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Mutual Information Based Analysis for the Distribution of Financial Contagion in Stock Markets

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  • Xudong Wang
  • Xiaofeng Hui

Abstract

This paper applies mutual information to research the distribution of financial contagion in global stock markets during the US subprime crisis. First, we symbolize the daily logarithmic stock returns based on their quantiles. Then, the mutual information of the stock indices is calculated and the block bootstrap approach is adopted to test the financial contagion. We analyze not only the contagion distribution during the entire crisis period but also its evolution over different stages by using the sliding window method. The empirical results prove the widespread existence of financial contagion and show that markets impacted by contagion tend to cluster geographically. The distribution of the contagion strength is positively skewed and leptokurtic. The average contagion strength is low at the beginning and then witnesses an uptrend. It has larger values in the middle stage and declines in the late phase of the crisis. Meanwhile, the cross-regional contagion between Europe and America is stronger than that between either America and Asia or Europe and Asia. Europe is found to be the region most deeply impacted by the contagion, whereas Asia is the least affected.

Suggested Citation

  • Xudong Wang & Xiaofeng Hui, 2017. "Mutual Information Based Analysis for the Distribution of Financial Contagion in Stock Markets," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-13, October.
    • Handle: RePEc:hin:jnddns:3218042
    DOI: 10.1155/2017/3218042
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    Cited by:

    1. De Keyser, Steven & Gijbels, Irène, 2024. "Parametric dependence between random vectors via copula-based divergence measures," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
    2. Xu, Xin & Huang, Shupei & Lucey, Brian M. & An, Haizhong, 2023. "The impacts of climate policy uncertainty on stock markets: Comparison between China and the US," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Choi, Insu & Kim, Woo Chang, 2024. "Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques," International Review of Financial Analysis, Elsevier, vol. 94(C).
    4. da Silva Filho, A.M. & Zebende, G.F. & Guedes, E.F., 2021. "Analysis of intentional lethal violent crimes: A sliding windows approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    5. Santos, E.C.O. & Guedes, E.F. & Zebende, G.F. & da Silva Filho, A.M., 2022. "Autocorrelation of wind speed: A sliding window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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