The effectiveness of the monetary policy in China: New evidence from long-range cross-correlation analysis and the components of multifractality

2018 ◽  
Vol 506 ◽  
pp. 1026-1037 ◽  
Author(s):  
Jing Qin ◽  
Jintian Ge ◽  
Xinsheng Lu
Keyword(s):  
Monetary Policy ◽  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Cross Correlation Analysis ◽  
New Evidence

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF CHINESE STOCK MARKETS BASED ON TIME DELAY

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 329-338 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
QIUYUE JIN
Keyword(s):  
Time Delay ◽  
Correlation Analysis ◽  
Long Range ◽  
Power Law ◽  
Spectrum Analysis ◽  
Cross Correlation ◽  
Analysis Time ◽  
Singularity Spectrum ◽  
Stock Indices ◽  
Cross Correlation Analysis

Multifractal detrended cross-correlation analysis (MF-DXA) has been developed to detect the long-range power-law cross-correlation of two simultaneous series. However, the synchronization of underlying data can not be guaranteed integrated by a variety of factors. We artificially imbed a time delay in considered series and study its influence on the multifractal cross-correlation analysis. Time delay is found to affect the multifractal characterization, where a larger time delay causes a weaker multifractality. We also propose an alternative modification on MF-DXA to make the process more robust. The logarithmic return and volatility of Chinese stock indices show cross-correlation scaling behavior and strong multifractality by MF-DXA as well as singularity spectrum analysis.

scholarly journals EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
You Gao ◽  
Nianpeng Wang
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Intrinsic Mode Functions ◽  
Nonstationary Signals ◽  
Seasonal Trends ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlated Signals ◽  
Emd Method

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

Projectile mass and energy dependence of multifractal detrended cross-correlation analysis of pseudorapidity space and azimuthal space in high energy nuclear collisions

2019 ◽  
Vol 28 (03) ◽  
pp. 1950012 ◽  
Author(s):  
Sucharita Chatterjee ◽  
Dipak Ghosh ◽  
Srimonti Dutta
Keyword(s):  
Experimental Data ◽  
Correlation Analysis ◽  
Long Range ◽  
Energy Dependence ◽  
Cross Correlation ◽  
High Energy ◽  
Long Range Correlations ◽  
Nuclear Collisions ◽  
Cross Correlation Analysis ◽  
The Cross

This paper studies the cross-correlation between the pseudorapidity and azimuthal distributions of the shower particles emitted in [Formula: see text]S-AgBr interactions at 200[Formula: see text]GeV and [Formula: see text]O-AgBr interactions at 60[Formula: see text]GeV applying Multifractal detrended cross-correlation analysis (MF-DXA) methodology. The cross-correlation between the pseudorapidity ([Formula: see text]) space and the azimuthal ([Formula: see text]) space is found to exhibit multifractality in case of both the interactions. The results obtained from the analysis of the experimental data were compared with those obtained for the randomly shuffled data for both the interactions, and the results revealed that the multifractality is due to the presence of long-range correlations. The study clearly indicates that the strength of the cross-correlation depends on both the projectile mass and energy.

scholarly journals Connectedness between crude oil and US equities: The impact of COVID-19 pandemic

2021 ◽  
Author(s):  
Aktham Maghyereh ◽  
hussein abdoh
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Moving Average ◽  
Equity Market ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Impact ◽  
Dynamical Connection

Abstract In this paper, we exploit multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the impact of COVID-19 pandemic on the cross-correlations between oil and US equity market (as represented by the S&P 500 index). First, we examine the detrended moving average cross-correlation coefficient between oil and S&P 500 returns before and during the COVID-19 pandemic. The correlation analysis shows that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long range cross correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equity in the long range and a deeper dynamical connection between oil and US equity markets as indicated by the multifractality tests.

Multiscale Detrended Cross-Correlation Analysis of Traffic Time Series Based on Empirical Mode Decomposition

2015 ◽  
Vol 14 (03) ◽  
pp. 1550023 ◽  
Author(s):  
Yi Yin ◽  
Pengjian Shang
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Long Range ◽  
Cross Correlation ◽  
Traffic Signals ◽  
Correlation Structure ◽  
Noise Effects ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cross Correlations

In this paper, we propose multiscale detrended cross-correlation analysis (MSDCCA) to detect the long-range power-law cross-correlation of considered signals in the presence of nonstationarity. For improving the performance and getting better robustness, we further introduce the empirical mode decomposition (EMD) to eliminate the noise effects and propose MSDCCA method combined with EMD, which is called MS-EDXA method, then systematically investigate the multiscale cross-correlation structure of the real traffic signals. We apply the MSDCCA and MS-EDXA methods to study the cross-correlations in three situations: velocity and volume on one lane, velocities on the present and the next moment and velocities on the adjacent lanes, and further compare their spectrums respectively. When the difference between the spectrums of MSDCCA and MS-EDXA becomes unobvious, there is a crossover which denotes the turning point of difference. The crossover results from the competition between the noise effects in the original signals and the intrinsic fluctuation of traffic signals and divides the plot of spectrums into two regions. In all the three case, MS-EDXA method makes the average of local scaling exponents increased and the standard deviation decreased and provides a relative stable persistent scaling cross-correlated behavior which gets the analysis more precise and more robust and improves the performance after noises being removed. Applying MS-EDXA method avoids the inaccurate characteristics of multiscale cross-correlation structure at the short scale including the spectrum minimum, the range for the spectrum fluctuation and general trend, which are caused by the noise in the original signals. We get the conclusions that the traffic velocity and volume are long-range cross-correlated, which is accordant to their actual evolution, while velocities on the present and the next moment and velocities on adjacent lanes reflect the strong cross-correlations both in temporal and spatial dimensions. We also reveal the similarity and uniqueness in the cross-correlation situations between velocities. Besides, signals on one lane show stronger long-range cross-correlation than that on adjacent lanes. Thus, the multiscale cross-correlation structure acquired by MS-EDXA is more close to the intrinsic mechanism of traffic system and reflects more accurate and more abundant traffic information.

Sign in / Sign up

Export Citation Format

Share Document