The cross-correlations of stock markets based on DCCA and time-delay DCCA

Lin, Aijing; Shang, Pengjian; Zhao, Xiaochuan

doi:10.1007/s11071-011-9991-8

Cited by 95 publications

(27 citation statements)

References 32 publications

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“…6 DCCA method has been used to investigate cross-correlations in many previous studies. [7][8][9][10][11][12][13] For example, Podobnik et al suggest a new test for quantifying the statistical significance of cross-correlation. 11 Horvatic et al demonstrate that power-law cross-correlation between different simultaneously recorded time series can be accurately quantified in the presence of highly non-stationary sinusoidal and polynomial overlying trends by using DCCA with polynomials of varying order.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric multiscale detrended cross-correlation analysis of financial time series

Yin

Shang

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We propose the asymmetric multiscale detrended cross-correlation analysis (MS-ADCCA) method and apply MS-ADCCA method to explore the existence of asymmetric cross-correlation for daily price returns in US and Chinese stock markets and to assess the properties of these asymmetric cross-correlations. The results all show the existences of asymmetric cross-correlations, while small asymmetries at small scales and larger asymmetries at larger scales are also displayed. There is a strong similarity between S&P500 and DJI, and we reveal that the asymmetries depend more on the cross-correlations of S&P500 vs. DJI, S&P500 vs. NQCI, DJI vs. NQCI, and ShangZheng vs. ShenCheng when the market is falling than rising, respectively. By comparing the spectra of S&P500 vs. NQCI and DJI vs. NQCI with uptrends and downtrends, we detect some new characteristics which lead to some new conclusions. Likewise, some new conclusions also can be drawn by the new characteristics displayed through the comparison between the spectra of ShangZheng vs. HSI and ShenCheng vs. HSI. Obviously, we conclude that although the overall spectra are similar and one market has the same effect when it is rising and falling in the study of asymmetric cross-correlations between it and different markets, the cross-correlations and asymmetries on the trends of the different markets are all different. MS-ADCCA method can detect the differences on the asymmetric cross-correlations by different trends of markets. Moreover, the uniqueness of cross-correlation between NQCI and HSI can be detected in the study of the asymmetric cross-correlations, which confirms that HSI is unique in the Chinese stock markets and NQCI is unique in the US stock markets further.

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Section: Introductionmentioning

confidence: 99%

Asymmetric multiscale detrended cross-correlation analysis of financial time series

Yin

Shang

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…According to Lin et al [22], if there are two time series, f (i), g(i), and there is no cross-correlation between the two time series, the relationship 2 + ≈ 2 + 2 should hold true. To expatiate the cross-correlation along the time series for meteorological factors and wildfire more clearly, this study defines the intercorrelation significance level between time series as |( Figures 6 and 7 are combined, the significance levels of the correlation coefficient between daily precipitation and relative humidity are more than 30%.…”

Section: Dcca Methodmentioning

confidence: 99%

Scale Analysis and Correlation Study of Wildfire and the Meteorological Factors That Influence It

Lu¹,

Zhou²,

Li³

et al. 2018

Mathematical Problems in Engineering

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Wildfire is a large-scale complex system. Insight into the mechanism that drives wildfires can be revealed by the distribution of the wildfire over a large time scale, which is one of the important topics in wildfire research. In this study, the scaling properties of four meteorological factors (relative humidity, daily precipitation, daily average temperature, and maximum wind speed) that can affect wildfires (number of wildfires per day) were investigated by using the detrended fluctuation analysis method. The results showed that the time series for these meteorological factors and wildfires have similar power exponents and turning points for the power exponents curve. The five types of time series have a lasting and steady long-range power law correlation over a certain time scale range, where the corresponding exponents were 0.6484, 0.5724, 0.8647, 0.7344, and 0.6734, respectively. They also have a reversible long-range power law correlation beyond a certain time scale, where the corresponding exponents are 0.3862, 0.2218, 0.1372, 0.2621, and 0.2678. The multifractal detrended fluctuation analysis results showed that the wildfire time series were multifractal. The results of the research based on the detrended cross-correlation analysis and the multifractal detrended cross-correlation analysis showed that relative humidity and daily precipitation have a considerable impact on the wildfire time series, while the impacts of daily average temperature and the maximum wind speed are relatively small. This study showed that identifying the factors causing the inherent volatility in the wildfire time series can improve understanding of the dynamic mechanism controlling wildfires and the meteorological parameters. These results can also be used to quantify the correlation between wildfire and the meteorological factors investigated in this study.

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“…As an effective methodologies to explore the cross-correlations between two signals, the detrended cross-correlation analysis (DCCA, also DXA) 10 and its multifractal version-MF-DXA 11 were proposed to be applied into lots of fields. [12][13][14][15][16][17][18][19][20][21][22] To further quantify the cross-correlated levels, a new crosscorrelation index called DXA coefficient, denoted as q DXA , was proposed by Zebende. 23 In addition, Podobnik et al…”

Section: Introductionmentioning

confidence: 99%

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis

Wang

2016

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In order to detect and quantify asymmetry of two time series, a novel cross-correlation coefficient is proposed based on recent asymmetric detrended cross-correlation analysis (A-DXA), which we called A-DXA coefficient. The A-DXA coefficient, as an important extension of DXA coefficient ρDXA, contains two directional asymmetric cross-correlated indexes, describing upwards and downwards asymmetric cross-correlations, respectively. By using the information of directional covariance function of two time series and directional variance function of each series itself instead of power-law between the covariance function and time scale, the proposed A-DXA coefficient can well detect asymmetry between the two series no matter whether the cross-correlation is significant or not. By means of the proposed A-DXA coefficient conducted over the asymmetry for California electricity market, we found that the asymmetry between the prices and loads is not significant for daily average data in 1999 yr market (before electricity crisis) but extremely significant for those in 2000 yr market (during the crisis). To further uncover the difference of asymmetry between the years 1999 and 2000, a modified H statistic (MH) and ΔMH statistic are proposed. One of the present contributions is that the high MH values calculated for hourly data exist in majority months in 2000 market. Another important conclusion is that the cross-correlation with downwards dominates over the whole 1999 yr in contrast to the cross-correlation with upwards dominates over the 2000 yr.

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The cross-correlations of stock markets based on DCCA and time-delay DCCA

Cited by 95 publications

References 32 publications

Asymmetric multiscale detrended cross-correlation analysis of financial time series

Asymmetric multiscale detrended cross-correlation analysis of financial time series

Scale Analysis and Correlation Study of Wildfire and the Meteorological Factors That Influence It

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis

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