英文标题：
《Spurious seasonality detection: a non-parametric test proposal》
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作者：
Aurelio F. Bariviera, Angelo Plastino, George Judge
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最新提交年份：
2018
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英文摘要：
  This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called \"day-of-the-week\" effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. Whereas time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non parametric test, it requires no assumptions about the distribution of returns so that it could be a practical alternative to conventional econometric tests. We made also an exhaustive application of the here proposed technique to 83 stock indices around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.
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中文摘要：
本文对星期日效应给出了一个一般而全面的定义。使用符号动力学，我们开发了一个基于顺序模式的独特测试，以检测它。这项测试揭示了一个事实，即所谓的“一周中的某一天”效应在一定程度上是数据隐藏相关结构的产物。我们还提出了基于人工时间序列的仿真。长记忆时间序列容易表现出日季节性，而纯白噪声信号则不表现出模式偏好。由于我们的测试是非参数测试，它不需要对收益分布进行任何假设，因此它可以成为传统计量经济学测试的一种实用替代方法。我们还对全球83个股指进行了详尽的应用。最后，本文强调了符号分析在经济时间序列研究中的相关性。
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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：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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