Modelling volatility in job loss during the COVID-19 pandemic: the Malaysian case
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 strateg...
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| 主要な著者: | , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Taylor & Francis
2024
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/105756/1/Modelling%20volatility%20in%20job%20loss%20during%20the%20COVID-19%20pandemic%20%20The%20Malaysian%20case.pdf http://psasir.upm.edu.my/id/eprint/105756/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181872386&doi=10.1080%2f23322039.2023.2291886&partnerID=40&md5=680052130e55f454fec9132f965f6788 |
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| 要約: | 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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