英文标题：
《Fractal approach towards power-law coherency to measure
cross-correlations between time series》
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作者：
Ladislav Kristoufek
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最新提交年份：
2017
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英文摘要：
We focus on power-law coherency as an alternative approach towards studying power-law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter $H_{\\rho}$ based on popular techniques usually utilized for studying power-law cross-correlations -- detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least $10^4$ observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.
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中文摘要：
我们关注幂律相干性，作为研究同时记录的时间序列之间幂律互相关的另一种方法。为了能够研究经验数据，我们引入了三种幂律相干参数的估计量$H\\u{\\ rho}$，它们基于研究幂律互相关常用的技术——去趋势互相关分析（DCCA）、去趋势移动平均互相关分析（DMCA）和高度互相关分析（HXA）。在有限样本性质研究中，我们关注估计量的偏差、方差和均方误差。我们发现，基于DMCA的方法是三种方法中最安全的选择。HXA方法对于具有至少10.4美元观测值的长时间序列是合理的，这在某些学科中很容易实现，但在其他学科中存在问题。基于DCCA的方法不能提供良好的性能，甚至会随着时间序列长度的增加而恶化。本文为研究时间序列之间的相互关系开辟了一个新的途径。
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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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