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摘要翻译：
应用平面高斯图解展开的随机矩阵理论方法，首先对最一般的多元高斯系统，然后对七种特殊的真协方差模型，以主方程的形式求出厄米等时和非厄米时滞互协方差估计量的平均谱密度。对于其中最简单的一个模型，证明了已有的结果是不正确的，给出了正确的结果，并将其推广到指数加权移动平均估计以及两个非高斯分布Student t和free Levy。本文围绕金融复杂系统的应用展开，其结果构成了对存在于其中的真实相关性的敏感探索。
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英文标题：
《Hermitian and non-Hermitian covariance estimators for multivariate
Gaussian and non-Gaussian assets from random matrix theory》
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作者：
Andrzej Jarosz
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最新提交年份：
2012
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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：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
The random matrix theory method of planar Gaussian diagrammatic expansion is applied to find the mean spectral density of the Hermitian equal-time and non-Hermitian time-lagged cross-covariance estimators, firstly in the form of master equations for the most general multivariate Gaussian system, secondly for seven particular toy models of the true covariance function. For the simplest one of these models, the existing result is shown to be incorrect and the right one is presented, moreover its generalizations are accomplished to the exponentially-weighted moving average estimator as well as two non-Gaussian distributions, Student t and free Levy. The paper revolves around applications to financial complex systems, and the results constitute a sensitive probe of the true correlations present there.
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PDF链接：
https://arxiv.org/pdf/1010.2981
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