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Evaluating Credit Risk Models

by Jose A. Lopez of the Federal Reserve Bank of San Francisco and
Marc R. Saidenberg of the Federal Reserve Bank of New York

June 30, 1999

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 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.

Published in: Journal of Banking & Finance, Vol. 24, No. 1-2, (January 2000), pp. 151-165.

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