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摘要翻译：
本文发现，股票市场收益率的剩余噪声存在一种新的相关结构，这种结构与驱动收益率的前几个显著因素的组成和稳定性密切相关，而且噪声带是由多个子带组成的，这些子带不是完全混合的。我们的发现使我们能够构造有效的广义随机矩阵理论市场模型，这些模型与相关性和特征向量聚类密切相关。我们展示了如何使用这些模型在一个模拟中合并了重尾。最后，我们演示了如何在传统的清洁处方中产生微妙的纯平稳风险估计偏差。
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英文标题：
《Hidden Noise Structure and Random Matrix Models of Stock Correlations》
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作者：
Ivailo I. Dimov, Petter N. Kolm, Lee Maclin, and Dan Y. C. Shiber
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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：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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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：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 find a novel correlation structure in the residual noise of stock market returns that is remarkably linked to the composition and stability of the top few significant factors driving the returns, and moreover indicates that the noise band is composed of multiple subbands that do not fully mix. Our findings allow us to construct effective generalized random matrix theory market models that are closely related to correlation and eigenvector clustering. We show how to use these models in a simulation that incorporates heavy tails. Finally, we demonstrate how a subtle purely stationary risk estimation bias can arise in the conventional cleaning prescription.
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PDF链接：
https://arxiv.org/pdf/0909.1383
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