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The MFCCA algorithm and its application in financial market: A new view of multifractal extension of DCCA

  • 1.

    Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China

  • 2.

    Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China

Cite this: JUSTC, 2015, 45(8): 683-691
https://doi.org/10.3969/j.issn.0253-2778.2015.08.010
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  • Corresponding author:

    DA Tingting (corresponding author), female, born in 1991, master. Research field: statistical finance.

  • Received Date: October 09, 2014
  • Revised Date: May 10, 2015
  • Published Date: August 30, 2015
    Abstract
  • Multifractal extension of detrended cross-correlation analysis (DCCA) usually involves the trouble that the computation of arbitrary powers of the negative cross-covariances leads to complex values. However, a commonly adopted modulus processing method MFDXA often indicates significant multifractal cross-correlation signal when actually no fractality exists. Mulitfractal cross-correlation analysis (MFCCA) proposed by Os′wiecimka preserves the sign of the cross-covariances and settles the trouble above. MFCCA is a natural general extension of MFDFA and DCCA. Here it was demonstrated that MFCCA performs more effectively and powerfully than MFDXA from the view of the general two-component ARFIMA processes model. MFCCA can correctly identify the signal of multifractality behavior and show sensitivity to the varying of the weight parameter W.
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