Cross-correlation analysis of stock markets using EMD and EEMD

Mengjia Xu, Pengjian Shang and Aijing Lin

Physica A: Statistical Mechanics and its Applications, 2016, vol. 442, issue C, 82-90

Abstract: 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.

Keywords: Empirical mode decomposition; Ensemble empirical mode decomposition; DCCA cross-correlation coefficient; Stock market (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:442:y:2016:i:c:p:82-90

DOI: 10.1016/j.physa.2015.08.063

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