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...

全面介紹

Saved in:
書目詳細資料
Main Authors: Habibullah, Muzafar Shah, Saari, Mohd Yusof, Maji, Ibrahim Kabiru, Haji Din, Badariah, Mohd Saudi, Nur Surayya
格式: Article
語言:English
出版: Taylor & Francis 2024
在線閱讀: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
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結: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.