英文标题：
《On clustering financial time series: a need for distances between
  dependent random variables》
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作者：
Gautier Marti, Frank Nielsen, Philippe Donnat, S\\\'ebastien Andler
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最新提交年份：
2016
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英文摘要：
  The following working document summarizes our work on the clustering of financial time series. It was written for a workshop on information geometry and its application for image and signal processing. This workshop brought several experts in pure and applied mathematics together with applied researchers from medical imaging, radar signal processing and finance. The authors belong to the latter group. This document was written as a long introduction to further development of geometric tools in financial applications such as risk or portfolio analysis. Indeed, risk and portfolio analysis essentially rely on covariance matrices. Besides that the Gaussian assumption is known to be inaccurate, covariance matrices are difficult to estimate from empirical data. To filter noise from the empirical estimate, Mantegna proposed using hierarchical clustering. In this work, we first show that this procedure is statistically consistent. Then, we propose to use clustering with a much broader application than the filtering of empirical covariance matrices from the estimate correlation coefficients. To be able to do that, we need to obtain distances between the financial time series that incorporate all the available information in these cross-dependent random processes.
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中文摘要：
以下工作文件总结了我们在金融时间序列聚类方面的工作。它是为信息几何学及其在图像和信号处理中的应用而编写的。这次研讨会邀请了几位纯数学和应用数学方面的专家，以及来自医学成像、雷达信号处理和金融领域的应用研究人员。作者属于后一类。本文是对金融应用（如风险或投资组合分析）中几何工具的进一步开发的一篇长篇介绍。事实上，风险和投资组合分析基本上依赖于协方差矩阵。除了高斯假设已知不准确外，协方差矩阵很难根据经验数据进行估计。为了从经验估计中过滤噪声，Mantegna提出了使用层次聚类的方法。在这项工作中，我们首先证明了这个过程在统计学上是一致的。然后，我们建议使用比从估计相关系数中过滤经验协方差矩阵更广泛的聚类应用。为了做到这一点，我们需要获得金融时间序列之间的距离，这些时间序列包含这些相互依赖的随机过程中的所有可用信息。
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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：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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