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

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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.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Adekoya, O. B., Ogunbowale, G. O., Akinseye, A. B., & Oduyemi, G. O. (2021). Improving the predictability of stock returns with global financial cycle and oil price in oil-exporting African countries. International Economics, 168, 166–181.

    Article  Google Scholar 

  • Adekoya, O. B., Oliyide, J. A., Yaya, O. S., & Al-Faryan, M. A. S. (2022). Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga. Resources policy, 77, 102728.

    Article  Google Scholar 

  • Adekoya, O. B., Asl, M. G., Oliyide, J. A., & Izadi, P. (2023). Multifractality and cross-correlation between the crude oil and the European and non-European stock markets during the Russia-Ukraine war. Resources Policy, 80, 103134.

    Article  Google Scholar 

  • Asaad, Z., & Marane, B. (2020). Corruption, terrorism and the stock market: The evidence from Iraq. The Journal of Asian Finance, Economics and Business, 7(10), 629–639.

    Article  Google Scholar 

  • Bastianin, A., Conti, F., & Manera, M. (2016). The impacts of oil price shocks on stock market volatility: Evidence from the G7 countries. Energy Policy, 98, 160–169.

    Article  Google Scholar 

  • Cong, R. G., Wei, Y. M., Jiao, J. L., & Fan, Y. (2008). Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy, 36(9), 3544–3553.

    Article  Google Scholar 

  • Cui, J., & Maghyereh, A. (2022). Time-frequency co-movement and risk connectedness among cryptocurrencies: New evidence from the higher-order moments before and during the COVID-19 pandemic. Financial Innovation, 8(1), 1–56.

    Article  Google Scholar 

  • Fu, Z., Niu, H., & Wang, W. (2022). Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis. Computational Economics, pp 1-25.

  • Gao, H. L., & Mei, D. C. (2019). The correlation structure in the international stock markets during global financial crisis. Physica A: Statistical Mechanics and its Applications, 534, 122056.

    Article  Google Scholar 

  • Huang, M., Shao, W., & Wang, J. (2023). Correlations between the crude oil market and capital markets under the Russia-Ukraine conflict: A perspective of crude oil importing and exporting countries. Resources Policy, 80, 103233.

    Article  Google Scholar 

  • Ihlen, E. A. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3, 141.

    Article  Google Scholar 

  • Ivanov, P. C., Amaral, L. A. N., Goldberger, A. L., Havlin, S., Rosenblum, M. G., Struzik, Z. R., & Stanley, H. E. (1999). Multifractality in human heartbeat dynamics. Nature, 399(6735), 461–465.

    Article  Google Scholar 

  • Jagtap, S., Trollman, H., Trollman, F., et al. (2022). The Russia-Ukraine conflict: Its implications for the global food supply chains. Foods, 11(14), 2098.

    Article  Google Scholar 

  • Jiang, W., Li, J., & Sun, G. (2021). Economic policy uncertainty and stock markets: A multifractal cross-correlations analysis. Fluctuation and Noise Letters, 20(02), 2150018.

    Article  Google Scholar 

  • Joo, K., Suh, J. H., Lee, D., & Ahn, K. (2020). Impact of the global financial crisis on the crude oil market. Energy Strategy Reviews, 30, 100516.

    Article  Google Scholar 

  • Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1–4), 87–114.

    Article  Google Scholar 

  • Lashermes, B., Abry, P., & Chainais, P. (2004). New insights into the estimation of scaling exponents. International Journal of Wavelets, Multiresolution and Information Processing, 2(04), 497–523.

    Article  Google Scholar 

  • Liu, Y., Zhang, W., & Fu, J. (2016). Binomial Markov-switching multifractal model with skewed t innovations and applications to Chinese SSEC index. Physica A: Statistical Mechanics and its Applications, 462, 56–66.

    Article  Google Scholar 

  • Liu, Y., Zhang, W., Fu, J., & Wu, X. (2020). Multifractal analysis of realized volatilities in Chinese stock market. Computational Economics, 56, 319–336.

    Article  Google Scholar 

  • Li, L., Willett, T. D., & Zhang, N (2012). The effects of the global financial crisis on China’s financial market and macroeconomy. Economics Research International, 2012.

  • Li, H., Xu, G., Huang, Q., Ruan, R., & Zhang, W. (2023). COVID-19 Impact on Stock Markets: A Multiscale Event Analysis Perspective. Computational Economics. https://doi.org/10.1007/s10614-023-10448-6

    Article  Google Scholar 

  • Lu, X., Tian, J., Zhou, Y., & Li, Z. (2013). Multifractal detrended fluctuation analysis of the Chinese stock index futures market. Physica A: Statistical Mechanics and its Applications, 392(6), 1452–1458.

    Article  Google Scholar 

  • Ma, F., Wei, Y., & Huang, D. (2013). Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets. Physica A: Statistical Mechanics and its Applications, 392(7), 1659–1670.

    Article  Google Scholar 

  • Mensi, W., Tiwari, A. K., & Yoon, S. M. (2017). Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis. Physica A: Statistical Mechanics and its Applications, 471, 135–146.

    Article  Google Scholar 

  • Mitra, S. K., Bhatia, V., Jana, R. K., Charan, P., & Chattopadhyay, M. (2018). Changing value detrended cross correlation coefficient over time: Between crude oil and crop prices. Physica A: Statistical Mechanics and its Applications, 506, 671–678.

    Article  Google Scholar 

  • Naimy, V., Montero, J. M., El Khoury, R., & Maalouf, N. (2020). Market volatility of the three most powerful military countries during their intervention in the Syrian War. Mathematics, 8(5), 834.

