Trend Analysis of Global Stock Market Linkage Based on a Dynamic Conditional Correlation Network

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.

Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market

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Relationship between Entropy and Dimension of Financial Correlation-Based Network

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Network Analysis of Pakistan Stock Market During the Turbulence of Economic Crisis

“…These theories unfolds many exciting aspects of stock markets, such as: significant influencers, community detections, forecasting future movements, analysis and prediction of an upcoming crisis, and so on (see e.g. Namaki, Shirazi, Raei, & Jafari, 2011;Yang, Li, & Zhang, 2014;Creamer, Ren, & Nickerson, 2013;Nobi, Lee, Kim, & Lee, 2014;Yin, Z. Liu, & P. Liu, 2017;. In a typical stock market network, stocks are taken as nodes of the network, and their edges are described by Pearson correlation coefficient (Liu & Tse, 2012;Kazemilari, Mohamadi, Mardani, & Streimikis, 2019;.…”