The relationships between Shanghai stock market and CNY/USD exchange rate: New evidence based on cross-correlation analysis, structural cointegration and nonlinear causality test

2012 ◽  
Vol 391 (23) ◽  
pp. 6051-6059 ◽  
Author(s):  
Li Liu ◽  
Jieqiu Wan
Keyword(s):  
Exchange Rate ◽  
Correlation Analysis ◽  
Stock Market ◽  
Cross Correlation ◽  
Evidence Based ◽  
Causality Test ◽  
Cross Correlation Analysis ◽  
New Evidence

MARGIN-TRADING ACTIVITIES AND FUTURE STOCK RETURNS: NEW EVIDENCE FROM NONLINEAR ANALYSIS

Fractals ◽  
2020 ◽  
Vol 28 (06) ◽  
pp. 2050126
Author(s):  
QINGSONG RUAN ◽  
JIARUI ZHANG ◽  
YAPING ZHOU ◽  
DAYONG LV
Keyword(s):  
Stock Returns ◽  
Cross Correlation ◽  
Predictive Power ◽  
Granger Causality Test ◽  
Return Predictability ◽  
Positive Information ◽  
Causality Test ◽  
Cross Correlation Analysis ◽  
Trading Activities ◽  
New Evidence

Using multifractal detrended cross-correlation analysis (MF-DCCA) and nonlinear Granger causality test, this paper examines the return predictability of margin-trading activities. Results show that the predictive power of margin-trading activities on subsequent stock returns varies with respect to the different aspects of margin trading. In line with previous studies, we find no significant correlation between margin-buying amount and subsequent stock returns. However, the margin-covering amount is negatively associated with subsequent stock returns; and margin debt is positively associated with the future stock returns. In general, our findings suggest that margin traders may have no positive information when they conduct a margin-buying position, but may possess negative information when covering their positions.

scholarly journals Dynamics of the Global Stock Market Networks Generated by DCCA Methodology

2020 ◽  
Vol 10 (6) ◽  
pp. 2171
Author(s):  
Ki-Hong Shin ◽  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Financial Crisis ◽  
Correlation Analysis ◽  
Stock Market ◽  
Stock Price ◽  
Cross Correlation ◽  
Global Financial Crisis ◽  
Average Degree ◽  
Clustering Coefficient ◽  
Bubble Collapse ◽  
Cross Correlation Analysis

A group of stock markets can be treated as a complex system. We tried to find the financial market crisis by constructing a global 24 stock market network while using detrended cross-correlation analysis. The community structures by the Girvan-Newman method are observed and other network properties, such as the average degree, clustering coefficient, efficiency, and modularity, are quantified. The criterion of correlation between any two markets on the detrended cross-correlation analysis was considered to be 0.7. We used the return (rt) and volatility (|rt|) time series for the periods of 1, 4, 10, and 20-year of composite stock price indices during 1997–2016. Europe (France, Germany, Netherland, UK), USA (USA1, USA2, USA3, USA4) and Oceania (Australia1, Australia2) have been confirmed to make a solid community. This approach also detected the signal of financial crisis, such as Asian liquidity crisis in 1997, world-wide dot-com bubble collapse in 2001, the global financial crisis triggered by the USA in 2008, European sovereign debt crisis in 2010, and the Chinese stock price plunge in 2015 by capturing the local maxima of average degree and efficiency.

scholarly journals Dynamic Cross-Correlations between Participants’ Attentions to P2P Lending and Offline Loan in the Private Lending Market

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Yingxiu Zhao ◽  
Wei Zhang ◽  
Xiangyu Kong
Keyword(s):  
Correlation Analysis ◽  
Power Law ◽  
Cross Correlation ◽  
Granger Causality Test ◽  
Short Term ◽  
Causality Test ◽  
Cross Correlation Analysis ◽  
Private Lending ◽  
P2p Lending ◽  
Cross Correlations

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.

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