Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective
Accurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/10/502 |
| _version_ | 1797572196077404160 |
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| author | Yishi Li Yongpin Ni Hanxing Zheng Linyi Zhou |
| author_facet | Yishi Li Yongpin Ni Hanxing Zheng Linyi Zhou |
| author_sort | Yishi Li |
| collection | DOAJ |
| description | Accurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with the least absolute shrinkage and selection operator vector autoregression. It assesses the volatility connectedness network using complex network analysis techniques. The data are derived from 31 publicly traded Chinese financial institutions between 4 January 2011 and 31 August 2023, encompassing the Chinese stock market crash in 2015 and the COVID-19 pandemic. The frequency dynamics of the volatility connectedness results indicate that long-term connectedness peaks and cross-sectoral connectedness rises during periods of financial instability, especially in the recent bull market (2014–2015) and the 2015 Chinese stock market crash. The volatility connectedness of Chinese financial institutions declined during the COVID-19 pandemic but rose during the post-COVID-19 pandemic period. Network estimation results show that securities triggered the 2015 bull market, whereas banks were the main risk transmitters during the 2015 market crash. These results have important practical implications for supervisory authorities. |
| first_indexed | 2024-03-10T20:51:05Z |
| format | Article |
| id | doaj.art-808100e1a3f34281b0ba0f49a1cf7bf2 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-10T20:51:05Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-808100e1a3f34281b0ba0f49a1cf7bf22023-11-19T18:19:41ZengMDPI AGSystems2079-89542023-10-01111050210.3390/systems11100502Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics PerspectiveYishi Li0Yongpin Ni1Hanxing Zheng2Linyi Zhou3College of Economics & Management, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaSchool of Economics and Management, Tongji University, Shanghai 200092, ChinaSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Government, Shanghai University of Political Science and Law, Shanghai 201701, ChinaAccurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with the least absolute shrinkage and selection operator vector autoregression. It assesses the volatility connectedness network using complex network analysis techniques. The data are derived from 31 publicly traded Chinese financial institutions between 4 January 2011 and 31 August 2023, encompassing the Chinese stock market crash in 2015 and the COVID-19 pandemic. The frequency dynamics of the volatility connectedness results indicate that long-term connectedness peaks and cross-sectoral connectedness rises during periods of financial instability, especially in the recent bull market (2014–2015) and the 2015 Chinese stock market crash. The volatility connectedness of Chinese financial institutions declined during the COVID-19 pandemic but rose during the post-COVID-19 pandemic period. Network estimation results show that securities triggered the 2015 bull market, whereas banks were the main risk transmitters during the 2015 market crash. These results have important practical implications for supervisory authorities.https://www.mdpi.com/2079-8954/11/10/502frequency volatility connectednessChinese financial institutionsfinancial regulationLASSO-VARnetwork estimation |
| spellingShingle | Yishi Li Yongpin Ni Hanxing Zheng Linyi Zhou Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective Systems frequency volatility connectedness Chinese financial institutions financial regulation LASSO-VAR network estimation |
| title | Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective |
| title_full | Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective |
| title_fullStr | Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective |
| title_full_unstemmed | Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective |
| title_short | Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective |
| title_sort | volatility connectedness of chinese financial institutions evidence from a frequency dynamics perspective |
| topic | frequency volatility connectedness Chinese financial institutions financial regulation LASSO-VAR network estimation |
| url | https://www.mdpi.com/2079-8954/11/10/502 |
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