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摘要翻译：
为了探讨金融相关矩阵与传统随机矩阵理论之间的关系，本文分析了股票市场相关矩阵的特征，如特征值的分布、收益率符号间的互相关、波动率互相关以及主值的多重分形特征。结果表明，股票市场的动态不是简单地分解为“市场”、“板块”和Wishart随机批量。当用于构造相关矩阵的时间序列足够长，从而抑制测量噪声时，可以清楚地看到这一点。相反，一个层次复杂和高度非线性的市场组织出现了，并表明整个市场的相关信息已经编码在其组成部分中。
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英文标题：
《Empirics versus RMT in financial cross-correlations》
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作者：
S. Drozdz, J. Kwapien, P. Oswiecimka
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最新提交年份：
2007
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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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英文摘要：
In order to pursue the issue of the relation between the financial cross-correlations and the conventional Random Matrix Theory we analyse several characteristics of the stock market correlation matrices like the distribution of eigenvalues, the cross-correlations among signs of the returns, the volatility cross-correlations, and the multifractal characteristics of the principal values. The results indicate that the stock market dynamics is not simply decomposable into 'market', 'sectors', and the Wishart random bulk. This clearly is seen when the time series used to construct the correlation matrices are sufficiently long and thus the measurement noise suppressed. Instead, a hierarchically convoluted and highly nonlinear organization of the market emerges and indicates that the relevant information about the whole market is encoded already in its constituents.
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PDF链接：
https://arxiv.org/pdf/0711.0644
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