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Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008

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  • Siem Jan Koopman
  • André Lucas
  • Bernd Schwaab

Abstract

We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default rate variation is due to systematic and industry factors. Approximately one-third of this systematic variation is captured by the macroeconomic and financial factors. The remainder is captured by frailty (40%) and industry (25%) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. We detect a build-up of systematic risk over the period preceding the 2008 credit crisis. This article has online supplementary material.

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File URL: http://hdl.handle.net/10.1080/07350015.2012.700859
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 30 (2012)
Issue (Month): 4 (May)
Pages: 521-532

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Handle: RePEc:taf:jnlbes:v:30:y:2012:i:4:p:521-532

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  1. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
  2. Darrell Duffie & Leandro Siata & Ke Wang, 2006. "Multi-Period Corporate Default Prediction With Stochastic Covariates," NBER Working Papers 11962, National Bureau of Economic Research, Inc.
  3. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A robust criterion for determining the number of static factors in approximate factor models," Working Paper Series 0903, European Central Bank.
  4. Sanjiv Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2006. "Common Failings: How Corporate Defaults are Correlated," NBER Working Papers 11961, National Bureau of Economic Research, Inc.
  5. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  6. 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.
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Cited by:
  1. Michele Campolieti & Deborah Gefang & Gary Koop, 2013. "A New Look at Variation in Employment Growth in Canada: The Role of Industry, Provincial, National and External Factors," Working Papers 26145565, Lancaster University Management School, Economics Department.
  2. Eser, Fabian & Schwaab, Bernd, 2013. "Assessing asset purchases within the ECB’s securities markets programme," Working Paper Series 1587, European Central Bank.
  3. 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.
  4. Barbara Choroś-Tomczyk & Wolfgang Karl Härdle & Ostap Okhrin, 2013. "CDO Surfaces Dynamics," SFB 649 Discussion Papers SFB649DP2013-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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