摘要翻译：
信息论为信息概念化和测量对象之间的关系提供了思想。它在科学中得到了广泛的应用，但令人惊讶的是，经济学和金融学对它的应用很少。我们表明时间序列数据可以作为信息进行有效的研究--通过注意到统计冗余和相关性之间的关系，我们可以利用信息论的结果构造随机变量联合相关性的检验。这个检验与Ryabko和Astola(2005，2006b，a)提出的检验具有相同的精神，但与这些检验不同的是，我们在原始随机过程的基础上增加了额外的随机性。它使用数据压缩来估计随机过程的熵率，这使得它能够测量随机变量集合之间的依赖性，而不是现有的计量经济学文献使用熵，并发现自己局限于依赖性的成对检验。我们展示了S&P500和PSI20股票收益率在不同样本周期和频率下如何检测到序列相关性。我们将该测试应用于合成数据，以判断其恢复已知时间依赖结构的能力。
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英文标题：
《An Information-Theoretic Test for Dependence with an Application to the
  Temporal Structure of Stock Returns》
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作者：
Galen Sher, Pedro Vitoria
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最新提交年份：
2013
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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：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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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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英文摘要：
  Information theory provides ideas for conceptualising information and measuring relationships between objects. It has found wide application in the sciences, but economics and finance have made surprisingly little use of it. We show that time series data can usefully be studied as information -- by noting the relationship between statistical redundancy and dependence, we are able to use the results of information theory to construct a test for joint dependence of random variables. The test is in the same spirit of those developed by Ryabko and Astola (2005, 2006b,a), but differs from these in that we add extra randomness to the original stochatic process. It uses data compression to estimate the entropy rate of a stochastic process, which allows it to measure dependence among sets of random variables, as opposed to the existing econometric literature that uses entropy and finds itself restricted to pairwise tests of dependence. We show how serial dependence may be detected in S&P500 and PSI20 stock returns over different sample periods and frequencies. We apply the test to synthetic data to judge its ability to recover known temporal dependence structures.
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PDF链接：
https://arxiv.org/pdf/1304.0353