Abstract #301395

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JSM 2003 Abstract #301395
Activity Number: 64
Type: Invited
Date/Time: Sunday, August 3, 2003 : 6:00 PM to 8:00 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301395
Title: Markov Chain Models for Delinquency: Transition Matrix Estimation and Forecasts
Author(s): Scott D. Grimshaw*+ and William P. Alexander
Companies: Brigham Young University and Capital One
Address: Statistics Department, Provo, UT, 84602,
Keywords: credit scoring ; prepayment modeling ; Bayes multinomial estimation
Abstract:

This poster describes a method for constructing loss forecasts from a loan-level Markov chain model for delinquency. This model represents the monthly movement of a loan between the different stages of delinquency. A Markov chain model with a constant transition matrix fails to capture that (1) loans are not homogeneous in credit quality and (2) repayment behavior is not stationary, since older loans are observed to have different behavior than newer loans. These characteristics require each loan each month to have its own transition matrix. A dynamic transition matrix or delinquency movement matrix can be modeled and estimated for each loan each month. Of particular interest are estimated probabilities on key states from loan-level logistic regression models and Bayesian transition matrix estimation for credit quality classifications.

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