Crisis contagion in the world trade network

Abstract

We present a model of worldwide crisis contagion based on the Google matrix analysis of the world trade network obtained from the UN Comtrade database. The fraction of bankrupted countries exhibits an on-off phase transition governed by a bankruptcy threshold κ related to the trade balance of the countries. For κ>κc, the contagion is circumscribed to less than 10% of the countries, whereas, for κ<κc, the crisis is global with about 90% of the countries going to bankruptcy. We measure the total cost of the crisis during the contagion process. In addition to providing contagion scenarios, our model allows to probe the structural trading dependencies between countries. For different networks extracted from the world trade exchanges of the last two decades, the global crisis comes from the Western world. In particular, the source of the global crisis is systematically the Old Continent and The Americas (mainly US and Mexico). Besides the economy of Australia, those of Asian countries, such as China, India, Indonesia, Malaysia and Thailand, are the last to fall during the contagion. Also, the four BRIC are among the most robust countries to the world trade crisis.

Interdependence of Sectors of Economic Activities for World Countries from the Reduced Google Matrix Analysis of WTO Data

“…This provides an additional relevant characterization of these interactions for policy makers allowing a better understanding of non obvious indirect economic ties between countries. As an example, our approach can be used by policy makers as a tool to contain economical crisis contagion [ 15 ].…”

“…In addition to the PageRank algorithm, it was shown that the analysis of the WTN with the CheiRank algorithm [ 13 , 14 ], assuming inverted links, plays also an important role for the study of the world trade. Indeed, the PageRank probabilities of nodes are on average proportional to the number of ingoing links characterizing the import capabilities of the economic actors while the CheiRank probabilities are on average proportional to the number of outgoing links, thus, characterizing export capabilities [ 11 , 12 , 15 ]. Since both export and import have to be taken into account to correctly describe the world trade, this clearly shows the importance of the combined PageRank-CheiRank analysis.…”

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

“…Chen et al [33] used complex network theories to measure systemic risks in the stock market and developed dynamic topological indicators to analyse financial contagion and qualify the magnitude of systemic risks. Coquidé et al [34] analysed the world trade network's risk contagion during the global crisis and explored the structural trading dependencies between countries. Constructing undirected and directed volatility networks of the global stock market, Lee et al [35] applied machine learning methods to study network indicators for establishing an international financial portfolio management approach.…”

“…Third, although bull and bear markets share noticeably different core stocks, about 22.5% of stocks (e.g., codes 600861 and 600356) are ranked in the top 20 centrality lists over the entire period, playing unique roles in leading the SSE A-shares market. Lastly, PageRank reflects how likely a given stock is influenced by other stocks, while CheiRank measures the impact of a given stock on the rest ones (We adopted methods from Coquidé et al [34,40] to compute the PageRank and CheiRank). Table 12 shows different results from other measurements and lists those vulnerable stocks in eight stages.…”

The strength of domestic production networks: an economic application of the Finn cycling index

“… 2020 ; Coquidé et al. 2020 ). These studies point to the potential in network analysis as a useful tool in measuring different aspects of the structure of global production networks or supply chains.…”