IDEAS home Printed from https://ideas.repec.org/p/pav/demwpp/demwp0063.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0063.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alessio Moneta, 2008. "Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis," Empirical Economics, Springer, vol. 35(2), pages 275-300, September.
    2. Jukka Corander & Mattias Villani, 2006. "A Bayesian Approach to Modelling Graphical Vector Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 141-156, January.
    3. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, July - De.
    4. David A. Bessler & Seongpyo Lee, 2002. "Money and prices: U.S. Data 1869-1914 (A study with directed graphs)," Empirical Economics, Springer, vol. 27(3), pages 427-446.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. repec:spr:stmapp:v:13:y:2004:i:3:d:10.1007_s10260-004-0097-z is not listed on IDEAS
    7. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    8. Corander, Jukka, 2003. "Bayesian graphical model determination using decision theory," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 253-266, May.
    9. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    10. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    11. repec:sbe:breart:v:16:y:1996:i:1:a:2878 is not listed on IDEAS
    12. Marco Grzegorczyk & Dirk Husmeier & Jörg Rahnenführer, 2011. "Modelling non-stationary dynamic gene regulatory processes with the BGM model," Computational Statistics, Springer, vol. 26(2), pages 199-218, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. P. Giudici & A. Spelta, 2016. "Graphical Network Models for International Financial Flows," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 128-138, January.
    2. Paolo Giudici & Laura Parisi, 2016. "Bail in or Bail out? The Atlante example from a systemic risk perspective," DEM Working Papers Series 124, University of Pavia, Department of Economics and Management.
    3. Paolo Giudici & Laura Parisi, 2016. "CoRisk: measuring systemic risk through default probability contagion," DEM Working Papers Series 116, University of Pavia, Department of Economics and Management.
    4. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2019. "Effectiveness of policy and regulation in European sovereign credit risk markets: A network analysis," Working Paper Series in Economics 125, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
    5. Monica Billio & Roberto Casarin & Matteo Iacopini, 2018. "Bayesian Markov Switching Tensor Regression for Time-varying Networks," Working Papers 2018:14, Department of Economics, University of Venice "Ca' Foscari".
    6. repec:taf:quantf:v:17:y:2017:i:12:p:1995-2008 is not listed on IDEAS
    7. repec:gam:jrisks:v:6:y:2018:i:3:p:95-:d:169274 is not listed on IDEAS
    8. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    9. repec:gam:jecnmx:v:4:y:2016:i:1:p:13:d:65308 is not listed on IDEAS
    10. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Nikolay Arefiev, 2016. "Graphical Interpretations of Rank Conditions For Identification of Linear Gaussian Models," HSE Working papers WP BRP 124/EC/2016, National Research University Higher School of Economics.
    12. Urbi Garay & Enrique Ter Horst & German Molina & Abel Rodriguez, 2016. "Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-23, March.
    13. repec:taf:jnlbes:v:36:y:2018:i:1:p:101-114 is not listed on IDEAS
    14. repec:gam:jrisks:v:7:y:2019:i:1:p:3-:d:195087 is not listed on IDEAS
    15. Carota, Cinzia & Durio, Alessandra & Guerzoni, Marco, 2014. "An Application of Graphical Models to the Innobarometer Survey: A Map of Firms’ Innovative Behaviour," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201444, University of Turin.
    16. Paolo Giudici & Laura Parisi, 2015. "Modeling Systemic Risk with Correlated Stochastic Processes," DEM Working Papers Series 110, University of Pavia, Department of Economics and Management.
    17. Casarin, Roberto & Costola, Michele & Yenerdag, Erdem, 2018. "Financial bridges and network communities," SAFE Working Paper Series 208, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    18. Nikolay Arefiev, 2016. "Identification of Monetary Policy Shocks within a Svar Using Restrictions Consistent with a DSGE Model," HSE Working papers WP BRP 125/EC/2016, National Research University Higher School of Economics.
    19. Daniel Felix Ahelegbey, 2015. "The Econometrics of Networks: A Review," Working Papers 2015:13, Department of Economics, University of Venice "Ca' Foscari".
    20. Paolo Giudici & Peter Sarlin & Alessandro Spelta, 2016. "The multivariate nature of systemic risk: direct and common exposures," DEM Working Papers Series 118, University of Pavia, Department of Economics and Management.

    More about this item

    Keywords

    Applied Bayesian models; Graphical Gaussian Models; Systemic financial risk;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pav:demwpp:demwp0063. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alice Albonico) The email address of this maintainer does not seem to be valid anymore. Please ask Alice Albonico to update the entry or send us the correct email address. General contact details of provider: http://edirc.repec.org/data/dppavit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.