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Stochastic Migration Models with Application to Corporate Risk

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  • Patrick Gagliardini

    (Crest)

  • Christian Gourieroux

    (Crest)

Abstract

In this paper we explain how to use the rating histories provided by theinternal scoring systems of banks and by rating agencies in order to predictthe future risk of a given borrower or of a set of borrowers. The method isdeveloped following the steps suggested by the Basle Committee. To intro-duce both migration correlation and non-Markovian serial dependence, weconsider rating histories with stochastic transition matrices. We develop thecomplete methodology to estimate both the number and dynamics of thefactors inßuencing the transitions. Further we explain how to use the sto-chastic migration model for prediction. As an illustration the ordered Probitmodel with unobservable dynamic factor is estimated from French data oncorporate risk.

Suggested Citation

  • Patrick Gagliardini & Christian Gourieroux, 2004. "Stochastic Migration Models with Application to Corporate Risk," Working Papers 2004-35, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2004-35
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    Cited by:

    1. 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.
    2. Alain Monfort & Jean-Paul Renne, 2013. "Default, Liquidity, and Crises: an Econometric Framework," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(2), pages 221-262, March.
    3. Christian Gouriéroux & Alain Monfort, 2017. "Composite Indirect Inference with Application," Working Papers 2017-07, Center for Research in Economics and Statistics, revised 28 Mar 2017.
    4. Monteiro, André A., 2009. "The econometrics of randomly spaced financial data: a survey," DES - Working Papers. Statistics and Econometrics. WS ws097924, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833, August.
    6. 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.
    7. Areski Cousin & Mohamed Reda Kheliouen, 2016. "A comparative study on the estimation of factor migration models," Working Papers halshs-01351926, HAL.
    8. Gourieroux, C. & Jasiak, J., 2012. "Granularity adjustment for default risk factor model with cohorts," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1464-1477.
    9. 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.
    10. Stefanescu, Catalina & Tunaru, Radu & Turnbull, Stuart, 2009. "The credit rating process and estimation of transition probabilities: A Bayesian approach," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 216-234, March.
    11. Monica Billio & Roberto Casarin, 2008. "Identifying Business Cycle Turning Points with Sequential Monte Carlo Methods," Working Papers 0815, University of Brescia, Department of Economics.

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