英文标题：
《Power-law cross-correlations estimation under heavy tails》
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作者：
Ladislav Kristoufek
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最新提交年份：
2016
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英文摘要：
We examine the performance of six estimators of the power-law cross-correlations -- the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the cross-periodogram estimator and the local cross-Whittle estimator -- under heavy-tailed distributions. The selection of estimators allows to separate these into the time and frequency domain estimators. By varying the characteristic exponent of the $\\alpha$-stable distributions which controls the tails behavior, we report several interesting findings. First, the frequency domain estimators are practically unaffected by heavy tails bias-wise. Second, the time domain estimators are upward biased for heavy tails but they have lower estimator variance than the other group for short series. Third, specific estimators are more appropriate depending on distributional properties and length of the analyzed series. In addition, we provide a discussion of implications of these results for empirical applications as well as theoretical explanations.
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中文摘要：
我们研究了幂律互相关的六个估计量——去趋势互相关分析、去趋势移动平均互相关分析、高度互相关分析、平均周期图估计量、互周期图估计量和局部互惠特尔估计量——在重尾分布下的性能。估计器的选择允许将其分为时域估计器和频域估计器。通过改变控制尾部行为的$\\alpha$稳定分布的特征指数，我们报告了几个有趣的发现。首先，频域估计器实际上不受重尾偏差的影响。第二，对于重尾，时域估计是向上偏置的，但对于短序列，它们的估计方差比另一组低。第三，根据分析序列的分布性质和长度，具体的估计更合适。此外，我们还讨论了这些结果对实证应用和理论解释的影响。
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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：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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