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Systemic Risk Diagnostics

Author

Listed:
  • Bernd Schwaab

    (VU University Amsterdam, and European Central Bank)

  • Andre Lucas

    (VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

Abstract

A macro-prudential policy maker can manage risks to financial stability only if currentand future risks can be reliably assessed. We propose a novel framework to assessfinancial system risk. Using a dynamic factor framework based on state-space methods, we model latent macro-financial and credit risk components for a large data setcomprising the U.S., the EU-27 area, and the rest of the world. Controlling for global,region-specific, and industry effects, we construct coincident measures ('thermometers')and forward looking indicators of financial distress and the likelihood of financial melt-down. We find that credit risk conditions can significantly and persistently de-couplefrom macro-financial fundamentals. Such decoupling can serve as an early warningsignal for macro-prudential policy.

Suggested Citation

  • Bernd Schwaab & Andre Lucas & Siem Jan Koopman, 2010. "Systemic Risk Diagnostics," Tinbergen Institute Discussion Papers 10-104/2/DSF 2, Tinbergen Institute, revised 29 Nov 2010.
  • Handle: RePEc:tin:wpaper:20100104
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    References listed on IDEAS

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    Cited by:

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    2. Andrea Mazzocchetti & Eliana Lauretta & Marco Raberto & Andrea Teglio & Silvano Cincotti, 2020. "Systemic financial risk indicators and securitised assets: an agent-based framework," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 9-47, January.
    3. Ini S Udom & Sani Ibrahim Doguwa, 2015. "Generating a composite index to support monetary and financial stability analysis in Nigeria," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
    4. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2014. "Forecasting systemic impact in financial networks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 781-794.
    5. Kauko, Karlo, 2014. "How to foresee banking crises? A survey of the empirical literature," Economic Systems, Elsevier, vol. 38(3), pages 289-308.
    6. Jin, Xisong & Nadal De Simone, Francisco de A., 2014. "Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 81-101.
    7. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.
    8. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    9. Claudio Morana, 2013. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks: New Insights on the US OIS SPreads Term Structure," Working Papers 233, University of Milano-Bicocca, Department of Economics, revised Feb 2013.

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    More about this item

    Keywords

    financial crisis; systemic risk; credit portfolio models; frailty-correlated defaults; state space methods;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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