2014
DOI: 10.1063/1.4893442
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Asymmetric multiscale detrended cross-correlation analysis of financial time series

Abstract: 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 a… Show more

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Cited by 13 publications
(4 citation statements)
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References 20 publications
(24 reference statements)
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“…In addition, one can analyze different kinds of asymmetric correlation natures according to the relationships between the exponents. 29,30 However, in the present case, the F …”
Section: A Description Of the Modified A-dxacontrasting
confidence: 46%
“…In addition, one can analyze different kinds of asymmetric correlation natures according to the relationships between the exponents. 29,30 However, in the present case, the F …”
Section: A Description Of the Modified A-dxacontrasting
confidence: 46%
“…e results show that the BP neural network model has a better fit for the stock time series prediction, and the prediction is more accurate. Yin et al proposed a data-driven neural network prediction model [11] for the problem that traditional statistical models cannot accurately describe the complex nonlinear characteristics of real systems. Schnurr applied radial basis neural network to the prediction of exchange rate and obtained more accurate prediction of exchange rate [12] en, based on the financial time series forecasting model combining fuzzy neural network and GARCH, a specific mixed model is given for the data of the Shanghai Stock Exchange Index [13].…”
Section: Related Workmentioning
confidence: 99%
“…In the past decade, many researchers have utilized the DCCA to uncover the cross-correlations in many research fields such as cardiac dynamics, bioinformatics, economics, meteorology, material science, etc. and obtained plenty of significant results (Zhou 2008;Shi et al 2009;Wang et al 2011c;Ghosh et al 2014;Pal et al 2014;Yin and Shang 2014). However, seldom researches have been found on environment problems especially on variations of particulate matters at urban traffic intersection.…”
Section: Introductionmentioning
confidence: 95%