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Modelling credit grade migration in large portfolios using cumulative t-link transition models

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  • Forster, Jonathan J.
  • Buzzacchi, Matteo
  • Sudjianto, Agus
  • Nagao, Risa

Abstract

For a credit portfolio, we are often interested in modelling the migration of accounts between credit grades over time. For a large retail portfolio, data on credit grade migration may be available only in the form of a series of (typically monthly) population transition matrices representing the gross flow of accounts between each pair of credit grades in the given time period. The challenge is to model the transition process on the basis of these aggregate flow matrices. Each row of an observed transition matrix represents a sample from an ordinal probability distribution. Following Malik and Thomas (2012), Feng, Gourieroux, and Jasiak (2008) and McNeil and Wendin (2006), we assume a cumulative link model for these ordinal distributions. Common choices of link function are based on the normal (probit link) or logistic distributions, but the fit to observed data can be poor. In this paper, we investigate the fit of alternative link specifications based on the t-distribution. Such distributions arise naturally when modelling data which arise through aggregating an inhomogeneous sample of obligors, by combining a simple structural-type model for credit migration at the obligor level, with a suitable mixing distribution to model the variability between obligors.

Suggested Citation

  • Forster, Jonathan J. & Buzzacchi, Matteo & Sudjianto, Agus & Nagao, Risa, 2016. "Modelling credit grade migration in large portfolios using cumulative t-link transition models," European Journal of Operational Research, Elsevier, vol. 254(3), pages 977-984.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:3:p:977-984
    DOI: 10.1016/j.ejor.2016.03.017
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    References listed on IDEAS

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    1. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
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