal of Economics and Finance, 2023, vol. 68, issue C Abstract: We propose a new clustering approach for comparing financial time series and employ it to study how the COVID-19 pandemic affected the U.S. stock market. Essentially, we compute the forecast accuracy of asymmetric GARCH models applied to S&P500 industries and use the model forecast errors for different horizons and cut-off points to calculate a distance matrix for the stock indices. Hierarchical clustering algorithms are used to assign the set of industries into clusters. We found homogeneous clusters of industries in terms of the impact of COVID-19 on US stock market volatility. The industries most affected by the pandemic and with less accurate stock market prediction (Hotels, Airline, Apparel, Accessories & Luxury Goods, and Automobile) are separated in Euclidean distance from those industries that were less impacted by COVID-19 and which had more accurate forecasting (Pharmaceuticals, Internet & Direct Marketing Retail, Data Processing, and Movies & Entertainment). Keywords: Cluster analysis; COVID-19; Forecast accuracy; Threshold GARCH model; S&P500; Unsupervised machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823000943 DOI: 10.1016/j.najef.2023.101971 Access Statistics for this article The North American Journal of Economics and Finance is currently edited by Hamid Beladi More articles in The North American Journal of Economics and Finance from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.