A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic.
This paper takes the unexpected event of the new coronavirus as the research background, selects the daily closing price data of the financial sectors (banking, insurance, securities, and multifinance) from 20 June 2017 to 31 December 2023. It then applies the TVP-VAR-DY model to empirically study t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314071 |
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| author | Cuicui Liu HuiZi Ma Xiangrong Wang Junfu Cui Xu Shen |
| author_facet | Cuicui Liu HuiZi Ma Xiangrong Wang Junfu Cui Xu Shen |
| author_sort | Cuicui Liu |
| collection | DOAJ |
| description | This paper takes the unexpected event of the new coronavirus as the research background, selects the daily closing price data of the financial sectors (banking, insurance, securities, and multifinance) from 20 June 2017 to 31 December 2023. It then applies the TVP-VAR-DY model to empirically study the risk spillover effect among financial sectors. The study identified three distinct stages: before, during, and after the epidemic. It revealed that the total systematic spillover exhibited an initial increase, followed by a subsequent decrease. Notably, the fluctuation in this phenomenon intensified significantly during the epidemic. The securities sector emerged as the most susceptible to spillover risks from other sectors and, in turn, the most vulnerable to risk contagion from other sectors. Conversely, the banking sector demonstrated relative stability. Furthermore, the multifinance sector is more susceptible to risk contagion from other sectors during the pre-epidemic and mid-epidemic stages. However, following the epidemic, as the economy has not yet fully recovered, the multifinance sector is more likely to experience spillover risks from other sectors, and the insurance sector also primarily acts as a risk spillover. Finally, five different lag orders were selected to test the robustness of the empirical results of the model. The test results demonstrated that the model was valid and the results were feasible. |
| format | Article |
| id | doaj-art-226eda8eec35432d8d4f6ab1df2ee383 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-226eda8eec35432d8d4f6ab1df2ee3832025-01-08T05:33:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031407110.1371/journal.pone.0314071A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic.Cuicui LiuHuiZi MaXiangrong WangJunfu CuiXu ShenThis paper takes the unexpected event of the new coronavirus as the research background, selects the daily closing price data of the financial sectors (banking, insurance, securities, and multifinance) from 20 June 2017 to 31 December 2023. It then applies the TVP-VAR-DY model to empirically study the risk spillover effect among financial sectors. The study identified three distinct stages: before, during, and after the epidemic. It revealed that the total systematic spillover exhibited an initial increase, followed by a subsequent decrease. Notably, the fluctuation in this phenomenon intensified significantly during the epidemic. The securities sector emerged as the most susceptible to spillover risks from other sectors and, in turn, the most vulnerable to risk contagion from other sectors. Conversely, the banking sector demonstrated relative stability. Furthermore, the multifinance sector is more susceptible to risk contagion from other sectors during the pre-epidemic and mid-epidemic stages. However, following the epidemic, as the economy has not yet fully recovered, the multifinance sector is more likely to experience spillover risks from other sectors, and the insurance sector also primarily acts as a risk spillover. Finally, five different lag orders were selected to test the robustness of the empirical results of the model. The test results demonstrated that the model was valid and the results were feasible.https://doi.org/10.1371/journal.pone.0314071 |
| spellingShingle | Cuicui Liu HuiZi Ma Xiangrong Wang Junfu Cui Xu Shen A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. PLoS ONE |
| title | A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. |
| title_full | A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. |
| title_fullStr | A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. |
| title_full_unstemmed | A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. |
| title_short | A comparative study of dynamic risk spillovers among financial sectors in China before and after the epidemic. |
| title_sort | comparative study of dynamic risk spillovers among financial sectors in china before and after the epidemic |
| url | https://doi.org/10.1371/journal.pone.0314071 |
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