期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 442, 期 -, 页码 82-90出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.physa.2015.08.063
关键词
Empirical mode decomposition; Ensemble empirical mode decomposition; DCCA cross-correlation coefficient; Stock market
资金
- China National Science [61071142, 61371130]
- Beijing National Science [4122059]
Empirical mode decomposition (EMD) is a data-driven signal analysis method for nonlinear and nonstationary data. Since it is intuitive, direct, posterior and adaptive, EMD is widely applied to various fields of study. In this paper, EMD and ensemble empirical mode decomposition (EEMD), a modified method of EMD, are applied to financial time series. Through analyzing the intrinsic mode functions (IMFs) of EMD and EEMD, we find EEMD method performs better on the orthogonality of IMFs than EMD. With clustering the ordered frequencies of IMFs, the IMFs obtained from EEMD method are grouped into high-, medium-, and low-frequency components, representing the short-, medium-, and long-term volatilities of the index sequences, respectively. With the cross-correlation analysis of DCCA cross-correlation coefficient, our findings allow us to gain further and detailed insight into the cross-correlations of stock markets. (C) 2015 Elsevier B.V. All rights reserved.
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