Hierarchical Graphical Models, With Application to Systemic Risk
Daniel Felix Ahelegbey () and
Paolo Giudici ()
No 2014:01, Working Papers from Department of Economics, University of Venice "Ca' Foscari"
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 graphical Gaussian 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 on-going banking union process in Europe. From a computational viewpoint, we develop a novel Markov Chain Monte Carlo algorithmbased on Bayes factor thresholding.
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://www.unive.it/pag/fileadmin/user_upload/dipa ... ey_giudici_01_14.pdf Revised version,
Working Paper: HIERARCHICAL GRAPHICAL MODELS, WITH APPLICATION TO SYSTEMIC RISK (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2014:01
Access Statistics for this paper
More papers in Working Papers from Department of Economics, University of Venice "Ca' Foscari" Contact information at EDIRC.
Bibliographic data for series maintained by Geraldine Ludbrook ().