The Financial Market in China under the COVID-19

Bingyu Zhao; Wanping Yang; Jun Wen; Wei Zhang

doi:10.1080/1540496X.2022.2070472

The Financial Market in China under the COVID-19

Bingyu Zhao
, Wanping Yang
, Jun Wen
, Wei Zhang

School of Economics and Finance (SOEF)

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.

Original language	English
Pages (from-to)	3726-3738
Number of pages	13
Journal	Emerging Markets Finance and Trade
Volume	58
Issue number	13
DOIs	https://doi.org/10.1080/1540496X.2022.2070472
State	Published - 2022

Keywords

COVID-19
ensemble empirical mode decomposition
financial markets
minimum spanning tree
wavelet analysis

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10.1080/1540496X.2022.2070472

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title = "The Financial Market in China under the COVID-19",

abstract = "The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.",

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AU - Zhao, Bingyu

AU - Yang, Wanping

AU - Wen, Jun

AU - Zhang, Wei

PY - 2022

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N2 - The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.

AB - The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.

KW - COVID-19

KW - ensemble empirical mode decomposition

KW - financial markets

KW - minimum spanning tree

KW - wavelet analysis

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IS - 13

ER -

The Financial Market in China under the COVID-19

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