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Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008

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  • Koopman, Siem Jan
  • Lucas, André
  • Schwaab, Bernd

Abstract

We develop a high-dimensional and partly nonlinear non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into a set of latent components that correspond with macroeconomic/financial, default-specific (frailty), and industry-specific effects. Discrete default counts together with macroeconomic and financial variables are modeled simultaneously in this framework. In our empirical study based on defaults of U.S. firms, we find that approximately 35 percent of default rate variation is due to systematic and industry factors. Approximately one third of systematic variation is captured by macroeconomic/financial factors. The remainder is captured by frailty (about 40 percent) and industry (about 25 percent) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. For example, we detect a build-up of systematic risk over the period preceding the 2008 credit crisis. JEL Classification: C33, G21

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1459.

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Date of creation: Aug 2012
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Handle: RePEc:ecb:ecbwps:20121459

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Keywords: credit portfolio models; default risk; financial crisis; frailty-correlated defaults; state space methods;

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  1. 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.
  2. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  3. 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.
  4. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
  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, 02.
  6. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models," LEM Papers Series 2007/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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Cited by:
  1. Barbara Choroś-Tomczyk & Wolfgang Karl Härdle & Ostap Okhrin, 2013. "CDO Surfaces Dynamics," SFB 649 Discussion Papers SFB649DP2013-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Campolieti, Michele & Gefang, Deborah & Koop, Gary, 2014. "A new look at variation in employment growth in Canada: The role of industry, provincial, national and external factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 257-275.
  3. Eser, Fabian & Schwaab, Bernd, 2013. "Assessing asset purchases within the ECB’s securities markets programme," Working Paper Series 1587, European Central Bank.
  4. Sant'Anna, Pedro H. C., 2013. "Testing for Uncorrelated Residuals in Dynamic Count Models with an Application to Corporate Bankruptcy," MPRA Paper 48376, University Library of Munich, Germany.

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