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The dependency structure of international commodity and stock markets after the Russia-Ukraine war

Author

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  • Cheng Zhang
  • Shuo Liu
  • Mimi Qin
  • Bin Gao

Abstract

In recent years, the international community has witnessed many crisis events, and the Russia-Ukraine war, which broke out on 24th February 2022, has increased international policy uncertainty and impacted the current world commodity and financial markets. Thus, we try to capture how the Russia-Ukraine war has affected the correlation structure of international commodity and stock markets. We study six groups of commodity daily returns and one group of stock daily returns and select the sample from 24th February 2022 to 1st June 2022 as the sample during the Russia-Ukraine war; in addition, we select the sample from 1st December 2019 to 31st December 2020 as the sample during COVID-19 control group, and the sample from 1st January 2014 to 31st December 2017 as the non-extreme event control group, to explore the correlation structure of international commodity and stock markets before the war, and to compare and uncover the impact of the uncertain event of the Russia-Ukraine war on the commodity and stock markets. In this paper, the marginal density function of each series is constructed using the ARMA-GARCH-std method, and the R-Vine copula model is built based on the marginal density function to analyze the correlation relationship between each market. From the Tree1 of the Vine copula, it is found that crude oil becomes the core connecting each commodity market and the stock market during the Russia-Ukraine war. The price fluctuations of crude oil may be contagious to agricultural and precious metal markets in the same direction, while the stock market price fluctuations are inversely correlated with commodity markets. Comparison with the selected control group sample reveals that the Russia-Ukraine war increases the correlation between the markets and enhances the possibility of risk transmission. The core of the correlation structure shifts from agricultural commodities and precious metals to crude oil after the Russia-Ukraine war.

Suggested Citation

  • Cheng Zhang & Shuo Liu & Mimi Qin & Bin Gao, 2025. "The dependency structure of international commodity and stock markets after the Russia-Ukraine war," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0316288
    DOI: 10.1371/journal.pone.0316288
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    1. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    2. Lubos Pástor & Pietro Veronesi, 2012. "Uncertainty about Government Policy and Stock Prices," Journal of Finance, American Finance Association, vol. 67(4), pages 1219-1264, August.
    3. Akbar, Muhammad & Iqbal, Farhan & Noor, Farzana, 2019. "Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan," Resources Policy, Elsevier, vol. 62(C), pages 154-164.
    4. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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