Bayesian Selection of Systemic Risk Networks

What is it about?

This paper develops a stochastic framework for financial network models, aimed at a more parsimonious and more realistic representation. Bayesian Gaussian graphical models are thus introduced in the field of systemic risk modeling, by estimating the adjacency matrix of a network of financial institutions in a robust and coherent way. The model is applied to study the largest banks in the Euro-area with the aim of identifying central institutions.

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Why is it important?

Our results show that, in the transmission of the perceived default risk, there is a strong country effect, which reflects the weakness and the strength of the underlying economies. Besides the country effect, the most central banks appear the large international ones, especially if from a relatively small country.