Correlation structure and principal components in the global crude oil market

Dai, Yue-Hua; Xie, Wen-Jie; Jiang, Zhi‐Qiang; Jiang, George J.; Zhou, Wei‐Xing

doi:10.1007/s00181-015-1057-1

Cited by 40 publications

(20 citation statements)

References 41 publications

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“…There are a few components whose magnitudes are significantly greater than the averages. However, we did not observe solid evidence that the correlations of the corresponding return time series of these components are among the largest, which is different from the U.S. stock market [17] and the global crude oil market [38]. We investigated the relationship between the eigenvector components and stocks' market capitalizations.…”

Section: Belowmentioning

confidence: 77%

“…The characteristic of a market effect is the eigenvector u 1 of the largest eigenvalue λ 1 has roughly equal components on all of the N stocks, showing a nice linear relationship between the returns of the eigenportfolio constructed from u 1 and of the market index [17]. Usually, other deviating eigenvalues do not reflect a market effect but the comovement of stocks in the same industrial sector [17], the same traits shared by stocks [34], or geographic localization [38]. However, it is also possible that other deviating eigenvalues also reflect a market effect, such as the USA housing market [18].…”

Section: Belowmentioning

confidence: 99%

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Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

et al. 2017

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-We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 stocks listed on the Shanghai and Shenzhen stock exchanges. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks' capitalizations.Introduction. -Financial markets evolve in a selforganized manner with the interacting elements forming complex networks at different levels, including international markets [1][2][3][4], individual markets [5][6][7][8], and security trading networks [9][10][11][12][13][14][15][16]. There are well-documented stylized facts of stock return time series within individual markets unveiled by the random matrix theory (RMT) analysis [6,17]: (1) The largest eigenvalue reflects the market effect such that its eigenportfolio returns are strongly correlated with the market returns; (2) Other largest eigenvalues contain information of industrial sectors; and (3) The smallest eigenvalues embed stock pairs with large correlations. However, for stock exchange index returns [1] and housing markets [18,19], the largest eigenvalues can be used to extract geographic traits. Moreover, the signs of eigenvector components contain information of local interactions [18,20,21].

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Section: Belowmentioning

confidence: 77%

Section: Belowmentioning

confidence: 99%

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

et al. 2017

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“…Indeed, the oil return risks could inhibit current investment for crude oil [5]. Changes in market stability have considerable impacts on economy [14][15][16][17][18]. First, changes in crude oil return risks essentially impact decisions made by market participants, such as oil producers, consumers, and policy makers.…”

Section: Opecmentioning

confidence: 99%

The Oil Market Reactions to OPEC’s Announcements

2019

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Because of the crucial implications of the market power of OPEC, the aim of this paper was to investigate the oil asymmetric market reactions, such as the price and risk reactions, to OPEC’s announcements. Specifically, this paper first explored the oil price reactions to OPEC’s announcements and their heterogeneity to depict the directional role of OPEC based on event study methodology. Furthermore, this paper analyzed the oil risk reactions in the framework of a linear model. Our findings reveal several key results. The oil price reactions to OPEC production decisions behave quite heterogeneously in three kinds of decisions. Specifically, the reaction to announcements of a production increase shows an invert “U” shape, whereas there is a linear effect of cut announcements. Otherwise, when a maintain decision is announced, the oil prices have no obvious change over the sample period. Additionally, the oil risk reactions to OPEC’s announcements are heavily related to the interaction item between OPEC decisions and its production over full sample periods. Furthermore, OPEC’s role in promoting stability in crude oil markets by changing its production shows a heterogeneous condition after global financial crisis.

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“…Kenett et al (2010) constructed a network using the partial correlation coefficient method as a global equity information and found that the financial sector, particularly the investment services, is the most influential stock. Zhou, Mu, and Kertész (2012), Meng et al (2014), Meng, Xie, and Zhou (2015), Qian et al (2015), and Dai, Xie, Jiang, Jiang, and Zhou (2016) used the stochastic matrix method as the global equity information and analysed its structural characteristics. Tu (2014) used Engle-Granger two-step method to build the edge information of stock network and analysed its structural characteristics.…”

Section: Introductionmentioning

confidence: 99%

Tail dependence networks of global stock markets

2018

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The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe–Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail‐dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper‐ and lower‐tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets.

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Correlation structure and principal components in the global crude oil market

Cited by 40 publications

References 41 publications

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

The Oil Market Reactions to OPEC’s Announcements

Tail dependence networks of global stock markets

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