Multiscale multifractal detrended cross-correlation analysis of financial time series

Wenbin Shi, Pengjian Shang, Jing Wang and Aijing Lin

Physica A: Statistical Mechanics and its Applications, 2014, vol. 403, issue C, 35-44

Abstract: In this paper, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). The method allows us to extend the description of the cross-correlation properties between two time series. MM-DCCA may provide new ways of measuring the nonlinearity of two signals, and it helps to present much richer information than multifractal detrended cross-correlation analysis (MF-DCCA) by sweeping all the range of scale at which the multifractal structures of complex system are discussed. Moreover, to illustrate the advantages of this approach we make use of the MM-DCCA to analyze the cross-correlation properties between financial time series. We show that this new method can be adapted to investigate stock markets under investigation. It can provide a more faithful and more interpretable description of the dynamic mechanism between financial time series than traditional MF-DCCA. We also propose to reduce the scale ranges to analyze short time series, and some inherent properties which remain hidden when a wide range is used may exhibit perfectly in this way.

Keywords: Multiscale analysis; Multifractal analysis; Detrended cross-correlation analysis; Financial time series (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (35)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:403:y:2014:i:c:p:35-44

DOI: 10.1016/j.physa.2014.02.023

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