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Monitoring Systemic Risk Basedon Dynamic Thresholds

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  • Kasper Lund-Jensen

Abstract

Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.

Suggested Citation

  • Kasper Lund-Jensen, 2012. "Monitoring Systemic Risk Basedon Dynamic Thresholds," IMF Working Papers 12/159, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:12/159
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    References listed on IDEAS

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    1. Domac, Ilker & Martinez Peria, Maria Soledad, 2003. "Banking crises and exchange rate regimes: is there a link?," Journal of International Economics, Elsevier, vol. 61(1), pages 41-72, October.
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    3. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    4. Luc Laeven & Fabian Valencia, 2010. "Resolution of Banking Crises; The Good, the Bad, and the Ugly," IMF Working Papers 10/146, International Monetary Fund.
    5. Kaminsky, Graciela L. & Reinhart, Carmen M., 2000. "On crises, contagion, and confusion," Journal of International Economics, Elsevier, vol. 51(1), pages 145-168, June.
    6. Carmen M. Reinhart & Kenneth S. Rogoff, 2011. "From Financial Crash to Debt Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 1676-1706, August.
    7. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    8. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    9. Fabian Valencia & Luc Laeven, 2008. "Systemic Banking Crises; A New Database," IMF Working Papers 08/224, International Monetary Fund.
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    Cited by:

    1. Sarlin, Peter, 2013. "On policymakers’ loss functions and the evaluation of early warning systems," Economics Letters, Elsevier, vol. 119(1), pages 1-7.
    2. Judith Eidenberger & Benjamin Neudorfer & Michael Sigmund & Ingrid Stein, 2013. "Quantifying Financial Stability in Austria, New Tools for Macroprudential Supervision," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 26, pages 62-81.
    3. Bruno, Valentina & Shin, Hyun Song, 2015. "Capital flows and the risk-taking channel of monetary policy," Journal of Monetary Economics, Elsevier, vol. 71(C), pages 119-132.
    4. Dr. Michael Sigmund & Dr. Ingrid Stein, 2017. "What predicts Financial (In)Stability? A Bayesian Approach," Credit and Capital Markets, Credit and Capital Markets, vol. 50(3), pages 299-336.
    5. Hamdaoui, Mekki, 2016. "Are systemic banking crises in developed and developing countries predictable?," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 114-138.

    More about this item

    Keywords

    Economic models; Financial risk; Financial stability; Banking sector; Banking crisis; Macroprudential Policy; Systemic Risk; banking; systemic banking crisis; banking crises;

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