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BRICS Capital Markets Co-Movement Analysis and Forecasting

Author

Listed:
  • Moinak Maiti

    (Department of Finance, National Research University Higher School of Economics, 194100 Saint Petersburg, Russia)

  • Darko Vukovic

    (International Laboratory for Finance and Financial Markets, Faculty of Economics, People’s Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, 117198 Moscow, Russia
    Geographical Institute “Jovan Cvijic” SASA, Djure Jaksica 9, 11000 Belgrade, Serbia)

  • Yaroslav Vyklyuk

    (Artificial Intelligence System Department, Lviv Polytechnic National University, Kniazia Romana Str. 5, 79013 Lviv, Ukraine)

  • Zoran Grubisic

    (Faculty for Banking, Insurance and Finance, Belgrade Banking Academy, 11000 Belgrade, Serbia)

Abstract

The present study analyses BRICS (Brazil, Russia, India, China, South Africa) capital markets in both time and frequency domain using wavelets. We used artificial neural network techniques to forecast the co-movement among BRICS capital markets. Wavelet coherence and clustering estimates uncover the interesting dynamics among the BRICS capital markets co-movement. A wavelet coherence diagram shows a clear contagion effect among BRICS nations, and it favors short period investments over longer period investments. Overall study estimates indicate that co-movement among BRICS nations significantly differs statistically at different levels. Except for China during the great financial crisis period, significant levels of co-movement were observed between other BRICS nations and that lasted for a longer period of time. A wavelet clustering diagram demonstrates that investors would not get any substantial benefits of diversification by investing only in the ‘Russia and China’ or ‘India and South Africa’ capital markets. Lastly, the study attempts to forecast the BRICS capital market co-movement using two different types of neural networks. Further, RMSE (Root Mean Square Error) values confirm the correctness of the forecasting model. The present study answers the key question, “What kind of integration and globalization framework do we need for sustainable development?”.

Suggested Citation

  • Moinak Maiti & Darko Vukovic & Yaroslav Vyklyuk & Zoran Grubisic, 2022. "BRICS Capital Markets Co-Movement Analysis and Forecasting," Risks, MDPI, vol. 10(5), pages 1-13, April.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:5:p:88-:d:797175
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    References listed on IDEAS

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    2. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
    3. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
    4. Almeida, José & Gaio, Cristina & Gonçalves, Tiago Cruz, 2024. "Crypto market relationships with bric countries' uncertainty – A wavelet-based approach," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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