Nonlinear impact of climate transition risks on green stock performance : perspectives from multiscale and lag effects
Gespeichert in:
| Autor*in: |
Wang, Junling [verfasserIn] Cheng, Siyu [verfasserIn] Rong, Xueyun [verfasserIn] Xu, Xin [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 financial analysis - Amsterdam [u.a.] : Elsevier Science, 1992, 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 |
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| Übergeordnetes Werk: |
volume:94 ; year:2024 ; month:07 ; elocationid:103269 ; pages:1-10 |
| Links: |
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| DOI / URN: |
10.1016/j.irfa.2024.103269 |
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| Katalog-ID: |
1891008250 |
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| 982 | |2 26 |1 00 |x DE-206 |b This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. | ||
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10.1016/j.irfa.2024.103269 doi (DE-627)1891008250 (DE-599)KXP1891008250 DE-627 ger DE-627 rda eng Wang, Junling verfasserin (DE-588)1337182583 (DE-627)1896767133 aut Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 Cheng, Siyu verfasserin (DE-588)1339840995 (DE-627)1899378081 aut Rong, Xueyun verfasserin (DE-588)1337183601 (DE-627)189676875X aut Xu, Xin verfasserin (DE-588)1338833316 (DE-627)1898529841 aut Enthalten in International review of financial analysis Amsterdam [u.a.] : Elsevier Science, 1992 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 Online-Ressource (DE-627)32465488X (DE-600)2029229-6 (DE-576)259272108 1057-5219 nnns volume:94 year:2024 month:07 elocationid:103269 pages:1-10 https://www.sciencedirect.com/science/article/pii/S1057521924002011/pdfft?md5=4e27495cc37ac419458371061f0b630c&pid=1-s2.0-S1057521924002011-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.irfa.2024.103269 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_2006 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_2038 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_2068 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_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2024 7 103269 1-10 26 01 0206 4536067310 x1z 10-06-24 26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. |
| spelling |
10.1016/j.irfa.2024.103269 doi (DE-627)1891008250 (DE-599)KXP1891008250 DE-627 ger DE-627 rda eng Wang, Junling verfasserin (DE-588)1337182583 (DE-627)1896767133 aut Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 Cheng, Siyu verfasserin (DE-588)1339840995 (DE-627)1899378081 aut Rong, Xueyun verfasserin (DE-588)1337183601 (DE-627)189676875X aut Xu, Xin verfasserin (DE-588)1338833316 (DE-627)1898529841 aut Enthalten in International review of financial analysis Amsterdam [u.a.] : Elsevier Science, 1992 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 Online-Ressource (DE-627)32465488X (DE-600)2029229-6 (DE-576)259272108 1057-5219 nnns volume:94 year:2024 month:07 elocationid:103269 pages:1-10 https://www.sciencedirect.com/science/article/pii/S1057521924002011/pdfft?md5=4e27495cc37ac419458371061f0b630c&pid=1-s2.0-S1057521924002011-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.irfa.2024.103269 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_2006 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_2038 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_2068 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_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2024 7 103269 1-10 26 01 0206 4536067310 x1z 10-06-24 26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. |
| allfields_unstemmed |
10.1016/j.irfa.2024.103269 doi (DE-627)1891008250 (DE-599)KXP1891008250 DE-627 ger DE-627 rda eng Wang, Junling verfasserin (DE-588)1337182583 (DE-627)1896767133 aut Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 Cheng, Siyu verfasserin (DE-588)1339840995 (DE-627)1899378081 aut Rong, Xueyun verfasserin (DE-588)1337183601 (DE-627)189676875X aut Xu, Xin verfasserin (DE-588)1338833316 (DE-627)1898529841 aut Enthalten in International review of financial analysis Amsterdam [u.a.] : Elsevier Science, 1992 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 Online-Ressource (DE-627)32465488X (DE-600)2029229-6 (DE-576)259272108 1057-5219 nnns volume:94 year:2024 month:07 elocationid:103269 pages:1-10 https://www.sciencedirect.com/science/article/pii/S1057521924002011/pdfft?md5=4e27495cc37ac419458371061f0b630c&pid=1-s2.0-S1057521924002011-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.irfa.2024.103269 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_2006 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_2038 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_2068 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_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2024 7 103269 1-10 26 01 0206 4536067310 x1z 10-06-24 26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. |
| allfieldsGer |
10.1016/j.irfa.2024.103269 doi (DE-627)1891008250 (DE-599)KXP1891008250 DE-627 ger DE-627 rda eng Wang, Junling verfasserin (DE-588)1337182583 (DE-627)1896767133 aut Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 Cheng, Siyu verfasserin (DE-588)1339840995 (DE-627)1899378081 aut Rong, Xueyun verfasserin (DE-588)1337183601 (DE-627)189676875X aut Xu, Xin verfasserin (DE-588)1338833316 (DE-627)1898529841 aut Enthalten in International review of financial analysis Amsterdam [u.a.] : Elsevier Science, 1992 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 Online-Ressource (DE-627)32465488X (DE-600)2029229-6 (DE-576)259272108 1057-5219 nnns volume:94 year:2024 month:07 elocationid:103269 pages:1-10 https://www.sciencedirect.com/science/article/pii/S1057521924002011/pdfft?md5=4e27495cc37ac419458371061f0b630c&pid=1-s2.0-S1057521924002011-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.irfa.2024.103269 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_2006 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_2038 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_2068 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_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2024 7 103269 1-10 26 01 0206 4536067310 x1z 10-06-24 26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. |
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10.1016/j.irfa.2024.103269 doi (DE-627)1891008250 (DE-599)KXP1891008250 DE-627 ger DE-627 rda eng Wang, Junling verfasserin (DE-588)1337182583 (DE-627)1896767133 aut Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 Cheng, Siyu verfasserin (DE-588)1339840995 (DE-627)1899378081 aut Rong, Xueyun verfasserin (DE-588)1337183601 (DE-627)189676875X aut Xu, Xin verfasserin (DE-588)1338833316 (DE-627)1898529841 aut Enthalten in International review of financial analysis Amsterdam [u.a.] : Elsevier Science, 1992 94(2024) vom: Juli, Artikel-ID 103269, Seite 1-10 Online-Ressource (DE-627)32465488X (DE-600)2029229-6 (DE-576)259272108 1057-5219 nnns volume:94 year:2024 month:07 elocationid:103269 pages:1-10 https://www.sciencedirect.com/science/article/pii/S1057521924002011/pdfft?md5=4e27495cc37ac419458371061f0b630c&pid=1-s2.0-S1057521924002011-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.irfa.2024.103269 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_2006 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_2038 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_2068 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_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2024 7 103269 1-10 26 01 0206 4536067310 x1z 10-06-24 26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility. |
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26 00 DE-206 This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility Nonlinear impact of climate transition risks on green stock performance perspectives from multiscale and lag effects Junling Wang, Siyu Cheng, Xueyun Rong, Xin Xu Climate change risks (dpeaa)DE-206 Green stock (dpeaa)DE-206 Multiscale analysis (dpeaa)DE-206 Nonlinear impact (dpeaa)DE-206 |
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| score |
7.0146856 |