Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 573, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physa.2021.125920
Keywords
Partial cross-correlation; Multifractal temporally weighted detrended; partial cross-correlation analysis; Partial cross-correlation coefficient
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Funding
- National Natural Science Foundation of China [11871061]
- Collaborative Research project for Overseas Scholars, China (Hong Kong and Macau) of the National Natural Science Foundation of China [61828203]
- Chinese Program for Changjiang Scholars and Innovative Research Team in University, China (PCSIRT) [IRT_15R58]
- Hunan Provincial Innovation Foundation for Postgraduate, China [CX2017B265]
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This paper proposes a new method called multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify the intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors. Through application on simulated series and time series from stock markets, it is found that this method can more accurately detect cross-correlations.
When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. In order to better study the time series of such cases, we extend the multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group (Wei et al., 2017) and propose multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. To test the performance of MF-TWDPCCA, we apply it and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al. (2015) on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can more accurately detect the intrinsic cross-correlations for two simultaneously recorded series than MF-DPXA and MF-TWXDFA. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. In addition, a new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series. (C) 2021 Elsevier B.V. All rights reserved.
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