Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/321385 
Year of Publication: 
2024
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 12 [Issue:] 1 [Article No.:] 2291886 [Year:] 2024 [Pages:] 1-26
Publisher: 
Taylor & Francis, Abingdon
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.
Subjects: 
Covid-19
job loss
ARDL
GARCH-M
Malaysia
JEL: 
E20
I10
J64
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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