We focus on the problem of the estimation of the credit migration matrices which are a widely used instrument in the context of risk management to account for the credit portfolio. We propose to apply a latent Markov model in which the possible movement directions are modeled as a continuous or discrete-valued stochastic process following a non-homogenous Markov chain of first or second order. It is a novel approach in this context, which outperform the existing models mainly based on an observed Markov process. The latter do not satisfactory represent the nature of the credit ratings which is mainly governed by underlying forces. We illustrate the traditional techniques employed in this field and their limitations in comparison to our proposal by applying them to simulated data which reproduce the credit ratings histories of Standard and Poor rated firms and sovereigns around the world from 1982 to 2014. We also show how the model may be of interest for prediction of default
Pennoni, F., Elisei, G. (2015). A discrete-valued latent stochastic process for the estimation of credit migration matrices. Intervento presentato a: CFE-CMSTATUSTICS International Conference on Computational and Financial Econometrics and International Conference of the ERCIM Working Group on Computational and Methodological Statistics 12-14 December, Senate House and Birkbeck Univerisity of London.
A discrete-valued latent stochastic process for the estimation of credit migration matrices
PENNONI, FULVIAPrimo
;
2015
Abstract
We focus on the problem of the estimation of the credit migration matrices which are a widely used instrument in the context of risk management to account for the credit portfolio. We propose to apply a latent Markov model in which the possible movement directions are modeled as a continuous or discrete-valued stochastic process following a non-homogenous Markov chain of first or second order. It is a novel approach in this context, which outperform the existing models mainly based on an observed Markov process. The latter do not satisfactory represent the nature of the credit ratings which is mainly governed by underlying forces. We illustrate the traditional techniques employed in this field and their limitations in comparison to our proposal by applying them to simulated data which reproduce the credit ratings histories of Standard and Poor rated firms and sovereigns around the world from 1982 to 2014. We also show how the model may be of interest for prediction of default
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