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Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

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Author Info

  • Drew Creal

    (University of Chicago, Booth School of Business)

  • Bernd Schwaab

    (European Central Bank)

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Andre Lucas

    (VU University Amsterdam)

Abstract

This paper has been accepted for publication in the 'Review of Economics and Statistics'.We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common dynamics and cross-sectional dependence due to shared exposure to dynamic latent factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obtained by means of simulation, thus enabling straightforward parameter estimation by standard maximum likelihood. We use the new mixed-measurement framework for the signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 until March 2010.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-042/2/DSF16.

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Date of creation: 21 Feb 2011
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Handle: RePEc:dgr:uvatin:20110042

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Web page: http://www.tinbergen.nl

Related research

Keywords: panel data; loss given default; default risk; dynamic beta density; dynamic ordered probit; dynamic factor model;

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References

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  1. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," CEPR Discussion Papers 5724, C.E.P.R. Discussion Papers.
  2. Siem Jan Koopman & André Lucas & André Monteiro, 2005. "The Multi-State Latent Factor Intensity Model for Credit Rating Transitions," Tinbergen Institute Discussion Papers 05-071/4, Tinbergen Institute, revised 04 Jul 2005.
  3. 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.
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  8. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
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  10. Siem Jan Koopman & Roman Kraeussl & Andre Lucas & Andre Monteiro, 2006. "Credit Cycles and Macro Fundamentals," Tinbergen Institute Discussion Papers 06-023/2, Tinbergen Institute.
  11. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September.
  12. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  13. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  14. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  15. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  17. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  18. 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.
  19. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
  20. Connor, Gregory & Korajczyk, Robert A., 1988. "Risk and return in an equilibrium APT : Application of a new test methodology," Journal of Financial Economics, Elsevier, vol. 21(2), pages 255-289, September.
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Citations

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Cited by:
  1. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2012. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," Tinbergen Institute Discussion Papers 12-020/4, Tinbergen Institute.
  2. James Wolter, 2013. "Separating the impact of macroeconomic variables and global frailty in event data," Economics Series Working Papers 667, University of Oxford, Department of Economics.
  3. Eser, Fabian & Schwaab, Bernd, 2013. "Assessing asset purchases within the ECB’s securities markets programme," Working Paper Series 1587, European Central Bank.
  4. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.

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