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摘要翻译：
研究了金融相关矩阵特征值谱的一些性质。特别是，我们研究了在经验中观察到的大特征值膨胀的性质，这些膨胀通常被认为是金融数据中包含的大量噪声的结果。我们通过对两个数据集的经验相关矩阵进行过滤来挑战这一常识，该过滤过程突出了它们所包含的一些聚类结构，并分析了这种过滤对特征值谱的后果。我们表明，经验上观察到的特征值大块是较小结构的叠加，而较小结构又是股票之间相互关联的结果。我们用因子模型来解释和证实这些发现，并将经验谱与随机矩阵理论预测的谱进行比较。
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英文标题：
《The fine structure of spectral properties for random correlation
matrices: an application to financial markets》
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作者：
G. Livan, S. Alfarano, E. Scalas
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最新提交年份：
2011
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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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英文摘要：
We study some properties of eigenvalue spectra of financial correlation matrices. In particular, we investigate the nature of the large eigenvalue bulks which are observed empirically, and which have often been regarded as a consequence of the supposedly large amount of noise contained in financial data. We challenge this common knowledge by acting on the empirical correlation matrices of two data sets with a filtering procedure which highlights some of the cluster structure they contain, and we analyze the consequences of such filtering on eigenvalue spectra. We show that empirically observed eigenvalue bulks emerge as superpositions of smaller structures, which in turn emerge as a consequence of cross-correlations between stocks. We interpret and corroborate these findings in terms of factor models, and and we compare empirical spectra to those predicted by Random Matrix Theory for such models.
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PDF链接：
https://arxiv.org/pdf/1102.4076
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