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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/26326
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| Title: | Estimation in the continuous time mover-stayer model with an application
to bond ratings migration |
| Authors: | Frydman, Halina
Kadam, Ashay |
| Keywords: | Ratings migration
mover-stayer model
Markov chain
estimation |
| Issue Date: | 19-Dec-2002 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-2003-2 |
| Abstract: | The usual tool for modeling bond ratings migration is a discrete,
timehomogeneuous Markov chain. Such model assumes that all bonds are
homogeneous with respect to their movement behavior among rating
categories and that the movement behavior does not change over time.
However, among recognized sources of heterogeneity in ratings migration
is age of a bond (time elapsed since issuance). It has been observed
that young bonds have a lower propensity to change ratings, and thus to
default, than more seasoned bonds. The aimof this paper is to introduce
a continuous, time-nonhomogeneuous model for bond ratings migration,
which also incorporates a simple form of population heterogeneity. The
specific form of heterogeneity postulated by the proposed model appears
to be suitable for modeling the effect of age of a bond on its
propensity to change ratings. This model, called a mover-stayer model,
is an extension of a time-nonhomogeneuous Markov chain. This paper
derives the maximum likelihood estimators for the parameters of a
continuous time mover-stayer model based on a sample of independent
continuously monitored histories of the process, and develops the
likelihood ratio test for discriminating between the Markov chain and
the mover-stayer model. The methods are illustrated using a sample of
rating histories of young corporate issuers. For this sample, the
likelihood ratio test rejects a Markov chain in favor of a mover-stayer
model. For young bonds with lowest rating the default probabilities
predicted by the mover-stayer model are substantially lower than those
predicted by the Markov chain. |
| URI: | http://hdl.handle.net/2451/26326 |
| Appears in Collections: | IOMS: Statistics Working Papers
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