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Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective


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  • Siem Jan Koopman

    (VU University Amsterdam)

  • Andre Lucas

    (VU University Amsterdam)

  • Bernd Schwaab

    (VU University Amsterdam)


We determine the magnitude and nature of systematic default risk using 1971{2009) default data from Moody's. We disentangle systematic risk factors due to business cycle effects, common default dynamics (frailty), and industry-specific dynamics (including contagion). To quantify the contribution of each of these factors to default rate volatility we introduce a new and flexible model class for factor structures on non-Gaussian (defaults) and Gaussian (macro factors) data simultaneously. We find that all three types of risk factors (macro, frailty, industry/contagion) are important for default risk. The systematic risk factors account for roughly one third of observed default risk variation. Half of this is captured by macro and financial market factors. The remainder is captured by frailty and industry effects (in roughly equal proportions). The frailty components are particularly relevant in times of stress. Models based only on macro variables may both under-estimate and over-estimate default activity during such times. This indicates that frailty factors do not simply capture missed non-linear responses of defaults to business cycle dynamics. We also find significant differences in the impact of crises on defaults at the sectoral level, implying frailty as well as contagion may play a role in systematic default clustering. Finally, we show that the contribution of frailty and industry factors on top of macro factors is economicallysignificant for assessing portfolio risk.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-004/2.

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Date of creation: 28 Jan 2010
Date of revision: 24 Aug 2010
Handle: RePEc:dgr:uvatin:20100004

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Keywords: systematic default risk; credit portfolio models; mixed-measurement dynamic factor model; frailty-correlated defaults; state space methods; dynamic credit risk management;

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Cited by:
  1. 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, Central Bank of Luxembourg 75, Central Bank of Luxembourg.
  2. Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers, Central Bank of Luxembourg 82, Central Bank of Luxembourg.


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