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摘要翻译：
通过比较金融协方差矩阵与随机矩阵预测的特征值分布和水平间距来检测普适性特征。设计了一个斩波过程，以便从单个金融数据集实例中产生资产-价格协方差的统计集合。对于最小特征值和单个间隔的局部结果在重新洗牌时间窗口和资产时是非常稳定的。它们分别与普遍的Tracy-Widom分布和Wigner推测相吻合。这表明有很强的稳健性，尤其是在光谱的低层部分，这与投资组合选择最相关。相反，单个协方差矩阵的全局谱密度以及所有展开的最近邻间距分布的平均值偏离标准高斯随机矩阵预测。这些数据与最近引入的广义随机矩阵模型相当一致，相关性显示出幂律衰减。
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英文标题：
《Universal Correlations and Power-Law Tails in Financial Covariance
Matrices》
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作者：
Gernot Akemann, Jonit Fischmann and Pierpaolo Vivo
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最新提交年份：
2009
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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：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
Signatures of universality are detected by comparing individual eigenvalue distributions and level spacings from financial covariance matrices to random matrix predictions. A chopping procedure is devised in order to produce a statistical ensemble of asset-price covariances from a single instance of financial data sets. Local results for the smallest eigenvalue and individual spacings are very stable upon reshuffling the time windows and assets. They are in good agreement with the universal Tracy-Widom distribution and Wigner surmise, respectively. This suggests a strong degree of robustness especially in the low-lying sector of the spectra, most relevant for portfolio selections. Conversely, the global spectral density of a single covariance matrix as well as the average over all unfolded nearest-neighbour spacing distributions deviate from standard Gaussian random matrix predictions. The data are in fair agreement with a recently introduced generalised random matrix model, with correlations showing a power-law decay.
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PDF链接：
https://arxiv.org/pdf/0906.5249
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