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Analyzing credit risk transmission to the non-financial sector in Europe: A network approach

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  • Christian Gross
  • Pierre L. Siklos

Abstract

We use a factor model and elastic net shrinkage to model a high-dimensional network of European CDS spreads. Our empirical approach allows us to assess the joint transmission of bank and sovereign risk to the non-financial corporate sector. Our findings identify a sectoral clustering in the CDS network, where financial institutions are in the center and non-financial entities as well as sovereigns are grouped around the financial center. The network has a geographical component reflected in different patterns of real-sector risk transmission across countries. Our framework also provides dynamic estimates of risk transmission, a useful tool for systemic risk monitoring.

Suggested Citation

  • Christian Gross & Pierre L. Siklos, 2019. "Analyzing credit risk transmission to the non-financial sector in Europe: A network approach," CAMA Working Papers 2019-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2019-43
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    6. Bonaccolto, Giovanni & Borri, Nicola & Consiglio, Andrea, 2023. "Breakup and default risks in the great lockdown," Journal of Banking & Finance, Elsevier, vol. 147(C).
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    8. Alexander Pütz & Pierre L. Siklos & Christoph Sulewski, 2019. "“Who pays the piper calls the tune” – Networks and transaction costs in commodity markets," CQE Working Papers 8819, Center for Quantitative Economics (CQE), University of Muenster.
    9. Yang, Lu, 2023. "Oil price bubbles: The role of network centrality on idiosyncratic sovereign risk," Resources Policy, Elsevier, vol. 82(C).
    10. Julio Gálvez, 2021. "Measuring interconnectedness across institutions and sectors," Financial Stability Review, Banco de España, issue Autumn.
    11. Gül Huyugüzel Kışla & Y. Gülnur Muradoğlu & A. Özlem Önder, 2022. "Spillovers from one country’s sovereign debt to CDS (credit default swap) spreads of others during the European crisis: a spatial approach," Journal of Asset Management, Palgrave Macmillan, vol. 23(4), pages 277-296, July.
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    More about this item

    Keywords

    networks; financial-real linkages; connectedness; systemic risk; credit risk; contagion; large datasets;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F3 - International Economics - - International Finance
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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