Measuring the risk of Chinese Fintech industry: evidence from the stock index
This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Va...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yao, Yinhong [verfasserIn] Li, Jianping [verfasserIn] Sun, Xiaolei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: | |
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| Schlagwörter: |
Financial Technology (Fintech) |
| Übergeordnetes Werk: |
Enthalten in: Finance research letters - New York : Elsevier Science, 2004, 39 |
|---|---|
| Übergeordnetes Werk: |
volume:39 |
| DOI / URN: |
10.1016/j.frl.2020.101564 |
|---|
| Katalog-ID: |
ELV00562164X |
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| 245 | 1 | 0 | |a Measuring the risk of Chinese Fintech industry: evidence from the stock index |
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| 520 | |a This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. | ||
| 650 | 7 | |8 1.1\x |a Finanzmarkt |0 (DE-2867)13723-2 |2 stw | |
| 650 | 7 | |8 1.2\x |a Finanzierung |0 (DE-2867)25742-3 |2 stw | |
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| 650 | 7 | |8 1.4\x |a Welt |0 (DE-2867)16809-5 |2 stw | |
| 650 | 4 | |a Financial Technology (Fintech) | |
| 650 | 4 | |a Tail risk | |
| 650 | 4 | |a Expectile regression model | |
| 650 | 4 | |a Expectile Value at Risk (EVaR) | |
| 650 | 4 | |a Expected shortfall (ES) | |
| 700 | 1 | |a Li, Jianping |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Xiaolei |e verfasserin |4 aut | |
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10.1016/j.frl.2020.101564 doi (DE-627)ELV00562164X (ELSEVIER)S1544-6123(19)31105-5 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin aut Measuring the risk of Chinese Fintech industry: evidence from the stock index 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. 1.1\x Finanzmarkt (DE-2867)13723-2 stw 1.2\x Finanzierung (DE-2867)25742-3 stw 1.3\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.4\x Welt (DE-2867)16809-5 stw Financial Technology (Fintech) Tail risk Expectile regression model Expectile Value at Risk (EVaR) Expected shortfall (ES) Li, Jianping verfasserin aut Sun, Xiaolei verfasserin aut Enthalten in Finance research letters New York : Elsevier Science, 2004 39 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:39 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 ECS-05001 SKW AR 39 |
| spelling |
10.1016/j.frl.2020.101564 doi (DE-627)ELV00562164X (ELSEVIER)S1544-6123(19)31105-5 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin aut Measuring the risk of Chinese Fintech industry: evidence from the stock index 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. 1.1\x Finanzmarkt (DE-2867)13723-2 stw 1.2\x Finanzierung (DE-2867)25742-3 stw 1.3\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.4\x Welt (DE-2867)16809-5 stw Financial Technology (Fintech) Tail risk Expectile regression model Expectile Value at Risk (EVaR) Expected shortfall (ES) Li, Jianping verfasserin aut Sun, Xiaolei verfasserin aut Enthalten in Finance research letters New York : Elsevier Science, 2004 39 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:39 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 ECS-05001 SKW AR 39 |
| allfields_unstemmed |
10.1016/j.frl.2020.101564 doi (DE-627)ELV00562164X (ELSEVIER)S1544-6123(19)31105-5 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin aut Measuring the risk of Chinese Fintech industry: evidence from the stock index 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. 1.1\x Finanzmarkt (DE-2867)13723-2 stw 1.2\x Finanzierung (DE-2867)25742-3 stw 1.3\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.4\x Welt (DE-2867)16809-5 stw Financial Technology (Fintech) Tail risk Expectile regression model Expectile Value at Risk (EVaR) Expected shortfall (ES) Li, Jianping verfasserin aut Sun, Xiaolei verfasserin aut Enthalten in Finance research letters New York : Elsevier Science, 2004 39 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:39 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 ECS-05001 SKW AR 39 |
| allfieldsGer |
10.1016/j.frl.2020.101564 doi (DE-627)ELV00562164X (ELSEVIER)S1544-6123(19)31105-5 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin aut Measuring the risk of Chinese Fintech industry: evidence from the stock index 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. 1.1\x Finanzmarkt (DE-2867)13723-2 stw 1.2\x Finanzierung (DE-2867)25742-3 stw 1.3\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.4\x Welt (DE-2867)16809-5 stw Financial Technology (Fintech) Tail risk Expectile regression model Expectile Value at Risk (EVaR) Expected shortfall (ES) Li, Jianping verfasserin aut Sun, Xiaolei verfasserin aut Enthalten in Finance research letters New York : Elsevier Science, 2004 39 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:39 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 ECS-05001 SKW AR 39 |
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10.1016/j.frl.2020.101564 doi (DE-627)ELV00562164X (ELSEVIER)S1544-6123(19)31105-5 DE-627 ger DE-627 rda eng Yao, Yinhong verfasserin aut Measuring the risk of Chinese Fintech industry: evidence from the stock index 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. 1.1\x Finanzmarkt (DE-2867)13723-2 stw 1.2\x Finanzierung (DE-2867)25742-3 stw 1.3\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.4\x Welt (DE-2867)16809-5 stw Financial Technology (Fintech) Tail risk Expectile regression model Expectile Value at Risk (EVaR) Expected shortfall (ES) Li, Jianping verfasserin aut Sun, Xiaolei verfasserin aut Enthalten in Finance research letters New York : Elsevier Science, 2004 39 Online-Ressource (DE-627)387481583 (DE-600)2145766-9 (DE-576)259272752 1544-6123 nnns volume:39 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 ECS-05001 SKW AR 39 |
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Measuring the risk of Chinese Fintech industry: evidence from the stock index |
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| title_full |
Measuring the risk of Chinese Fintech industry: evidence from the stock index |
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Yao, Yinhong |
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Finance research letters |
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Yao, Yinhong Li, Jianping Sun, Xiaolei |
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Elektronische Aufsätze |
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Yao, Yinhong |
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10.1016/j.frl.2020.101564 |
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measuring the risk of chinese fintech industry: evidence from the stock index |
| title_auth |
Measuring the risk of Chinese Fintech industry: evidence from the stock index |
| abstract |
This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. |
| abstractGer |
This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. |
| abstract_unstemmed |
This study measures the risk of the emerging Fintech industry in China and identifies its influencing risk factors by calculating the tail risk of Fintech stock index. The expectile regression model that includes the lagged returns and macroeconomic risk factors is used to calculate the Expectile Value-at-Risk (EVaR). Based on the 1230 daily returns of Fintech index ranges from July 2, 2014, to September 10, 2019, the empirical results indicate that the Fintech industry possesses a higher risk, and is affected by both the past development and internal macroeconomic condition. |
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| title_short |
Measuring the risk of Chinese Fintech industry: evidence from the stock index |
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| up_date |
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