Impact of COVID-19 and economic policy uncertainty on China's stock market returns: evidence from quantile-on-quantile and causality-in-quantiles approaches

被引:24
作者
Ullah, Assad [1 ,2 ]
Zhao, Xinshun [2 ]
Amin, Azka [3 ]
Syed, Aamir Aijaz [4 ]
Riaz, Adeel [5 ]
机构
[1] Hainan Normal Univ, Sch Econ & Management, Haikou, Hainan, Peoples R China
[2] Henan Univ, Sch Econ, Kaifeng, Peoples R China
[3] Sukkur IBA Univ, Dept Business Adm, Sukkur, Pakistan
[4] Shri Ramswaroop Mem Univ, Inst Management Commerce & Econ, Lucknow, India
[5] Henan Univ, Sch Business, Kaifeng, Peoples R China
关键词
COVID-19; Economic policy uncertainty; Stock market returns; Quantile-on-quantile; Causality in quantiles; China; OIL PRICES; FRESH INSIGHTS; GROWTH; PERFORMANCE; REGRESSION; ENERGY;
D O I
10.1007/s11356-022-22680-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
COVID-19 unexpectedly ensnared the entire world and wreaked havoc on global economic and financial systems. The stock market is sensitive to black swan events, and the COVID-19 disaster was no exception. Against this backdrop, this study explores the impact of COVID-19 and economic policy uncertainty (EPU) on Chinese stock markets' returns for the period spanning January 23, 2020 to August 04, 2021. The outcomes of the novel quantile-on-quantile regression analysis revealed that both COVID-19 and EPU had a significant negative impact on both Shanghai and Shenzhen stock market returns, while COVID-19 aggravated the level of economic uncertainty in both financial markets. The quantile causality approach of Troster et al. (2018) validates our main estimations. We conclude that COVID-19 and a high level of EPU enervated the returns of China's leading stock markets. Our study provides key insights to policymakers and market participants to determine the behavior of China's stock market returns vis-a-vis COVID-19 during the peak of the pandemic and beyond. Specifically, our findings apprise portfolio investors to augment their portfolio diversification fronts.
引用
收藏
页码:12596 / 12607
页数:12
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