Citation
Abstract
This study employs a suitable volatility model that examines the impact of COVID-19 new cases and deaths on the volatility of daily job loss in Malaysia. Autoregressive Distributed Lag (ARDL) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) were employed as the modelling strategy to estimate daily data from January to December 2020. In addition, the asymmetric GARCH-M (EGARCH-M, TGARCH-M, and PGARCH-M) were further applied. The findings from different versions of the ARDL(p,q1,q2)-(E,T,P)GARCH(1,1)-M model show that the ARDL-EGARCH-M model can capture the volatility and clustering of variability in job loss. The findings revealed asymmetry effects, suggesting that negative shocks (bad news) in a pandemic period increased volatility in job loss compared to positive shocks (good news). Policy implications relating to lockdown measures and news signals were provided.
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Additional Metadata
Item Type: | Article |
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Divisions: | Putra Business School School of Business and Economics |
DOI Number: | https://doi.org/10.1080/23322039.2023.2291886 |
Publisher: | Taylor & Francis |
Keywords: | Covid-19; Job loss; ARDL; GARCH-M; Malaysia |
Depositing User: | Mohamad Jefri Mohamed Fauzi |
Date Deposited: | 20 Aug 2024 06:40 |
Last Modified: | 20 Aug 2024 06:40 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/23322039.2023.2291886 |
URI: | http://psasir.upm.edu.my/id/eprint/105756 |
Statistic Details: | View Download Statistic |
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