Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA)
The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Xiaoyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
Detrended cross-correlation coefficient Detrended cross-correlation analysis (DCCA) |
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| Übergeordnetes Werk: |
Enthalten in: Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study - Dai, Jiamiao ELSEVIER, 2022, europhysics journal, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:529 ; year:2019 ; day:1 ; month:09 ; pages:0 |
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| DOI / URN: |
10.1016/j.physa.2019.121534 |
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| 520 | |a The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. | ||
| 520 | |a The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. | ||
| 650 | 7 | |a Detrended cross-correlation coefficient |2 Elsevier | |
| 650 | 7 | |a Detrended cross-correlation analysis (DCCA) |2 Elsevier | |
| 650 | 7 | |a Commodity markets |2 Elsevier | |
| 650 | 7 | |a Non-stationary principal component analysis (NSPCA) |2 Elsevier | |
| 650 | 7 | |a The CSI300 index |2 Elsevier | |
| 650 | 7 | |a Pearson correlation coefficient |2 Elsevier | |
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10.1016/j.physa.2019.121534 doi GBV00000000000654.pica (DE-627)ELV047126000 (ELSEVIER)S0378-4371(19)30898-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zhu, Xiaoyu verfasserin aut Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. Detrended cross-correlation coefficient Elsevier Detrended cross-correlation analysis (DCCA) Elsevier Commodity markets Elsevier Non-stationary principal component analysis (NSPCA) Elsevier The CSI300 index Elsevier Pearson correlation coefficient Elsevier Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:529 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.physa.2019.121534 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 529 2019 1 0901 0 |
| spelling |
10.1016/j.physa.2019.121534 doi GBV00000000000654.pica (DE-627)ELV047126000 (ELSEVIER)S0378-4371(19)30898-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zhu, Xiaoyu verfasserin aut Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. Detrended cross-correlation coefficient Elsevier Detrended cross-correlation analysis (DCCA) Elsevier Commodity markets Elsevier Non-stationary principal component analysis (NSPCA) Elsevier The CSI300 index Elsevier Pearson correlation coefficient Elsevier Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:529 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.physa.2019.121534 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 529 2019 1 0901 0 |
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10.1016/j.physa.2019.121534 doi GBV00000000000654.pica (DE-627)ELV047126000 (ELSEVIER)S0378-4371(19)30898-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zhu, Xiaoyu verfasserin aut Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. Detrended cross-correlation coefficient Elsevier Detrended cross-correlation analysis (DCCA) Elsevier Commodity markets Elsevier Non-stationary principal component analysis (NSPCA) Elsevier The CSI300 index Elsevier Pearson correlation coefficient Elsevier Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:529 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.physa.2019.121534 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 529 2019 1 0901 0 |
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10.1016/j.physa.2019.121534 doi GBV00000000000654.pica (DE-627)ELV047126000 (ELSEVIER)S0378-4371(19)30898-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zhu, Xiaoyu verfasserin aut Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. Detrended cross-correlation coefficient Elsevier Detrended cross-correlation analysis (DCCA) Elsevier Commodity markets Elsevier Non-stationary principal component analysis (NSPCA) Elsevier The CSI300 index Elsevier Pearson correlation coefficient Elsevier Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:529 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.physa.2019.121534 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 529 2019 1 0901 0 |
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10.1016/j.physa.2019.121534 doi GBV00000000000654.pica (DE-627)ELV047126000 (ELSEVIER)S0378-4371(19)30898-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zhu, Xiaoyu verfasserin aut Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) 2019transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. Detrended cross-correlation coefficient Elsevier Detrended cross-correlation analysis (DCCA) Elsevier Commodity markets Elsevier Non-stationary principal component analysis (NSPCA) Elsevier The CSI300 index Elsevier Pearson correlation coefficient Elsevier Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:529 year:2019 day:1 month:09 pages:0 https://doi.org/10.1016/j.physa.2019.121534 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 529 2019 1 0901 0 |
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Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA) |
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The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. |
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The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. |
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The categorization of high dimensional data present a fascinating challenge to statistical models as frequent number of highly correlated dimensions or attributes can affect the accuracy of model. When the underlying time series present non-stationarity, typically when the series are contaminated by external trends, the strength of cross-correlations among variables is often overestimated or underestimated, and therefore, the traditional PCA method fails to rely on reliable cross-correlations to guide for the linear transformation of original variables. In this study, we apply the non-stationary principal component analysis (NSPCA) introduced by Zhao and Shang (2015) to analysis cross-correlations between the CSI300 Index and commodity markets. The main result of this paper is that for commodity markets, utilization of the non-stationary principal component analysis (NSPCA) can improve the efficiency, with more proportion of total population variance (information) due to an equal number of principal components than the traditional PCA. Meanwhile, as the increasing of time scales, this efficiency of NSPCA has a trend to increase. To check that whether the first several principal components can preserve some important information of original data or not, we apply detrended cross-correlation analysis (DCCA) to examine cross-correlation relationship between the CSI300 Index and the selected first five principal components and conclude that the information of long-range dependence measured by Hurst exponent has not been lost. |
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