信用风险度量模型.pdf

Evaluating Credit Risk Models
Jose A. Lopez
Economic Research Department
Federal Reserve Bank of San Francisco
101 Market Street
San Francisco, CA 94105-1530
Phone: (415) 977-3894
Fax: (415) 974-2168
.******@sf.
Marc R. Saidenberg
Research and Market Analysis Group
Federal Reserve Bank of New York
33 Liberty Street
New York, NY 10045
Phone: (212) 720-5958
Fax: (212) 720-8363
marc.******@ny.
Draft Date: June 30, 1999
Acknowledgments: The views expressed here are those of the authors and not necessarily those of the Federal
Reserve Bank of New York, the Federal Reserve Bank of San Francisco or the Federal Reserve System. We thank
Beverly Hirtle, William Perraudin, Judy Peng, Anthony Saunders, Philip Strahan, and participants at the Bank of
England’s conference on “Credit Risk Modelling and the Regulatory Implications” for ments and
suggestions.
Evaluating Credit Risk Models
Abstract
Over the past decade, commercial banks have devoted many resources to developing
internal models to better quantify their financial risks and assign economic capital. These efforts
have been recognized and encouraged by bank regulators. Recently, banks have extended these
efforts into the field of credit risk modeling. However, an important question for both banks and
their regulators is evaluating the accuracy of a model’s forecasts of credit losses, especially given
the small number of available forecasts due to their typically long planning horizons. Using a
panel data approach, we propose evaluation methods for credit risk models based on cross-
sectional simulation. Specifically, models are evaluated not only on their forecasts over time, but
also on their forecasts at a given point in time for simulated credit portfolios. Once the forecasts
corresponding to these portfolios are generated, they can be evaluated using various statistical
methods.
I. Introduction
Over the past decade, banks have devoted many resources to