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中国股票市场交叉相关性的动态演变。

Dynamic evolution of cross-correlations in the Chinese stock market.

作者信息

Ren Fei, Zhou Wei-Xing

机构信息

School of Business, East China University of Science and Technology, Shanghai, China; School of Science, East China University of Science and Technology, Shanghai, China; Research Center for Econophysics, East China University of Science and Technology, Shanghai, China.

出版信息

PLoS One. 2014 May 27;9(5):e97711. doi: 10.1371/journal.pone.0097711. eCollection 2014.

Abstract

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management.

摘要

互相关分析被广泛应用于理解股票市场的相互联系以及投资组合风险估计。目前对中国市场相关性的研究主要集中在收益序列之间的静态相关性,因此迫切需要研究它们的动态相关性。我们的研究旨在揭示中国股票市场互相关的动态演变,并对演变行为给出准确解释。由1999年1月4日至2011年12月30日在上海证券交易所交易的367只A股股票的收益序列构建的相关矩阵,是在一个大小为400天的移动窗口上计算的。仔细分析了相关矩阵的相关系数、特征值和特征向量的统计性质的演变。我们发现,在2001年和2008年的两次市场崩溃期间,股票相关性显著增加,在此期间只有五个特征值显著偏离随机相关矩阵,并且在这些波动时期的系统性风险高于平静时期。通过研究不同时间段偏离特征向量的重要贡献者,我们观察到信息技术、电子和房地产等商业部门的动态演变行为,这些部门在崩溃之前(之后)引领上涨(下跌)。我们的结果为理解中国股票市场互相关的动态演变提供了新的视角,并且风险估计结果对风险管理的应用具有重要价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aee1/4035345/691e5c15846a/pone.0097711.g001.jpg

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本文引用的文献

1
Scaling and volatility of breakouts and breakdowns in stock price dynamics.
PLoS One. 2013 Dec 23;8(12):e82771. doi: 10.1371/journal.pone.0082771. eCollection 2013.
2
An exotic long-term pattern in stock price dynamics.
PLoS One. 2012;7(12):e51666. doi: 10.1371/journal.pone.0051666. Epub 2012 Dec 17.
3
Changes in cross-correlations as an indicator for systemic risk.
Sci Rep. 2012;2:888. doi: 10.1038/srep00888. Epub 2012 Nov 26.
4
Correlation and network analysis of global financial indices.
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Aug;86(2 Pt 2):026101. doi: 10.1103/PhysRevE.86.026101. Epub 2012 Aug 2.
5
Risk-return relationship in a complex adaptive system.
PLoS One. 2012;7(3):e33588. doi: 10.1371/journal.pone.0033588. Epub 2012 Mar 30.
6
Evolvement of uniformity and volatility in the stressed global financial village.
PLoS One. 2012;7(2):e31144. doi: 10.1371/journal.pone.0031144. Epub 2012 Feb 8.
7
Temporal evolution of financial-market correlations.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Aug;84(2 Pt 2):026109. doi: 10.1103/PhysRevE.84.026109. Epub 2011 Aug 8.
8
Evolution of worldwide stock markets, correlation structure, and correlation-based graphs.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Aug;84(2 Pt 2):026108. doi: 10.1103/PhysRevE.84.026108. Epub 2011 Aug 5.
9
Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Apr;83(4 Pt 2):046121. doi: 10.1103/PhysRevE.83.046121. Epub 2011 Apr 25.
10
Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market.
PLoS One. 2010 Dec 20;5(12):e15032. doi: 10.1371/journal.pone.0015032.

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