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Stochastic Migration Models with Application to Corporate Risk

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  • Patrick Gagliardini

Abstract

In this article we explain how to use rating histories provided by the internal scoring systems of banks and rating agencies in order to predict the future risk of a set of borrowers. The method is developed following the steps suggested by the Basle Committee. To introduce both migration correlation and non-Markovian serial dependence, we consider rating histories with stochastic transition matrices. We develop the methodology to estimate both the number and dynamics of the factors influencing the transitions and we explain how to use the model for prediction. As an illustration, the ordered probit model with unobservable dynamic factor is estimated from French data on corporate risk. Copyright 2005, Oxford University Press.

Suggested Citation

  • Patrick Gagliardini, 2005. "Stochastic Migration Models with Application to Corporate Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(2), pages 188-226.
  • Handle: RePEc:oup:jfinec:v:3:y:2005:i:2:p:188-226
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbi013
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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. 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.
    4. 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.
    5. 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.
    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. 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.
    10. 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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