Detrended cross-correlation analysis in quantiles between oil price and the US stock market

Energy ◽  
2021 ◽  
pp. 122918
Author(s):  
Ousama Ben-Salha ◽  
Khaled Mokni
2020 ◽  
Vol 10 (6) ◽  
pp. 2171
Author(s):  
Ki-Hong Shin ◽  
Gyuchang Lim ◽  
Seungsik Min

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.


2020 ◽  
pp. 2150031
Author(s):  
You-Shuai Feng ◽  
Hong-Yong Wang

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.


2018 ◽  
Vol 2018 (2) ◽  
pp. 023402 ◽  
Author(s):  
Longfeng Zhao ◽  
Wei Li ◽  
Andrea Fenu ◽  
Boris Podobnik ◽  
Yougui Wang ◽  
...  

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