英文标题：
《Detrended partial cross-correlation analysis of two nonstationary time
  series influenced by common external forces》
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作者：
Xi-Yuan Qian (ECUST), Ya-Min Liu (ECUST), Zhi-Qiang Jiang (ECUST),
  Boris Podobnik (BU and ZSEM), Wei-Xing Zhou (ECUST), H. Eugene Stanley (BU)
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最新提交年份：
2015
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英文摘要：
  When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using classic detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross-correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multi-scale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross-correlation between crude oil and gold futures by taking into consideration the impact of the US dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the MF-DCCA method fails.
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中文摘要：
当公共因素强烈影响复杂自然或社会系统中记录的两个幂律互相关时间序列时，不考虑这些公共因素而使用经典的去趋势互相关分析（DCCA）会使结果产生偏差。我们使用去趋势部分互相关分析（DPXA）来揭示两个同时记录的时间序列之间的内在幂律互相关，在消除其他时间序列作为公共力的影响后，存在非平稳性。DPXA方法是考虑偏相关分析的去趋势互相关分析的推广。我们用含有分数布朗运动的二元分数布朗运动证明了该方法。我们发现，DPXA能够恢复分析的交叉赫斯特指数，因此多尺度DPXA系数是传统互相关系数的可行替代方案。通过分析受污染的二元分数布朗运动，我们证明了DPXA系数优于DCCA系数。考虑到美元指数的影响，我们计算了DPXA系数，并利用它们提取原油和黄金期货之间的内在相关性。为了推广DPXA方法并研究多重分形时间序列，我们发展了多重分形DPXA（MF-DPXA）方法。我们分析了被强白噪声掩盖的多重分形二项测度，发现MF-DPXA方法量化了隐藏的多重分形性质，而MF-DCCA方法失败。
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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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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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