Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets : comparing the impacts of three Stock Connect programs
Gespeichert in:
| Autor*in: |
Yao, Yinhong [verfasserIn] Li, Jingyu [verfasserIn] Chen, Wei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International review of economics & finance - Amsterdam [u.a.] : Elsevier Science, 1992, 89(2024), 1 vom: Jan., Seite 1217-1233 |
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| Übergeordnetes Werk: |
volume:89 ; year:2024 ; number:1 ; month:01 ; pages:1217-1233 |
| Links: |
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| DOI / URN: |
10.1016/j.iref.2023.08.020 |
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| Katalog-ID: |
1877615072 |
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| 982 | |2 26 |1 00 |x DE-206 |b This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. | ||
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10.1016/j.iref.2023.08.020 doi (DE-627)1877615072 (DE-599)KXP1877615072 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin (DE-588)1200878337 (DE-627)1683876660 aut Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 Li, Jingyu verfasserin (DE-588)1254060863 (DE-627)1796679275 aut Chen, Wei verfasserin (DE-588)1173828680 (DE-627)1043638725 (DE-576)515722456 aut Enthalten in International review of economics & finance Amsterdam [u.a.] : Elsevier Science, 1992 89(2024), 1 vom: Jan., Seite 1217-1233 Online-Ressource (DE-627)324452098 (DE-600)2026509-8 (DE-576)259271977 1059-0560 nnns volume:89 year:2024 number:1 month:01 pages:1217-1233 https://www.sciencedirect.com/science/article/pii/S1059056023003398/pdfft?md5=1ba3a7d058fd7816370c0fc061613b85&pid=1-s2.0-S1059056023003398-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.iref.2023.08.020 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 89 2024 1 1 1217-1233 26 01 0206 4452840132 x1z 10-01-24 26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. |
| spelling |
10.1016/j.iref.2023.08.020 doi (DE-627)1877615072 (DE-599)KXP1877615072 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin (DE-588)1200878337 (DE-627)1683876660 aut Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 Li, Jingyu verfasserin (DE-588)1254060863 (DE-627)1796679275 aut Chen, Wei verfasserin (DE-588)1173828680 (DE-627)1043638725 (DE-576)515722456 aut Enthalten in International review of economics & finance Amsterdam [u.a.] : Elsevier Science, 1992 89(2024), 1 vom: Jan., Seite 1217-1233 Online-Ressource (DE-627)324452098 (DE-600)2026509-8 (DE-576)259271977 1059-0560 nnns volume:89 year:2024 number:1 month:01 pages:1217-1233 https://www.sciencedirect.com/science/article/pii/S1059056023003398/pdfft?md5=1ba3a7d058fd7816370c0fc061613b85&pid=1-s2.0-S1059056023003398-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.iref.2023.08.020 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 89 2024 1 1 1217-1233 26 01 0206 4452840132 x1z 10-01-24 26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. |
| allfields_unstemmed |
10.1016/j.iref.2023.08.020 doi (DE-627)1877615072 (DE-599)KXP1877615072 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin (DE-588)1200878337 (DE-627)1683876660 aut Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 Li, Jingyu verfasserin (DE-588)1254060863 (DE-627)1796679275 aut Chen, Wei verfasserin (DE-588)1173828680 (DE-627)1043638725 (DE-576)515722456 aut Enthalten in International review of economics & finance Amsterdam [u.a.] : Elsevier Science, 1992 89(2024), 1 vom: Jan., Seite 1217-1233 Online-Ressource (DE-627)324452098 (DE-600)2026509-8 (DE-576)259271977 1059-0560 nnns volume:89 year:2024 number:1 month:01 pages:1217-1233 https://www.sciencedirect.com/science/article/pii/S1059056023003398/pdfft?md5=1ba3a7d058fd7816370c0fc061613b85&pid=1-s2.0-S1059056023003398-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.iref.2023.08.020 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 89 2024 1 1 1217-1233 26 01 0206 4452840132 x1z 10-01-24 26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. |
| allfieldsGer |
10.1016/j.iref.2023.08.020 doi (DE-627)1877615072 (DE-599)KXP1877615072 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin (DE-588)1200878337 (DE-627)1683876660 aut Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 Li, Jingyu verfasserin (DE-588)1254060863 (DE-627)1796679275 aut Chen, Wei verfasserin (DE-588)1173828680 (DE-627)1043638725 (DE-576)515722456 aut Enthalten in International review of economics & finance Amsterdam [u.a.] : Elsevier Science, 1992 89(2024), 1 vom: Jan., Seite 1217-1233 Online-Ressource (DE-627)324452098 (DE-600)2026509-8 (DE-576)259271977 1059-0560 nnns volume:89 year:2024 number:1 month:01 pages:1217-1233 https://www.sciencedirect.com/science/article/pii/S1059056023003398/pdfft?md5=1ba3a7d058fd7816370c0fc061613b85&pid=1-s2.0-S1059056023003398-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.iref.2023.08.020 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 89 2024 1 1 1217-1233 26 01 0206 4452840132 x1z 10-01-24 26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. |
| allfieldsSound |
10.1016/j.iref.2023.08.020 doi (DE-627)1877615072 (DE-599)KXP1877615072 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin (DE-588)1200878337 (DE-627)1683876660 aut Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 Li, Jingyu verfasserin (DE-588)1254060863 (DE-627)1796679275 aut Chen, Wei verfasserin (DE-588)1173828680 (DE-627)1043638725 (DE-576)515722456 aut Enthalten in International review of economics & finance Amsterdam [u.a.] : Elsevier Science, 1992 89(2024), 1 vom: Jan., Seite 1217-1233 Online-Ressource (DE-627)324452098 (DE-600)2026509-8 (DE-576)259271977 1059-0560 nnns volume:89 year:2024 number:1 month:01 pages:1217-1233 https://www.sciencedirect.com/science/article/pii/S1059056023003398/pdfft?md5=1ba3a7d058fd7816370c0fc061613b85&pid=1-s2.0-S1059056023003398-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.iref.2023.08.020 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 89 2024 1 1 1217-1233 26 01 0206 4452840132 x1z 10-01-24 26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets. |
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Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets.</subfield></datafield></record></collection>
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26 00 DE-206 This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets comparing the impacts of three Stock Connect programs Yinhong Yao, Jingyu Li, Wei Chen Extreme risk spillover (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Stock connect program (dpeaa)DE-206 Time-varying copula (dpeaa)DE-206 Wavelet decomposition (dpeaa)DE-206 |
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">89</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="c">1</subfield><subfield code="h">1217-1233</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4452840132</subfield><subfield code="y">x1z</subfield><subfield code="z">10-01-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">This paper investigates the multiscale extreme risk spillovers among Shanghai, Shenzhen, Hong Kong, and London stock markets by combining the wavelet decomposition and time-varying copula-CoVaR methods, and compares the impacts of three Stock Connect programs proposed by China. Based on the daily closing prices of four stock indexes ranging from Jan 4, 2013, to Jan 21, 2022, we find that multiscale extreme risk spillovers significantly exist among four stock markets with dynamic and asymmetric characteristics. The magnitudes of risk spillover effects decrease as the time scale increases. The implementation of the SH-HK and SH-L programs could improve the spillovers between related stock markets, while the SZ-HK program possesses a slightly decreasing effect, and their impacts are lower than the external shock events. These results systematically reveal the extreme risk transmission features among four stock markets, which are beneficial for practical portfolio optimization and risk management in the opening stock markets.</subfield></datafield></record></collection>
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7.4007607 |