  This article investigates the correlation structure of the global crude oil
market 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 (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, 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 eigenfortfolio 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.
