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A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk

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  • Siem Jan Koopman
  • André Lucas
  • Robert J. Daniels

Abstract

We model 19812002 annual default frequencies for a panel of US firms in different rating and age classes from the Standard and Poor's database. The data is decomposed into a systematic and firm-specific risk component, where the systematic component reflects the general economic conditions and default climate. We have to cope with (i) the shared exposure of each age cohort and rating class to the same systematic risk factor; (ii) strongly non-Gaussian features of the individual time series; (iii) possible dynamics of an unobserved common risk factor; (iv) changing default probabilities over the age of the rating, and (v) missing observations. We propose a non-Gaussian ultivariate state space model that deals with all of these issues simultaneously. The model is estimated using importance sampling techniques that have been modified to a multivariate setting. We show in a simulation study that such a multivariate approach improves the performance of the importance sampler.

Suggested Citation

  • Siem Jan Koopman & André Lucas & Robert J. Daniels, 2005. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," DNB Working Papers 055, Netherlands Central Bank, Research Department.
  • Handle: RePEc:dnb:dnbwpp:055
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    References listed on IDEAS

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    1. Michel Dietsch, 2004. "Should SME exposures be treated as retail or corporate exposures: a comparative analysis of probabilities of default and assets correlations in French and German SMEs," ULB Institutional Repository 2013/14164, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2014. "Nowcasting and forecasting global financial sector stress and credit market dislocation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 741-758.
    2. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    3. Bernd Schwaab & Siem Jan Koopman & André Lucas, 2017. "Global Credit Risk: World, Country and Industry Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 296-317, March.
    4. Weißbach, Rafael & Mollenhauer, Thomas, 2011. "Modelling Rating Transitions," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48698, Verein für Socialpolitik / German Economic Association.
    5. 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.
    6. Carlos Castro, 2012. "Confidence sets for asset correlations in portfolio credit risk," REVISTA DE ECONOMÍA DEL ROSARIO, UNIVERSIDAD DEL ROSARIO, June.
    7. Konrad Banachewicz & André Lucas, 2008. "Quantile forecasting for credit risk management using possibly misspecified hidden Markov models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 566-586.
    8. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor van der Geest, 2008. "Estimating systematic continuous-time trends in recidivism using a non-Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130.
    9. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    10. Abel Elizalde, 2006. "CREDIT RISK MODELS IV: UNDERSTANDING AND PRICING CDOs," Working Papers wp2006_0608, CEMFI.
    11. Konrad Banachewicz & André Lucas & Aad van der Vaart, 2008. "Modelling Portfolio Defaults Using Hidden Markov Models with Covariates," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 155-171, March.
    12. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    13. 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.
    14. Nikola A. Tarashev & Haibin Zhu, 2006. "The pricing of portfolio credit risk," BIS Working Papers 214, Bank for International Settlements.
    15. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    16. Andre Lucas & Bastiaan Verhoef, 2012. "Aggregating Credit and Market Risk: The Impact of Model Specification," Tinbergen Institute Discussion Papers 12-057/2/DSF36, Tinbergen Institute.
    17. Zhu, Haibin & Tarashev, Nikola A., 2008. "The pricing of correlated default risk: evidence from the credit derivatives market," Discussion Paper Series 2: Banking and Financial Studies 2008,09, Deutsche Bundesbank.
    18. Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2011. "Systemic risk diagnostics: coincident indicators and early warning signals," Working Paper Series 1327, European Central Bank.
    19. Bernd Schwaab & Andre Lucas & Siem Jan Koopman, 2010. "Systemic Risk Diagnostics," Tinbergen Institute Discussion Papers 10-104/2/DSF 2, Tinbergen Institute, revised 29 Nov 2010.
    20. Neumann, Tobias, 2018. "Mortgages: estimating default correlation and forecasting default risk," Bank of England working papers 708, Bank of England.
    21. Michael Kalkbrener & Akwum Onwunta, 2009. "Validating Structural Credit Portfolio Models," Working Papers 014, COMISEF.
    22. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 2(1), pages 1-22, March.

    More about this item

    Keywords

    credit risk; multivariate unobserved component models; importance sampling; non-Gaussian state space models.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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