Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets
By Ling-Yun He, Shu-Peng Chen. 2011
141 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f64f76a6f4bfe1a00355f993a94d7f5d0d388476
How researchers see this paper
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In Economics and Econometrics
In Finance
- Guangxi Cao
- Longbing Xu
- Jie Cao
- Chi Xie
- Gangjin Wang
- Ladislav Kristoufek
- Ayoub Ammy-Driss
- Matthieu Garcin
In Statistical and Nonlinear Physics
- Guangxi Cao
- Longbing Xu
- Jie Cao
- Gangjin Wang
- Ming-Yan Yang
- Shou Chen
- Ladislav Kristoufek
- Jiao-Jiao Yang
Citations in the corpus
- Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, background): “As the financial time series provide sufficient number of observations and they have been shown to possess long-range auto-correlations (volatility, traded volume, signs of changes and absolute returns to name the most frequently studied ones), these also create an appropriate setting for the power-”
- Multifractal detrended cross-correlations between the Chinese exchange market and stock market (2012, methodology): “[27,37–39,43], but Fig.”
- Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient (2013, background): “However, in the real world, especially in the financial markets, the time series are often non-stationary and heterogeneous [32,33].”