Evaluating the performance of Static versus Dynamic models of credit default: evidence from long-term Small Business Administration-guarenteed loans

ABSTRACT The financial crisis exposed the limitations of credit risk models to risk managers, financial regulators, investors and rating agencies. We compare the performance of conventional static-scoring techniques employed in practice with dynamic survival-time models to predict dollar losses on a portfolio of smallbusiness loans.We find that the dynamic models consistently generate more accurate dollar-loss forecasts over multiple time periods and performance horizons. Our results support the hypothesis that seasoning is a key factor in the development of accurate loss forecasts for longer-term amortizing loans (eg, small-business and mortgage loans). Furthermore, our results suggest that banks consider developing capital adequacy, loan-loss provisioning and securitized loan valuation models with a dynamic sample and model design.