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...

Model uncertainty remains a challenge to researchers in different applications. When many competing models are available for estimation, and without enough guidance from theory, model averaging represents an alternative to model selection. Despite model averaging approaches have been present in...

In this contribution we aim at improving ordinal variable selection in the context of causal models. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate, and, therefore, to select an effective model for classification...

To estimate the probability of default of companies and the correlated rating classes, it is necessary to use efficiently the information contained in different databases. In this respect, we propose a novel approach, based on the recursive usage of Bayes theorem, that can be very helpful in...

The recent European sovereign debt crisis clearly illustrates the importance of measuring the contagion effects of bank failures. Indeed, to better understand and monitor contagion risk, the European Central Bank is assuming the supervision of the largest banks in each of the member states. We...

Considering the attention placed on SMEs in the new Basel Capital Accord, we propose a set of Bayesian and classical longitudinal models to predict SME default probability, taking unobservable firm and business sector heterogeneities as well as analysts’ recommendations into account. We...