Price–volume cross-correlation analysis of CSI300 index futures

By Donghua Wang, Yuan Suo, Xiaowen Yu, Man Lei. 2013

48 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/80669dc01d49f7e77dccd77d742f700bd591c567

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ladislav Kristoufek
2. Elie Bouri
3. Eduard Baumöhl
4. Ayoub Ammy-Driss
5. Matthieu Garcin
6. Miloslav S. Vosvrda
7. Rangan Gupta
8. David Roubaud

In Finance

1. Ladislav Kristoufek
2. Ayoub Ammy-Driss
3. Matthieu Garcin
4. Miloslav S. Vosvrda
5. Eduard Baumöhl

In Statistical and Nonlinear Physics

1. Ladislav Kristoufek
2. Miloslav S. Vosvrda

Citations in the corpus

• Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market (2014, unknown): “MF-DCCA is used widely to detect the cross-correlation between financial markets [9–19].”
• 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-”