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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)

Abstract

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.

Suggested Citation

  • Siem Jan Koopman & Andre Lucas & Bernd Schwaab, 2010. "Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective," Tinbergen Institute Discussion Papers 10-004/2, Tinbergen Institute, revised 24 Aug 2010.
  • Handle: RePEc:tin:wpaper:20100004
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    File URL: https://papers.tinbergen.nl/10004.pdf
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    References listed on IDEAS

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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    5. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
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    Cited by:

    1. 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.
    2. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.
    3. 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.
    4. Xisong Jin & Francisco Nadal De Simone, 2016. "Tracking Changes in the Intensity of Financial Sector's Systemic Risk," BCL working papers 102, Central Bank of Luxembourg.

    More about this item

    Keywords

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

    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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