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Modelling volatility in job loss during the COVID-19 pandemic: the Malaysian case


Citation

Habibullah, Muzafar Shah and Saari, Mohd Yusof and Maji, Ibrahim Kabiru and Haji Din, Badariah and Mohd Saudi, Nur Surayya (2024) Modelling volatility in job loss during the COVID-19 pandemic: the Malaysian case. Cogent Economics and Finance, 12 (1). pp. 1-26. ISSN 2332-2039

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
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
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