Cross-Correlation Analysis of Crude Oil-Related Stock Markets in China Caused by the Conflict Between Russia and Ukraine

Jian Wang, Wenjing Jiang, Menghao Huang and Wei Shao ()
Additional contact information
Jian Wang: Nanjing University of Information Science and Technology
Wenjing Jiang: Nanjing University of Information Science and Technology
Menghao Huang: Nanjing University of Information Science and Technology
Wei Shao: Nanjing University of Information Science and Technology

Computational Economics, 2025, vol. 65, issue 3, No 6, 1299-1317

Abstract: Abstract In this study, we apply multifractal detrended fluctuation analysis (MF-DFA) to explore the differences in China’s financial markets efficiency around the Russia-Ukraine Conflict. We investigate the stock markets for fossil oil, fertilizer and grain. The results show that the three industries around the conflict both have multifractal characteristics, and the multifractal characteristics after the conflict are stronger. This phenomenon shows that the efficiency of the stock markets have decreased after the conflict. Then, we adopt multifractal detrended cross-correlation analysis (MF-DCCA) to examine the nonlinear cross-correlations between fossil oil / chemical fertilizer and fossil oil / grain. The results indicate that there are cross correlations between the two time series pairs. In addition, the cross-correlations between chemical fertilizer and fossil oil after the conflict increase significantly, while that between grain and fossil oil are increase slightly. This paper is great interest by policy makers and participants involved in these markets given the economic and financial consequences derived from such dynamics.

Keywords: MF-DFA; MF-DCCA; Fossil oil; Chemical fertilizer; Grain (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10554-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10554-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10554-z

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().