    Article  Google Scholar 

  • Niu, H., Wang, W., & Zhang, J. (2019). Recurrence duration statistics and time-dependent intrinsic correlation analysis of trading volumes: A study of Chinese stock indices. Physica A: Statistical Mechanics and its Applications, 514, 838–854.

    Article  Google Scholar 

  • Oral, E., & Unal, G. (2019). Modeling and forecasting time series of precious metals: A new approach to multifractal data. Financial Innovation, 5(1), 1–28.

    Article  Google Scholar 

  • Podobnik, B., & Stanley, H. E. (2008). Detrended cross-correlation analysis: A new method for analyzing two nonstationary time series. Physical Review Letters, 100(8), 084102.

    Article  Google Scholar 

  • Qin, J., Lu, X., Zhou, Y., & Qu, L. (2015). The effectiveness of China’s RMB exchange rate reforms: An insight from multifractal detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 421, 443–454.

    Article  Google Scholar 

  • Ruan, Q., Yang, H., Lv, D., & Zhang, S. (2018). Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA. Physica A: Statistical Mechanics and its Applications, 503, 243–256.

    Article  Google Scholar 

  • Shaikh, I. (2019). The impact of terrorism on Indian securities market. Economic research-Ekonomska istraz̆ivanja, 32(1), 1744–1764.

    Article  Google Scholar 

  • Shao, W., & Wang, J. (2020). Does the “ice-breaking’’ of South and North Korea affect the South Korean financial market? Chaos, Solitons & Fractals, 132, 109564.

    Article  Google Scholar 

  • Sun, M. (2022). The impact of the Russia-Ukraine conflict on global grain market and food security: Short-and long-term effects. Seed Biology, 1(1), 1–4.

    Article  Google Scholar 

  • Wang, Q., & Liu, L. (2022). Pandemic or panic? A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market. Financial Innovation, 8(1), 1–38.

    Article  Google Scholar 

  • Wang, X., & Wu, C. (2018). Asymmetric volatility spillovers between crude oil and international financial markets. Energy Economics, 74, 592–604.

    Article  Google Scholar 

  • Wang, F., Ye, X., & Wu, C. (2019). Multifractal characteristics analysis of crude oil futures prices fluctuation in China. Physica A: Statistical Mechanics and its Applications, 533, 122021.

    Article  Google Scholar 

  • Wen, D., Liu, L., Ma, C., & Wang, Y. (2020). Extreme risk spillovers between crude oil prices and the US exchange rate: Evidence from oil-exporting and oil-importing countries. Energy, 212, 118740.

    Article  Google Scholar 

  • Wu, W., Lee, C. C., Xing, W., & Ho, S. J. (2021). The impact of the COVID-19 outbreak on Chinese-listed tourism stocks. Financial Innovation, 7(1), 1–18.

    Article  Google Scholar 

  • Yang, L., Zhu, Y., & Wang, Y. (2016). Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 451, 357–365.

    Article  Google Scholar 

  • Yan, R., Yue, D., Chen, X., & Wu, X. (2020). Non-linear characterization and trend identification of liquidity in China’s new OTC stock market based on multifractal detrended fluctuation analysis. Chaos, Solitons & Fractals, 139, 110063.

    Article  Google Scholar 

  • Yan, R., Yue, D., Wu, X., & Gao, W. (2021). Multiscale Multifractal Detrended Fluctuation Analysis and Trend Identification of Liquidity in the China’s Stock Markets. Computational Economics, pp 1-25.

  • Yao, C. Z., Liu, C., & Ju, W. J. (2020). Multifractal analysis of the WTI crude oil market, US stock market and EPU. Physica A: Statistical Mechanics and its Applications, 550, 124096.

    Article  Google Scholar 

  • Yuan, Y., Zhuang, X. T., & Jin, X. (2009). Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 388(11), 2189–2197.

    Article  Google Scholar 

  • Zavadska, M., Morales, L., & Coughlan, J. (2020). Brent crude oil prices volatility during major crises. Finance Research Letters, 32, 101078.

    Article  Google Scholar 

  • Zhang, S., Guo, Y., Cheng, H., & Zhang, H. (2021). Cross-correlations between price and volume in China’s crude oil futures market: A study based on multifractal approaches. Chaos, Solitons & Fractals, 144, 110642.

    Article  Google Scholar 

  • Zhou, W. X. (2008). Multifractal detrended cross-correlation analysis for two nonstationary signals. Physical Review E, 77(6), 066211.

    Article  Google Scholar 

  • Zhou, W. X. (2009). The components of empirical multifractality in financial returns. Europhysics Letters, 88(2), 28004.

    Article  Google Scholar 

  • Zunino, L., Tabak, B. M., Figliola, A., Pérez, D. G., Garavaglia, M., & Rosso, O. A. (2008). A multifractal approach for stock market inefficiency. Physica A: Statistical Mechanics and its Applications, 387(26), 6558–6566.

    Article  Google Scholar 

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Acknowledgements

The author thanks the reviewers for giving such constructive suggestions which helped improving the quality of this manuscript.

Funding

The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant Nos. 22KJB110020) and Jiangsu shuangchuang project (JSSCBS20210431) were received for this paper.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Wang, Wenjing Jiang, Menghao Huang & Wei Shao

  2. Center for Applied Mathematics of Jiangsu Province, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Wang

  3. Jiangsu International Joint Laboratory on System Modeling and Data Analysis, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Wang

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  1. Jian Wang
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Correspondence to Wei Shao.

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Wang, J., Jiang, W., Huang, M. et al. Cross-Correlation Analysis of Crude Oil-Related Stock Markets in China Caused by the Conflict Between Russia and Ukraine. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10554-z

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