A Multifractal Cross-Correlation Analysis of Economic Policy Uncertainty: Evidence from China and US

2021 ◽  
pp. 2150041
Author(s):  
Ruwei Zhao ◽  
Peng-Fei Dai
Keyword(s):  
Economic Policy ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Economic Fluctuation ◽  
Cross Correlation Analysis ◽  
Uncertainty Index ◽  
Cross Correlations ◽  
Renyi Exponent

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.

scholarly journals Economic Policy Uncertainty and Local Carbon Emission Trading: A Multifractal Analysis from US and Guangdong

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruwei Zhao
Keyword(s):  
Economic Policy ◽  
Carbon Emission ◽  
Cross Correlation ◽  
Emission Trading ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Cross Correlation Analysis ◽  
Uncertainty Index ◽  
Carbon Emission Trading ◽  
Correlation Statistics

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.

Economic Policy Uncertainty and Stock Markets: A Multifractal Cross-Correlations Analysis

2021 ◽  
pp. 2150018
Author(s):  
Wei Jiang ◽  
Jianfeng Li ◽  
Guanglin Sun
Keyword(s):  
Economic Policy ◽  
Stock Markets ◽  
Composite Index ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Cross Correlation Analysis ◽  
The Us ◽  
Cross Correlations ◽  
The Cross ◽  
Underlying Mechanisms

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.

scholarly journals Dynamic Cross-Correlations Analysis on Economic Policy Uncertainty and US Dollar Exchange Rate: AMF-DCCA Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruwei Zhao ◽  
Yian Cui
Keyword(s):  
Economic Policy ◽  
Exchange Rate ◽  
Cross Correlation ◽  
Measurement Instrument ◽  
Policy Uncertainty ◽  
Dollar Exchange Rate ◽  
Economic Policy Uncertainty ◽  
Cross Correlation Analysis ◽  
Us Dollar ◽  
Correlation Statistics

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.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

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.

scholarly journals Belgian economic policy uncertainty index: Improvement through text mining

2018 ◽  
Vol 34 (2) ◽  
pp. 355-365 ◽  
Author(s):  
Ellen Tobback ◽  
Hans Naudts ◽  
Walter Daelemans ◽  
Enric Junqué de Fortuny ◽  
David Martens
Keyword(s):  
Economic Policy ◽  
Text Mining ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Uncertainty Index

MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS

Fractals ◽  
2014 ◽  
Vol 22 (04) ◽  
pp. 1450007 ◽  
Author(s):  
YI YIN ◽  
PENGJIAN SHANG
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Statistical Characteristics ◽  
Detailed Knowledge ◽  
Stock Indices ◽  
Regional Markets ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

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.

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