scholarly journals The impact of COVID-19 on the stock market crash risk in China

2021 ◽  
Vol 57 ◽  
pp. 101419 ◽  
Author(s):  
Zhifeng Liu ◽  
Toan Luu Duc Huynh ◽  
Peng-Fei Dai
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Peng-Fei Dai ◽  
Xiong Xiong ◽  
Zhifeng Liu ◽  
Toan Luu Duc Huynh ◽  
Jianjun Sun

AbstractThis paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ping Zhang ◽  
Jieying Gao ◽  
Yanbin Zhang ◽  
Te-Wei Wang

Due to the increasing linkage of China and the US stock markets today, we constructed a TVP-VAR model to study the dynamic spillover effects between the US stock volatility and China’s stock market crash risk. We found dynamic spillover effects are constantly strengthening between US stock volatility and China’s stock market crash risk: when the US stock volatility increases, China’s stock market crash risk increases. In addition, the gradual improvement of financial market openness in China, the short-term capital outflow from China, and the depreciation of the RMB exchange rate will increase China’s stock market crash risk. And, the impacts of short-term capital outflow from China are more significant. Further, the increase in China’s stock market crash risk will lead to the decline of the US stock volatility, which may be due to the flight to quality.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Binghui Wu ◽  
Yuanman Cai ◽  
Mengjiao Zhang

This paper uses the partial least squares method to construct the investor sentiment index in Chinese stock market. The Shanghai Stock Exchange 180 Index and the Shenzhen Stock Exchange 100 Index are used as samples. From the perspectives of holistic sentiment and heterogeneous sentiment, this paper studies the impact of investor sentiment on stock price crash risk. The results show that investor sentiment can significantly affect stock price crash risk in Shanghai and Shenzhen A-share markets, especially in the Shenzhen A-share market no matter from which perspective. And investor pessimism has a greater impact on stock price crash risk in the Shenzhen A-share market from the perspective of heterogeneous sentiment. Compared with the available researches, this paper makes two contributions: (i) the comparative analysis is adopted to discuss the differences between Shanghai and Shenzhen A-share markets, abandoning the research approach that takes the two markets as a whole in existing literature, and (ii) this paper not only studies the impact of investor holistic sentiment on stock price crash risk from a macro perspective, but also adds a more micro heterogeneous sentiment and conducts a comparative analysis.


Author(s):  
Arnulfo M. Castellanos ◽  
Francisco S. Vargas ◽  
Luis G. Rentería

The global financial crisis that took place during the period 2007-09 had its most prominent manifestation in the general stock market crash. This could be studied from the perspective of financial contagion, using a mathematical tool known as wavelets. This paper aims to assess the impact of the US stock market crash on the other stock markets all over the world. As an initial point the assumption that the former was the epicenter of the global financial crisis stands out. In order to determine the existence of differentiated impacts that show the presence of inertial factors in different stock exchange markets, a filtering technique is used on stock market indexes to assess such impacts. The data series are worked out on different time scales in order to identify short and long term effects.  


2018 ◽  
Vol 24 (1) ◽  
pp. 178-188
Author(s):  
A.Yu. Mikhailov ◽  
◽  
T.F. Burova ◽  

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