Correlation structure and principal components in the global crude oil market

Yue-Hua Dai, Wen-Jie Xie, Zhi-Qiang Jiang, George J. Jiang () and Wei-Xing Zhou
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Yue-Hua Dai: East China University of Science and Technology
Wen-Jie Xie: East China University of Science and Technology
Zhi-Qiang Jiang: East China University of Science and Technology
George J. Jiang: Washington State University

Empirical Economics, 2016, vol. 51, issue 4, No 10, 1519 pages

Abstract: Abstract 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. doi: 10.5547/ISSN0195-6574-EJ-Vol12-No3-7 , 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.

Keywords: Crude oil; Principal component analysis; Correlation structure; Regionalization; Geographical information; Eigenvalue (search for similar items in EconPapers)
JEL-codes: C15 G1 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (28)

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DOI: 10.1007/s00181-015-1057-1

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