This paper builds on the credit-scoring literature and proposes a method to calculate portfolio credit risk. Individual default risk estimates are used to compose a value-at-risk (VaR) measure of credit risk. In general, credit-scoring models suffer from a sample-selection bias. The starting point is therefore to estimate an unbiased scoring model using the bivariate probit approach. The paper uses a large data set with Swedish consumer credit data that contains extensive financial and personal information on both rejected and approved applicants. We study how marginal changes in a default-risk-based acceptance rule would shift the size of the bankÕs loan portfolio, its VaR exposure and average credit losses. Finally, we compare the risk in the sample portfolio with that in an efficiently provided portfolio of equal size. The results show that the size of a small consumer loan does not affect associated default risk, implying that the bank provides loans in a way that is not consistent with default-risk minimization. VaR calculations indicate that an efficient selection (by means of a default-riskbased rule) of loan applicants can reduce credit risk by up to 80%.
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In this paper we empirically study interactions between real activity and the financial stance. Using aggregate data we examine a number of candidate measures of the financial stance of the economy. We find strong evidence for substantial spillover effects on aggregate activity from our preferred measure. Given this result, we use a large micro data-set for corporate firms to develop a macro-micro model of the interaction between the financial and real economy. This approach implies that the impulse responses of a given aggregate shock will depend on the portfolio structure of firms at any given point in time.
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