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Trend analysis of global stock market linkage based on a dynamic conditional correlation network

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  • Kedong Yin
  • Zhe Liu
  • Peide Liu

Abstract

The paper analyses the trend of global stock market linkages via daily data of 51 stock indices spanning the period 22 July 2005 to 30 June 2016 which covers four regions: America, Europe, Asia Pacific and Africa. A dynamic conditional multivariate generalized autoregressive conditional heteroskedasticity (DCC-MVGARCH) approach was used to calculate dynamic correlation coefficient in order to construct the volatility networks. The methods of minimum spanning tree (MST) and low pass filter were for the first time applied to analyze the variable periodicity of the comovement. The original contribution of this paper is that contrary to previous works, financial events such as Quantitative Easing (QE) and Bailouts are accounted for rather than only crisis factors such as the 2008 financial crisis and the European Debt crisis. The main findings of the paper are as follows: (1) Financial crisis promotes and strengthens global stock markets linkage in the short run; (2) Linkage cycles post crisis are significantly short, due to the effect of monetary policy spillover effects caused by QE from developed to developing countries; and (3) European stock markets are the information transmission hub for global stock market. The research conclusions would be significant for both government to regulate markets as well as for investors to diversify risks.

Suggested Citation

  • Kedong Yin & Zhe Liu & Peide Liu, 2017. "Trend analysis of global stock market linkage based on a dynamic conditional correlation network," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(4), pages 779-800, July.
    • Handle: RePEc:taf:jbemgt:v:18:y:2017:i:4:p:779-800
    DOI: 10.3846/16111699.2017.1341849
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    Citations

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    Cited by:

    1. Borjigin, Sumuya & Gao, Ting & Sun, Yafei & An, Biao, 2020. "For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. Xiuping Ji & Sujuan Wang & Honggen Xiao & Naipeng Bu & Xiaonan Lin, 2022. "Contagion Effect of Financial Markets in Crisis: An Analysis Based on the DCC–MGARCH Model," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    3. Mengting Li & Qifa Xu & Cuixia Jiang & Yezheng Liu, 2024. "The role of long‐ and short‐run correlation networks in international portfolio selection," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3147-3176, July.
    4. Mori Kogid & Jaratin Lily & Rozilee Asid & James M. Alin & Dullah Mulok, 2022. "Volatility spillover and dynamic co-movement of foreign direct investment between Malaysia and China and developed countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 131-148, February.
    5. Jean-Baptiste Hasse, 2022. "Systemic risk: a network approach," Empirical Economics, Springer, vol. 63(1), pages 313-344, July.
    6. Yin, Kedong & Liu, Zhe & Jin, Xue, 2020. "Interindustry volatility spillover effects in China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    7. Jean-Baptiste Hasse, 2020. "Systemic Risk: a Network Approach," Working Papers halshs-02893780, HAL.
    8. Xiaqing Su & Zhe Liu, 2021. "Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market," Mathematics, MDPI, vol. 9(12), pages 1-22, June.

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