scholarly journals Segment Stock Market, Foreign Investors, and Cross-Correlation: Evidence from MF-DCCA and Spillover Index

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Nan Xu ◽  
Songsong Li
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Multifractal Analysis ◽  
Cross Correlation ◽  
Spillover Effects ◽  
The Other ◽  
Foreign Investors ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

Employing the tools of multifractal detrended cross-correlation analysis (MF-DCCA) and Diebold–Yilmaz spillover index (D.Y. spillover index), we examine the effect that the foreign investors have on the cross-correlations between the two-segment stock markets, that are the accessible and the inaccessible stock markets, and the other ten respective stock markets. The shares cross-listed by the same corporates on both the A-share and H-share stock markets of China serve as the best sample to compile the two stock indices, which stands for the inaccessible stock market (AHA) and the accessible stock market (AHH), respectively. Empirical results show that the cross-correlations between the two-segment stock markets and the other ten pairs are multifractal, the multifractal strength of cross-correlations is stronger in AHH than AHA, and the intensified growth of the multifractal cross-correlations in AHA can be seen as the increasing of the openness in the inaccessible market. The empirical result of D.Y. spillover index is consistent with the multifractal analysis above, and another interesting finding is that among the selected markets, the three markets with the strongest spillover effects with AHA and AHH are Taiwan, South Korea, and Singapore, respectively, and the weakest one is Australia during the sample scenarios.

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.

scholarly journals Joint Multifractal Analysis and Source Testing of River Level Records Based on Multifractal Detrended Cross-Correlation Analysis

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Liang Wu ◽  
Manling Wang ◽  
Tongzhou Zhao
Keyword(s):  
Correlation Analysis ◽  
Multifractal Analysis ◽  
Cross Correlation ◽  
Source Analysis ◽  
Data Set ◽  
River Level ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Joint Multifractal Analysis

The joint multifractal analysis is usually conducted in two different variables for their cross-correlations but rarely used for two records of one variable collected at two different places. It is important for the detection of change in multifractality in space. Besides, the cross-correlations in two analyzed series make the analysis of sources of joint multifractality difficult. There are few studies on the source of joint multifractality. We focus on the two issues for two level records at pairs of adjacent sites along one river and carry out an extension of our previous work which is about the single multifractality of one record with the same data set. The data set is collected from 10 observation stations of a northern China river and contains about two million high-frequency river level records. Results of joint multifractal analysis via multifractal detrended cross-correlation analysis show that the change in joint multifractality at pairs of adjacent sites caused by weak cross-correlations can be detected by comparing the single generalized Hurst exponent with the joint scaling exponent function and reveal the effects of human activities on joint multifractality. This analysis provides an approach for detecting the change in multifractality. Following the idea of our previous work, two robust hypothesis tests via a set of pairs of surrogate series are proposed for the source testing of joint multifractality. The analysis of the effects of cross-correlations is carried out via a proposed simultaneously half-shifting technique which can both minimize the cross-correlations between original series and make full use of records. Results of source analysis show not only the effects of autocorrelations in series and probability distribution of river levels but also the effects of cross-correlations between series.

Multifractal Fluctuation Analysis of Correlations Between the Sector Stock Markets in China and the US

2020 ◽  
pp. 2150031
Author(s):  
You-Shuai Feng ◽  
Hong-Yong Wang
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Rapid Development ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
The Us ◽  
Cross Correlations ◽  
The Cross ◽  
Stock Indexes

With the rapid development of economic globalization, the stock markets in China and the US are increasingly linked. The fluctuation features and cross-correlations of the two countries’ markets have attracted extensive attention from market investors and researchers. In this paper, the fractal analysis methods including multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) and coupled detrended cross-correlation analysis (CDCCA) are applied to explore the volatilities of CSI300 and SP500 sector stock indexes as well as the cross-correlations and coupling cross-correlations between the two corresponding sector stock indexes. The results show that the auto-correlations, cross-correlations and coupling cross-correlations have multifractal fluctuation characteristics, and that the cross-correlations are asymmetric. Additionally, the coupling cross-correlation strengths are distinct due to the different influence of long-range correlations and fat-tailed distribution. Further, the co-movement between China and the US sector stock markets is susceptible to external market factors such as major economic events and national policies.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

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.

Dynamic Cross-Correlations between Investors’ Attention and CSI300 Index Futures

2019 ◽  
Vol 18 (04) ◽  
pp. 1950022
Author(s):  
Xiong Xiong ◽  
Kewei Xu ◽  
Dehua Shen
Keyword(s):  
Cross Correlation ◽  
Futures Market ◽  
Relative Activity ◽  
Short Term ◽  
Index Futures ◽  
Search Volume ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

Using search volume on Baidu Index as the proxy for investors’ attention, we investigate the dynamic nonlinear relationship between investors’ attention and CSI300 index futures market. Multifractal detrend cross-correlation analysis (MF-DCCA) is employed to explore the multifractal features of the cross-correlations between investors’ attention and the return and relative activity of index futures market. We find that the power-law cross-correlations between investors’ attention and CSI300 index futures market are stronger in the short term than in the long term, and the cross-correlations are significantly multifractal. Precisely, the cross-correlation between abnormal search volume (ASV) and the relative activity is persistent, and the cross-correlation between ASV and return of IF is persistent in the short term but weakly anti-persistent in the long term. Besides, we also find that, with the restriction on index futures market, the cross-correlations between investors’ attention and CSI300 index futures market become less stable.

scholarly journals Quantifying the Cross-Correlations between Online Market Participation Willingness and Stock Market Dynamics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Chu ◽  
Xiao Li ◽  
Yongjie Zhang
Keyword(s):  
Stock Market ◽  
Asset Allocation ◽  
Cross Correlation ◽  
Vital Role ◽  
Market Dynamics ◽  
Chinese Stock Market ◽  
Market Participation ◽  
Market Index ◽  
Cross Correlations ◽  
The Cross

The investors’ market participation willingness plays a vital role in the decision-making process of asset allocation. With the newly emerged dataset of investors’ market participation willingness, this paper provides the first evidence on the dynamic relationship between market participation willingness and the market dynamics in the Chinese stock market. We select four typical Chinese stock market indices, i.e., SSE50 Index, CSI300 Index, Small and Medium Enterprise Market Index, and Growth Enterprise Market Index, to represent different aspects of the Chinese stock market. Moreover, we use mutual information to measure the overall dependence between market participation willingness and stock market and employ the DCCA cross-correlation coefficient and MF-DCCA to investigate the cross-correlation between market participation willingness and market dynamics. We find that there exist overall dependence and power-law cross-correlation between market participation willingness and the Chinese stock market, and the cross-correlations are significantly multifractal.

scholarly journals Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Gang-Jin Wang ◽  
Chi Xie ◽  
Shou Chen ◽  
Feng Han
Keyword(s):  
Multifractal Analysis ◽  
Cross Correlation ◽  
Market Dynamics ◽  
Gas Oil ◽  
Short Term ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Rolling Windows ◽  
Oil Gas

We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas), oil and CO2(Oil-CO2), and gas andCO2(Gas-CO2) based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, andCO2during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA) method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

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