Graphical network models for international financial flows

The late-2000s financial crisis has stressed the need of understanding the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, that take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of graphical Gaussian models, which can be employed to accurately estimate the adjacency matrix, the main input for the estimation of the interconnections between different countries. We consider different types of graphical models: besides classical ones, we introduce Bayesian graphical models, that can take model uncertainty into account, and dynamic Bayesian graphical models, that provide a convenient framework to model temporal cross-border data, decomposing the model into autoregressive and contemporaneous networks. The paper shows how the application of the proposed models to the Bank of International Settlements locational banking statistics allows the identification of four distinct groups of countries, that can be considered central in systemic risk contagion.