Financial Risk Contagion Analysis Via Complex Network Theory

In January 2020,the COVID-19 broke out in China,which seriously affected China’s economic activities and caused a negative impact on the financial market.In March,this epidemic spread to the world,and the international stock market plummeted.The impact was even more serious than that of the 2008 financial crisis.Countries pay more and more attention to financial risks today.It is crucial for countries to research on risk contagion in order to improve the prevention of financial risk and develop financial market stably.Based on the theory of complex networks,this study does a timely research on risk contagion in the global and domestic financial markets during the period of COVID-19.In terms of theory,complex networks and risk contagion are systematically introduced in this research.In addition,this article defines the algorithm of the minimum spanning tree model and topology such as centrality.It explains four correlation measures: Pearson correlation,Granger causality,mutual information and Brownian distance,and also compares two linear measurements and two non-linear measurements.Through the introduction of related theories of complex networks,the feasibility and applicability of complex networks for financial risk communication research can be demonstrated.This paper studies the influence of network structure on financial risk contagion and the role of network nodes in risk spread.It proposes that global financial market risk contagion may be affected by geographical factors,historical factors and economic factors,and analyses the domestic industry risk contagion from downstream channels and information channels.This study analyses the macro and micro impacts of public health emergencies on the financial market,laying a rich theoretical foundation for empirical analysis.This article mainly makes the empirical analysis from two aspects,including the global financial market and the domestic industry market,discussing the impact of the epidemic on the financial network and the contagion path of financial risks during the epidemic.In terms of empirical analysis,this article mainly discusses the impact of the epidemic on the financial network and the contagion path of financial risks during the epidemic based on the global financial market and the domestic industry market.Firstly,four correlation measurement methods are used to construct international financial networks and minimum spanning trees model for 36 countries and regions,and to analyse the topology and centrality of the network.Secondly,the establishment of domestic industry networks give some evidence that the path of risk infection between industries in my country is through upstream industries to downstream industries.The following conclusions are drawn via the analysis: In the global financial market,the linkages between countries are distributed in regional clusters.During the COVID-19,connections between countries have increased and the networks become tighter.On the whole,France,Chinese Hong Kong,and the United States were the centres of Europe,Asia-Pacific,and the Americas respectively before the epidemic.During the epidemic,Singapore in the Asia-Pacific region also became significant in risk transmission,and Canada replaced the United States as the centre of the Americas.South Africa acted as a bridge connecting Europe and the Asia-Pacific region before and during the epidemic.This study finds that the sample size requirement of mutual information theory is high.Although it can capture more correlation relationships than that from Pearson correlation,it cannot accurately reflect the effect of national regional clusters.Brownian distance is the optimal non-linear correlation.The generated networks are highly interpretable and reasonable,and Brownian distance is the best method when analysing topology.In the domestic industry market,the analysis gives some evidence that the path of risk infection between industries in my country is through upstream industries to downstream industries.Using the best method Brownian distance to measure the high degree of connectivity and correlation between industries in our country,it finds that node linkages were much more during the epidemic and the networks were denser,but after the peak of the epidemic,the networks became looser.The machinery and equipment industry in China is the central during these three periods and has a significant impact on other manufacturing and service industries.Some possible contributions of this article are as follow: Firstly,two non-linear correlation measures are selected,including mutual information theory and Brownian distance,and results of analysis shows that Brownian distance is optimal method.The second one is to consider the global financial market and the domestic industry market.It explores the path and mechanism of financial risk contagion through network models.The third one is to use real-time data in 2020 to analyse the impact of the COVID-19 on the spread of global and domestic financial risks,and to provide some useful advice for financial risk prevention.