Using Detrended Cross-Correlation Analysis in geophysical data

Marinho, Euler B. S.; Sousa, Arthur Matsuo Yamashita Rios de; Andrade, Roberto F. S.

doi:10.1016/j.physa.2012.12.038

Cited by 60 publications

(26 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Analyzing fractal properties of time series has been a significant contribution of physics to a broad range of other disciplines [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]. In time series, fractal properties translate directly into specific correlation structures.…”

Section: Introductionmentioning

confidence: 99%

Fractal approach towards power-law coherency to measure cross-correlations between time series

Krištoufek

2017

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

We focus on power-law coherency as an alternative approach towards studying powerlaw cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter H ρ based on popular techniques usually utilized for studying power-law cross-correlationsdetrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least 10 4 observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.

show abstract

Section: Introductionmentioning

confidence: 99%

Fractal approach towards power-law coherency to measure cross-correlations between time series

Krištoufek

2017

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

show abstract

“…The DCCA cross-correlation coefficient ρ DCCA has been applied in many areas, such as meteorology, 31,32 economics, 33,34 traffic 35 and physics. 36 …”

Section: Dcca Cross-correlation Coefficientmentioning

confidence: 99%

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

Cao

Han

2014

Int. J. Mod. Phys. B

View full text Add to dashboard Cite

Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole. PACS number: 89.65.Gh 1450236-1 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-3 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-4 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-6 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only.

show abstract

“…Power-law cross-correlations have brought a new perspective into analysis of bivariate time series with applications across a wide range of disciplines -hydrology [1], (hydro)meteorology [2,3], seismology and geophysics [4,5], economics and finance [6][7][8][9][10][11][12], biometrics [13], biology [14], DNA sequences [15], neuroscience [16], electricity [17], traffic [18][19][20][21], and others. Analyzing cross-correlations between a pair of time series brings new insights into their dynamics and specifically, the power-law behavior of these may suggest very special features compared to exponential (vanishing) cross-correlations [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Power-law cross-correlations estimation under heavy tails

Krištoufek

2016

Communications in Nonlinear Science and Numerical Simulation

View full text Add to dashboard Cite

We examine the performance of six estimators of the power-law cross-correlations -the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the crossperiodogram estimator and the local cross-Whittle estimator -under heavy-tailed distributions. The selection of estimators allows to separate these into the time and frequency domain estimators. By varying the characteristic exponent of the α-stable distributions which controls the tails behavior, we report several interesting findings. First, the frequency domain estimators are practically unaffected by heavy tails bias-wise. Second, the time domain estimators are upward biased for heavy tails but they have lower estimator variance than the other group for short series. Third, specific estimators are more appropriate depending on distributional properties and length of the analyzed series. In addition, we provide a discussion of implications of these results for empirical applications as well as theoretical explanations.

show abstract

Using Detrended Cross-Correlation Analysis in geophysical data

Cited by 60 publications

References 28 publications

Fractal approach towards power-law coherency to measure cross-correlations between time series

Fractal approach towards power-law coherency to measure cross-correlations between time series

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

Power-law cross-correlations estimation under heavy tails

Contact Info

Product

Resources

About