Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis

2022 ◽  
Vol 75 ◽  
pp. 102473
Author(s):  
Faheem Aslam ◽  
Zil-e-huma ◽  
Rashida Bibi ◽  
Paulo Ferreira
2021 ◽  
pp. 2150041
Author(s):  
Ruwei Zhao ◽  
Peng-Fei Dai

In this study, we utilized the prevailing economic policy uncertainty index (EPU) as the proxy of state economic fluctuation and investigated Sino–US economic fluctuation long horizon cross-correlation with a multifractal detrended cross-correlation analysis (MF-DCCA). With the MF-DCCA approach, we found a reliable long-range cross-correlation between China and US EPU changes. In addition, we discovered that a power law cross-correlation existed for the variation of most scaling orders. However, no persistence of cross-correlations was detected within the Sino–US EPU change series. Additionally, we implemented Rényi exponent and spectrum singularity checks. Both the examination results proved series multifractality with the presented arch-shaped curves. We further calculated the Hölder exponent bounds within each series and found that the China EPU changes had maximal multifractality with the largest exponent difference.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruwei Zhao

This paper investigates the long-term dynamic cross-correlation evolution between US economic policy uncertainty index (USEPU) and Guangdong carbon emission trading price (GDCP) from the multifractal detrended cross-correlation analysis (MF-DCCA) perspective. With the calculation of correlation statistics and fluctuation function, the beginning procedures of MF-DCCA, we find that the cross-correlation between USEPU and GDCP is significant and presents power law property. Also, with the Hurst exponent, we find that the long-horizon correlations between series are persistent. Moreover, we perform Rényi exponent and spectrum singularity check. The empirical findings reveal that the all the correlations are of multifractality and the correlation of GDCP holds the highest degree.


2021 ◽  
pp. 2150018
Author(s):  
Wei Jiang ◽  
Jianfeng Li ◽  
Guanglin Sun

We utilize the multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the cross-correlations between the US economic policy uncertainty (EPU) and US stock markets in the framework of Fractal Market Hypothesis (FMH). The data contain daily closing values of EPU, and the returns of Dow Jones Industrial Average Index (DJI), S&P 500 index (GSPC) and NASDAQ Composite Index (IXIC). Our empirical results show that changes in EPU and fluctuations in the US stock markets interact in a nonlinear way. Furthermore, there exists significant multifractality in the cross-correlations between EPU and stock markets. The cross-correlations exhibit dynamics and are affected by major international events. We capture the underlying mechanisms such as multifractality and nonlinear relation that dominate EPU-US stock markets nexus by means of FMH. The findings add a new dimension to the existing literature, and are important for market participants to adjust investment decisions.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruwei Zhao ◽  
Yian Cui

In this paper, we employ the multifractal detrended cross-correlation analysis (MF-DCCA) as the measurement instrument for the dynamic cross-correlation inspection between US economic policy uncertainty (EPU) index and US dollar exchange rate return (Ret). By calculating the cross-correlation statistics, we find mild acceptance of cross-correlation between EPU and Ret qualitatively. With further application of MF-DCCA methodology, we find strong power law cross-correlation existence within all scaling orders. Also, apparent persistence of cross-correlation has been discovered with significant Hurst exponents of all orders. Besides, we find that long-term cross-correlation demonstrates more persistence and higher degree of multifractality than those in the short term. Finally, we utilize the rolling window and binominal measurement analysis as revisits of the model. The results are consistent with model statements.


2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.


Fractals ◽  
2014 ◽  
Vol 22 (04) ◽  
pp. 1450007 ◽  
Author(s):  
YI YIN ◽  
PENGJIAN SHANG

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.


2014 ◽  
Vol 10 (S306) ◽  
pp. 397-399
Author(s):  
Ya-Juan Lei

AbstractWe analyze the cross-correlation function of the soft and hard X-rays of the atoll source 4U 1636-53 with RXTE data. The results show that the cross-correlations evolve along the different branches of the color-color diagram. At the lower left banana states, we have both positive and ambiguous correlations, and positive correlations are dominant for the lower banana and the upper banana states. The anti-correlation is detected at the top of the upper banana states. The cross-correlations of two atoll sources 4U 1735-44 and 4U 1608-52 have been studied in previous work, and the anti-correlations are detected at the lower left banana or the top of the upper banana states. Our results show that, in the 4U 1636-53, the distribution of the cross-correlations in the color-color diagram is similar to those of 4U 1735-44 and 4U 1608-52, and confirm further that the distribution of cross-correlations in color-color diagram could be correlated with the luminosity of the source.


Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850058 ◽  
Author(s):  
XUEGENG MAO ◽  
PENGJIAN SHANG

It is a crucial topic to identify the cross-correlations between time series in multivariate systems. In this paper, we extend the detrended cross-correlation analysis (DCCA) into the multivariate systems, assigned multivariate detrended cross-correlation analysis (MVDCCA). Numerical simulations of synthetic multivariate time series generated by two-exponent and mix-correlated ARFIMA processes are applied to illustrate the validity of the proposed MVDCCA. Results show that the external coupling parameter determines the strength of cross-correlation no matter that it is inter-independent or correlated among channels in a certain multivariate time series. The MVDCCA method is robust enough to detect the scale properties of time series by estimating the Hurst exponent. And we use cross-correlation coefficient to quantify the level of cross-correlations clearly. Furthermore, the MVDCCA method performs well when applied to the stock markets combining the stock daily price returns and trading volume of stock indices. By comparing results only using stock daily price returns in published literatures, we find that the higher recognizability between the pair stock indices can be observed whatever from the same regions or different regions in multivariate situations and the conclusions are more comprehensive.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Yingxiu Zhao ◽  
Wei Zhang ◽  
Xiangyu Kong

In this paper, we examine the dynamic cross-correlations between participants’ attentions to the P2P lending and offline loan (lending) with the method of multifractal detrended cross-correlation analysis (MF-DCCA). The empirical result mainly shows that (1) the power-law cross-correlation exists between participants’ attentions to the P2P lending and offline loan and is persistent, (2) the cross-correlation is more stable in the short term, and (3) the relation subjected to a small fluctuation is more cross-correlated than that under larger ones. Furthermore, we carry out the robustness test to verify the result. The Granger causality test indicates that participants’ attentions to P2P lending and offline loan Granger cause each other in the short term.


2014 ◽  
Vol 28 (11) ◽  
pp. 1450090 ◽  
Author(s):  
Guangxi Cao ◽  
Yan Han ◽  
Yuemeng Chen ◽  
Chunxia Yang

Based on the daily price data of Shanghai and London gold spot markets, we applied detrended cross-correlation analysis (DCCA) and detrended moving average cross-correlation analysis (DMCA) methods to quantify power-law cross-correlation between domestic and international gold markets. Results show that the cross-correlations between the Chinese domestic and international gold spot markets are multifractal. Furthermore, forward DMCA and backward DMCA seems to outperform DCCA and centered DMCA for short-range gold series, which confirms the comparison results of short-range artificial data in L. Y. He and S. P. Chen [Physica A 390 (2011) 3806–3814]. Finally, we analyzed the local multifractal characteristics of the cross-correlation between Chinese domestic and international gold markets. We show that multifractal characteristics of the cross-correlation between the Chinese domestic and international gold markets are time-varying and that multifractal characteristics were strengthened by the financial crisis in 2007–2008.


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