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Ms. Yevgeniya Korniyenko, Manasa Patnam, Rita Maria del Rio-Chanon, and Mason A. Porter
This paper studies the interconnectedness of the global financial system and its susceptibility to shocks. A novel multilayer network framework is applied to link debt and equity exposures across countries. Use of this approach—that examines simultaneously multiple channels of transmission and their important higher order effects—shows that ignoring the heterogeneity of financial exposures, and simply aggregating all claims, as often done in other studies, can underestimate the extent and effects of financial contagion.The structure of the global financial network has changed since the global financial crisis, impacted by European bank’s deleveraging and higher corporate debt issuance. Still, we find that the structure of the system and contagion remain similar in that network is highly susceptible to shocks from central countries and those with large financial systems (e.g., the USA and the UK). While, individual European countries (excluding the UK) have relatively low impact on shock propagation, the network is highly susceptible to the shocks from the entire euro area. Another important development is the rising role of the Asian countries and the noticeable increase in network susceptibility to shocks from China and Hong Kong SAR economies.
Ms. Yevgeniya Korniyenko, Manasa Patnam, Rita Maria del Rio-Chanona, Mason A. Porter, and Mr. Vikram Haksar

multiplex network can be found in Annex B . B. Conjunctural Analysis of Global Financial Network Using Multiplex Networks Tools In this section, we analyze the global financial network properties and dynamics by comparing multilayer and aggregated networks over time. For each year and each asset type in the sample, we calculate several basic network statistics (B.1), we then conduct a more detail analysis of important network players using PageRank centrality measures (B.2). 1. Basic measures of network structure For the broad comparison of the global

Ms. Yevgeniya Korniyenko, Manasa Patnam, Rita Maria del Rio-Chanon, and Mason A. Porter

measures which have been generalized for the multiplex framework and used for the purpose of this research. Some structural network measures are naturally extrapolated in the mutliplex framework ( Battiston, Nicosia, and Latora, 2014 ), for example, the strength of node i on layer α is given by s i [ α ] = Σ j ω i j [ α ] . Similar is true for in and out strength. The generalization of eigenvector centrality and PageRank centrality measures is not straight forward and has been extrapolated differently (we follow approach used by ( De Domenico and others, 2015