« Receiver Operating Characteristic (ROC) | Main | BLAST and SAS: String matching algorithms and their application »
January 14, 2005
Bayesian Methods for Improving Credit Scoring Models
Abstract: We propose a Bayesian methodology that enables banks with
small datasets to improve their default probability estimates by
imposing prior information. As prior information, we use coefficients
from credit scoring models estimated on other datasets. Through
simulations, we explore the default prediction power of three Bayesian
estimators in three different scenarios and find that all three
perform better than standard maximum likelihood estimates. We
therefore recommend that banks consider Bayesian estimation for
internal and regulatory default prediction models.
Keywords: Credit Ratings, Rating Agency, Bayesian Inference, Basel II
JEL Classification: C11, G21, G33
Bayesian Methods for Improving Credit Scoring Models
by Gunter Löffler of the University of Ulm,
Peter N. Posch of the University of Ulm, and
Christiane Schoene of the University of Ulm
Posted 2004 December 16.
Posted by omor at January 14, 2005 03:25 PM