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Journal of Banking & Finance Vol. 24 No. 1-2 2000

Evaluating credit risk models

Jose A. Lopez1; Marc R. Saidenberg2,3

1 Federal Reserve Bank of San Francisco · 2 Federal Reserve Bank of New York · 3 Analysis Group (United States)

open access

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.

DOI
10.1016/s0378-4266(99)00055-2
Volume
24
Issue
1-2
Pages
151-165
Language
en
Sources
openalex crossref bibtex:phds-export.bib

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