Bank lending policy, credit scoring and value-at-risk Export

@article{2003_JacobsonRoszbach, abstract = {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 bank's 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-risk-based rule) of loan applicants can reduce credit risk by up to 80\%. (C) 2002 Elsevier Science B.V. All rights reserved.}, author = {Jacobson, T. and Roszbach, K.}, citeulike-article-id = {651473}, journal = {Journal of Banking and Finance}, keywords = {banks, bivariate, consumer, credit, lending, policy, probit, risk, scoring, value-at-risk}, number = {4}, pages = {615--633}, posted-at = {2006-05-18 18:21:35}, priority = {0}, title = {Bank lending policy, credit scoring and value-at-risk}, volume = {27}, year = {2003} }

TY - JOUR ID - 2003_JacobsonRoszbach L3 - citeulike-article-id:651473 N2 - 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 bank's 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-risk-based rule) of loan applicants can reduce credit risk by up to 80%. (C) 2002 Elsevier Science B.V. All rights reserved. IS - 4 JF - Journal of Banking and Finance EP - 633 TI - Bank lending policy, credit scoring and value-at-risk VL - 27 SP - 615 KW - banks KW - bivariate KW - consumer KW - credit KW - lending KW - policy KW - probit KW - risk KW - scoring KW - value-at-risk AU - Jacobson, T AU - Roszbach, K PY - 2003/// ER -