Cross-correlations between the CSI300 index and commodity markets: Non-stationary principal component analysis (NSPCA)

Xiaoyu Zhu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 529, issue C

Abstract: 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.

Keywords: Pearson correlation coefficient; Detrended cross-correlation coefficient; Non-stationary principal component analysis (NSPCA); Detrended cross-correlation analysis (DCCA); Commodity markets; The CSI300 index (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119308982
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:529:y:2019:i:c:s0378437119308982

DOI: 10.1016/j.physa.2019.121534

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().