Pandemic and Indonesia Stock Market Performance
DOI:
https://doi.org/10.52728/ijjm.v2i4.263Keywords:
Pandemic, Stringency Index, EGARCH, Return VolatilityAbstract
This study aims to analyze the impact of the COVID-19 pandemic on Indonesia’s stock market performance. Considering the characteristics of daily stock return data that shows the characteristics of volatility clustering, the analytical method used is to develop a heteroscedastic model specification whose parameters are estimated using the maximum likelihood method. Based on data from March 2020 to January 2021, this study finds that the Exponential-GARCH asymmetric model is the best model compared to the Standard-GARCH symmetric model or the asymmetric Threshold-GARCH model. The inference analysis conducted on the Exponential-GARCH asymmetric model in this study shows that the stock market's performance that is significantly affected by this pandemic is the volatility of its returns. Stock price volatility is one of the important variables in stock market performance. This study produces empirical findings that government policies on social restrictions contribute significantly to suppressing stock market volatility. As for government policies in mitigating the risk of the spread of the epidemic, in this study, it is measured through a stringency index. This index was released by the Oxford COVID-19 Government Response Tracker (OxCGRT) which monitors the government's response to the coronavirus in 160 countries and is a parameter that evaluates the policies taken by a country's government based on nine metrics. This index does not measure the effectiveness of a country's government response, but only the level of tightness. However, the results of the tests carried out in this study did not find a significant impact of pandemic indicators, the number of cases, and the number of daily deaths related to COVID-19 on stock returns.
Downloads
References
Ashraf, B. N. (2020). Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. Journal of Behavioral and Experimental Finance, 27, 100371. https://doi.org/10.1016/j.jbef.2020.100371
Bai, C., Quayson, M., & Sarkis, J. (2021). COVID-19 pandemic digitization lessons for sustainable development of micro-and small- enterprises. Sustainable Production and Consumption, 27, 1989–2001. https://doi.org/10.1016/j.spc.2021.04.035
Baig, A. M., Khaleeq, A., Ali, U., & Syeda, H. (2020). Evidence of the COVID-19 Virus Targeting the CNS: Tissue Distribution, Host–Virus Interaction, and Proposed Neurotropic Mechanisms. ACS Chemical Neuroscience, 11(7), 995–998. https://doi.org/10.1021/acschemneuro.0c00122
Billings, M. (1997). The Influenza Pandemic of 1918. Stanford Education. https://virus.stanford.edu/uda/
Brodeur, M., Audette-Chapdelaine, S., Savard, A.-C., & Kairouz, S. (2021). Gambling and the COVID-19 pandemic: A scoping review. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 111, 110389. https://doi.org/10.1016/j.pnpbp.2021.110389
Garrett, T. A. (2008). Economic Effects of the 1918 Influenza Pandemic. https://files.stlouisfed.org/files/htdocs/publications/review/08/03/Garrett.pdf
Gentilini, U., & Orton, I. (2021). Oxford Supertracker: The Global Directory for COVID Policy Trackers and Surveys. https://supertracker.spi.ox.ac.uk/
Haryanto. (2020). Dampak COVID-19 terhadap Pergerakan Nilai Tukar Rupiah dan Indeks Harga Saham Gabungan (IHSG). The Indonesian Journal of Development Planning, 4(2), 151–165. https://doi.org/https://doi.org/10.36574/jpp.v4i2.114
Ibrahim, I., Kamaludin, K., & Sundarasen, S. (2020). COVID-19, Government Response, and Market Volatility: Evidence from the Asia-Pacific Developed and Developing Markets. Economies, 8(4), 105. https://doi.org/10.3390/economies8040105
Kartika, A. (2010). Faktor-faktor yang mempengaruhi audit delay di Indonesia (Studi Empiris Pada Perusahaan-Perusahaan LQ 45 Yang Terdaftar Di Bursa Efek Jakarta). Jurnal Bisnis Dan Ekonomi (JBE), 16(1), 1–17. https://www.unisbank.ac.id/ojs/index.php/fe3/article/view/310
Padovani, E., & Iacuzzi, S. (2021). Real-time crisis management: Testing the role of accounting in local governments. Journal of Accounting and Public Policy, 40(3), 106854. https://doi.org/10.1016/j.jaccpubpol.2021.106854
Sreeramula, S., & Rahardjo, D. (2021). Estimating COVID-19 Rt in Real-time: An Indonesia health policy perspective. Machine Learning with Applications, 6, 100136. https://doi.org/10.1016/j.mlwa.2021.100136
Stebbins, S. (2020, April 14). Unemployment: These are every state’s claims since the coronavirus shut the economy down. USA Today, 1. https://www.usatoday.com/story/money/2020/04/14/coronavirus-unemployment-claims-caused-covid-19-crisis-state/5130034002/
Tosepu, R., Gunawan, J., Effendy, D. S., Ahmad, L. O. A. I., Lestari, H., Bahar, H., & Asfian, P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of The Total Environment, 725, 138436. https://doi.org/10.1016/j.scitotenv.2020.138436
Utomo, C. D., & Hanggraeni, D. (2021). The Impact of COVID-19 Pandemic on Stock Market Performance in Indonesia. The Journal of Asian Finance, Economics and Business, 8(5), 777–784. https://doi.org/https://doi.org/10.13106/jafeb.2021.vol8.no5.0777
Wagner, A. F. (2020). What the stock market tells us about the post-COVID-19 world. Nature Human Behaviour, 4(5), 440. https://www.nature.com/articles/s41562-020-0869-y
WHO. (2008). The Global Burden of Disease: 2004 update. https://apps.who.int/iris/handle/10665/43942
Yousef, I. (2020). Spillover of COVID-19: Impact on Stock Market Volatility. Journal of Psychosocial Rehabilitation, 24(6), 18069–18081. https://www.psychosocial.com/article/PR261476/34211/
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
