英文标题：
《Finite sample properties of power-law cross-correlations estimators》
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作者：
Ladislav Kristoufek
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最新提交年份：
2014
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英文摘要：
We study finite sample properties of estimators of power-law cross-correlations -- detrended cross-correlation analysis (DCCA), height cross-correlation analysis (HXA) and detrending moving-average cross-correlation analysis (DMCA) -- with a special focus on short-term memory bias as well as power-law coherency. Presented broad Monte Carlo simulation study focuses on different time series lengths, specific methods\' parameter setting, and memory strength. We find that each method is best suited for different time series dynamics so that there is no clear winner between the three. The method selection should be then made based on observed dynamic properties of the analyzed series.
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中文摘要：
我们研究幂律互相关估计量的有限样本性质——去趋势互相关分析（DCCA）、高度互相关分析（HXA）和去趋势移动平均互相关分析（DMCA）——特别关注短期记忆偏差和幂律相关性。提出了广泛的蒙特卡罗模拟研究，重点是不同的时间序列长度、特定方法的参数设置和记忆强度。我们发现，每种方法最适合不同的时间序列动力学，因此三者之间没有明显的赢家。然后，应根据所分析序列的观测动态特性选择方法。
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分类信息：

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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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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