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Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation

Author

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  • Dong, Zhiliang
  • An, Haizhong
  • Liu, Sen
  • Li, Zhengyang
  • Yuan, Meng

Abstract

To better discover the evolutionary features of the short-period correlation coefficients for the stock indices of Brazil, Russia, India, China, and South Africa (the BRICS countries), the stock closing price time series data for the last 18 years for the main stock indices of the BRICS countries are selected as the study sample in this paper. By using the sliding window method, the 60-day correlation coefficient of the main stock index series is obtained to construct the stock index time-varying network of the BRICS countries, and the evolution characteristics of the short-term stock index correlation are studied. The results show that, there is a certain positive linkage between BRICS stock indices from 2001 to 2018, but not always. The evolution of the relevant relationship in the BRICS countries is mainly controlled by the key correlation modes, and the regularity is obvious. According to the key modes, it is suggested that investors who prefer a high level of risk in the stock market of the BRICS countries should choose the Brazil or South Africa stock market for portfolio investment; investors who prefer a medium level of risk should choose any combination of the Brazil, Russia, India, and South Africa stock markets for investment; investors who prefer a low level of risk can choose China and a joint investment in four other stock markets. In addition, in a period of time that if there are five stock indices that are all positively correlated, Brazil, Russia, India, and South Africa are all positively correlated, and Brazil, Russia, and South Africa are all positively correlated, it is suggested that investors who prefer a low level of risk should not invest to reduce losses.

Suggested Citation

  • Dong, Zhiliang & An, Haizhong & Liu, Sen & Li, Zhengyang & Yuan, Meng, 2020. "Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 63-74.
    • Handle: RePEc:eee:reveco:v:69:y:2020:i:c:p:63-74
    DOI: 10.1016/j.iref.2020.04.008
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    1. Wiliński, M. & Sienkiewicz, A. & Gubiec, T. & Kutner, R. & Struzik, Z.R., 2013. "Structural and topological phase transitions on the German Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5963-5973.
    2. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets Around the Global Financial Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 195-210, February.
    3. Huang, Shupei & An, Haizhong & Huang, Xuan & Jia, Xiaoliang, 2018. "Co-movement of coherence between oil prices and the stock market from the joint time-frequency perspective," Applied Energy, Elsevier, vol. 221(C), pages 122-130.
    4. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    5. Wei Sun & Svetlozar Rachev & Frank Fabozzi & Petko Kalev, 2009. "A new approach to modeling co-movement of international equity markets: evidence of unconditional copula-based simulation of tail dependence," Empirical Economics, Springer, vol. 36(1), pages 201-229, February.
    6. Jan Hanousek & Evžen Kočenda, 2011. "Foreign News and Spillovers in Emerging European Stock Markets," Review of International Economics, Wiley Blackwell, vol. 19(1), pages 170-188, February.
    7. Geert Bekaert & Robert J. Hodrick & Xiaoyan Zhang, 2009. "International Stock Return Comovements," Journal of Finance, American Finance Association, vol. 64(6), pages 2591-2626, December.
    8. Courty, Pascal & Li, Hao, 1999. "Timing of Seasonal Sales," The Journal of Business, University of Chicago Press, vol. 72(4), pages 545-572, October.
    9. Edward M. H. Lin & Edward W. Sun & Min-Teh Yu, 2018. "Systemic risk, financial markets, and performance of financial institutions," Annals of Operations Research, Springer, vol. 262(2), pages 579-603, March.
    10. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
    11. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.
    12. M. Wili'nski & A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Structural and topological phase transitions on the German Stock Exchange," Papers 1301.2530, arXiv.org, revised Jul 2013.
    13. Lee, Jieun & Chung, Kee H., 2018. "Foreign ownership and stock market liquidity," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 311-325.
    14. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    15. Li, Xiao-Ming & Peng, Lu, 2017. "US economic policy uncertainty and co-movements between Chinese and US stock markets," Economic Modelling, Elsevier, vol. 61(C), pages 27-39.
    16. Raza, Syed Ali & Shahbaz, Muhammad & Amir-ud-Din, Rafi & Sbia, Rashid & Shah, Nida, 2018. "Testing for wavelet based time-frequency relationship between oil prices and US economic activity," Energy, Elsevier, vol. 154(C), pages 571-580.
    17. Boubaker, Heni & Raza, Syed Ali, 2017. "A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets," Energy Economics, Elsevier, vol. 64(C), pages 105-117.
    18. Sun Wei & Rachev Svetlozar & Stoyanov Stoyan V. & Fabozzi Frank J., 2008. "Multivariate Skewed Student's t Copula in the Analysis of Nonlinear and Asymmetric Dependence in the German Equity Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-37, May.
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