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Random Matrix Theory and Cross-correlations in Global Financial Indices and Local Stock Market Indices

机译:随机矩阵理论与全球金融指数的互相关   和本地股市指数

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摘要

We analyzed cross-correlations between price fluctuations of global financialindices (20 daily stock indices over the world) and local indices (dailyindices of 200 companies in the Korean stock market) by using random matrixtheory (RMT). We compared eigenvalues and components of the largest and thesecond largest eigenvectors of the cross-correlation matrix before, during, andafter the global financial the crisis in the year 2008. We find that themajority of its eigenvalues fall within the RMT bounds [{\lambda}_,{\lambda}+], where {\lambda}_- and {\lambda}_+ are the lower and the upperbounds of the eigenvalues of random correlation matrices. The components of theeigenvectors for the largest positive eigenvalues indicate the identicalfinancial market mode dominating the global and local indices. On the otherhand, the components of the eigenvector corresponding to the second largesteigenvalue are positive and negative values alternatively. The componentsbefore the crisis change sign during the crisis, and those during the crisischange sign after the crisis. The largest inverse participation ratio (IPR)corresponding to the smallest eigenvector is higher after the crisis thanduring any other periods in the global and local indices. During the globalfinancial the crisis, the correlations among the global indices and among thelocal stock indices are perturbed significantly. However, the correlationsbetween indices quickly recover the trends before the crisis.
机译:我们使用随机矩阵理论(RMT)分析了全球金融指数(全球20个日股指数)的价格波动与本地指数(韩国股票市场中200家公司的日指数)之间的相互关系。我们比较了2008年全球金融危机爆发之前,之中和之后互相关矩阵的最大特征向量和第二大特征向量的特征值。我们发现其大多数特征值都在RMT范围内[{\ lambda} _,{\ lambda} +],其中{\ lambda} _-和{\ lambda} _ +是随机相关矩阵特征值的下限和上限。最大正特征值的特征向量的分量表示主导全球和本地指数的相同金融市场模式。另一方面,对应于第二最大特征值的特征向量的分量交替地为正值和负值。在危机期间,危机变化之前的组件在危机期间标志,在危机变化之后的组件在危机之后。危机后,与最小特征向量相对应的最大逆参与比(IPR)高于全球和本地指数中的任何其他时期。在全球金融危机期间,全球指数之间和本地股票指数之间的相关性受到显着干扰。然而,指数之间的相关性迅速恢复了危机之前的趋势。

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