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Bank Lending Policy, Credit Scoring, and the Survival of Loans

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  • Kasper Roszbach

    (Sveriges Riksbank)

Abstract

To evaluate loan applicants, banks increasingly use credit scoring models. The objective of such models typically is to minimize default rates or the number of incorrectly classified loans. Thereby they fail to take into account that loans are multiperiod contracts, for which reason it is important for banks not only to know if but also when a loan will default. In this paper a bivariate tobit model with a variable censoring threshold and sample selection effects is estimated for (1) the decision to provide a loan or not and (2) the survival time of granted loans. The model proves to be an effective tool to separate applicants with short and with long survival times. The bank's loan provision process is shown to be inefficient: loans are granted in a way that conflicts with both default risk minimization and survival time maximization. There is thus no trade-off between higher default risk and higher return in the lending policy. © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 86 (2004)
Issue (Month): 4 (November)
Pages: 946-958

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Handle: RePEc:tpr:restat:v:86:y:2004:i:4:p:946-958

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  1. Stephen D. Williamson, 1984. "Costly Monitoring, Loan Contracts and Equilibrium Credit Rationing," Working Papers, Queen's University, Department of Economics 572, Queen's University, Department of Economics.
  2. Carling, Kenneth & Jacobson, Tor & Roszbach, Kasper, 2001. "Dormancy risk and expected profits of consumer loans," Journal of Banking & Finance, Elsevier, Elsevier, vol. 25(4), pages 717-739, April.
  3. Guillen, Montserrat & Manuel Artis, 1994. "Count Data Models For A Credit Scoring System," Working Papers, Risk and Insurance Archive 021, Risk and Insurance Archive.
  4. Gale, Douglas & Hellwig, Martin, 1985. "Incentive-Compatible Debt Contracts: The One-Period Problem," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 52(4), pages 647-63, October.
  5. Townsend, Robert M, 1982. "Optimal Multiperiod Contracts and the Gain from Enduring Relationships under Private Information," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 90(6), pages 1166-86, December.
  6. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series in Economics and Finance, Stockholm School of Economics 260, Stockholm School of Economics.
  7. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 74(1), pages 101-24, January.
  8. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, American Economic Association, vol. 71(3), pages 393-410, June.
  9. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, Elsevier, vol. 40(1), pages 3-14, January.
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Cited by:
  1. Santos Silva, J.M.C. & Murteira, J.M.R., 2009. "Estimation of default probabilities using incomplete contracts data," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(3), pages 457-465, June.
  2. Ragasa, Catherine & Thornsbury, Suzanne & Joshi, Satish, 2013. "Sustainability of EU Food Safety Certification: A survival analysis of firm decisions:," IFPRI discussion papers, International Food Policy Research Institute (IFPRI) 1296, International Food Policy Research Institute (IFPRI).
  3. Bertola, Giuseppe & Hochguertel, Stefan, 2007. "Household debt and credit: Economic issues and data problems," CFS Working Paper Series, Center for Financial Studies (CFS) 2007/32, Center for Financial Studies (CFS).
  4. DeYoung, Robert & Glennon, Dennis & Nigro, Peter, 2008. "Borrower-lender distance, credit scoring, and loan performance: Evidence from informational-opaque small business borrowers," Journal of Financial Intermediation, Elsevier, Elsevier, vol. 17(1), pages 113-143, January.
  5. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series in Economics and Finance, Stockholm School of Economics 260, Stockholm School of Economics.
  6. Diana Bonfim, 2007. "Credit Risk Drivers: Evaluating the Contribution of Firm Level Information and of Macroeconomic Dynamics," Working Papers, Banco de Portugal, Economics and Research Department w200707, Banco de Portugal, Economics and Research Department.
  7. Madsen, Jakob B., 2012. "A behavioral model of house prices," Journal of Economic Behavior & Organization, Elsevier, Elsevier, vol. 82(1), pages 21-38.
  8. Marshall, Andrew & Tang, Leilei & Milne, Alistair, 2010. "Variable reduction, sample selection bias and bank retail credit scoring," Journal of Empirical Finance, Elsevier, Elsevier, vol. 17(3), pages 501-512, June.
  9. Bechlioulis, Alexandros & Brissimis, Sophocles, 2014. "Consumer default and optimal consumption decisions," MPRA Paper 56864, University Library of Munich, Germany.
  10. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden) 168, Sveriges Riksbank (Central Bank of Sweden).
  11. Jakob B Madsen, 2011. "A Repayment Model of House Prices," Development Research Unit Working Paper Series, Monash University, Department of Economics 09-11, Monash University, Department of Economics.

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