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

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.

The Impact of COVID-19 on the Dependence of Chinese Stock Market

“…erefore, it is particularly important to choose a method that can be used to measure the dependence under nonlinear condition, at the same time avoiding the influence of the model selection and parameter setting. With the development of entropy theory and the expansion of its application, methods such as mutual information have been applied in a large number of financial market researches [31][32][33][34][35][36]. is method can well overcome the shortcomings of the abovementioned various methods.…”

Stock returns prediction using kernel adaptive filtering within a stock market interdependence approach

“…It has became clear that all agents involved in a given stock market may exhibit interconnections and correlations, representing important internal forces of the market (Collins & Biekpe, 2003;Jizba et al, 2012) -that is, the movement of a stock market in a country is likely to be affected by movement of other stocks in both that country and in other regions (Masih & Masih, 2001). The following strategies have been proposed to identify and quantify interactions on this type of complex system (Greenblatt et al, 2012): i) spacetime, such as covariance (Wang & Ye, 2016;De Ketelaere et al, 2018), correlation (Kenett et al, 2015), Granger causality (Papana et al, 2017), Shannon entropy (Sulthan et al, 2016), mutual information (Wang & Hui, 2017), and Renyi entropy (Brody et al, 2007); ii) space-frequency and space-time-frequency, such as Fourier transform (Fang & Chang, 2017;Saia et al, 2017), coherence (Vacha & Barunik, 2012), phase synchronization (Radhakrishnan et al, 2016), directed transfer function (Kamiński et al, 2001), wavelet transform (Joo & Kim, 2015;Saia, 2017), and cross-time frequency measures (Loh, 2013). The previous works study how the price of one stock is influenced by the economic factors of other markets.…”

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“…Wang X.D. et al used mutual information to study the financial contagion effect of the major global stock markets during the 2008 financial crisis, and found that mutual information can effectively investigate the interdependence of, and changes to, various stock markets during different periods of the financial crisis [38]. Wu X.B.…”