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

DA Tingting; ZHANG Shuguang; DA Cheng

doi:10.3969/j.issn.0253-2778.2015.08.010

PDF( 877 KB)

Open Access JUSTC

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:

https://doi.org/10.3969/j.issn.0253-2778.2015.08.010

More Information

Corresponding author: DA Tingting (corresponding author), female, born in 1991, master. Research field: statistical finance.
Received Date: 10 October 2014
Rev Recd Date: 11 May 2015
Publish Date: 31 August 2015

Abstract Full text PDF

Abstract

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.

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.