英文标题：
《Correlation structure and principal components in global crude oil
  market》
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作者：
Yue-Hua Dai (ECUST), Wen-Jie Xie (ECUST), Zhi-Qiang Jiang (ECUST),
  George J. Jiang (WSU), Wei-Xing Zhou (ECUST)
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最新提交年份：
2014
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英文摘要：
  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.
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中文摘要：
本文利用1992年至2012年全球71个石油价格时间序列的日收益率，研究了全球原油市场的相关结构。我们从相关矩阵中识别出六组具有明显地理特征的时间序列，这支持韦纳（1991）的全球石油市场区域化假设。我们发现，时间序列的簇内对具有高度相关性，而簇间对具有相对较低的相关性。主成分分析表明，相关矩阵的大多数特征值位于随机矩阵理论的预测之外，这些偏离特征值及其对应的特征向量包含丰富的经济信息。具体来说，最大特征值反映了全球市场的集体效应，其他四个最大特征值具有区分六个集群的分配函数，最小特征值突出了具有最大相关系数的时间序列对。我们构建了一个基于最大特征值特征值的全球石油市场指数，该指数与平均价格时间序列演化相似，在买入持有策略下，其表现优于基准1/N美元投资组合。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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