文献类型：期刊文献

英文题名：Correlation structure and principal components in the global crude oil market

作者：Dai, Yue-Hua[1];Xie, Wen-Jie[1];Jiang, Zhi-Qiang[1];Jiang, George J.[2];Zhou, Wei-Xing[1,3]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]Washington State Univ, Dept Finance & Management Sci, Pullman, WA 99164 USA;[3]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2016

卷号：51

期号：4

起止页码：1501

外文期刊名：EMPIRICAL ECONOMICS

收录：;WOS:【SSCI(收录号:WOS:000387345900010)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 11075054), the Shanghai (Follow-up) Rising Star Program (Grant No. 11QH1400800), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Crude oil; Principal component analysis; Correlation structure; Regionalization; Geographical information; Eigenvalue

摘要：The correlation structure of the global crude oil market is investigated using the daily returns of 71 oil price time series across the world from 1992 to 2012. We identify from the correlation matrix six clusters of time series exhibiting evident geographical traits, which supports Weiner's (Energy J 12:95-107.1991) regionalization hypothesis of the global oil market. We find that intra-cluster pairs of time series are highly correlated, while inter-cluster pairs have relatively low correlations. Principal component analysis shows that most eigenvalues of the correlation matrix locate outside the prediction of the random matrix theory and these deviating eigenvalues and their corresponding eigenvectors contain rich economic information. Specifically, the largest eigenvalue reflects a collective effect of the global market, the other four largest eigenvalues possess a partitioning function to distinguish the six clusters, and the smallest eigenvalues highlight the pairs of time series with the largest correlation coefficients. We construct an index of the global oil market based on the eigenportfolio of the largest eigenvalue, which evolves similarly as the average price time series and has better performance than the benchmark 1 / N portfolio under the buy-and-hold strategy.