The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.
We acknowledge financial support from the Italian ministry PRIN MISURA project: Multivariate models for risk assessment. We also thank the referee for useful comments and discussion that have helped improving the content of the paper.
Felix Ahelegbey, D. and Giudici, P. (2014), "Bayesian Selection of Systemic Risk Networks", Bayesian Model Comparison (Advances in Econometrics, Vol. 34), Emerald Group Publishing Limited, Bingley, pp. 117-153. https://doi.org/10.1108/S0731-905320140000034007
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