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Hierarchical Graphical Models, With Application To Systemic Risk

Author

Listed:
  • Daniel Felix Ahelegbey

    (Department of Economics, University of Venice Ca' Foscari)

  • Paolo Giudici

    (Department of Economics and Management, University of Pavia)

Abstract

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 algorithm based on Bayes factor thresholding.

Suggested Citation

  • Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application To Systemic Risk," DEM Working Papers Series 063, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0063
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0063.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Applied Bayesian models; Graphical Gaussian Models; Systemic financial risk;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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