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2007 | nr 17 | 203--224
Tytuł artykułu

Credit Risk Analysis Using Failure Time Models

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Credit granting institutions have to face the risk that some amount of money that has been lent will not be repaid. Therefore banks and other lending institutions are concerned with the issue of measuring the risk of each credit default, which is needed to accept or reject credit applications. The aim of this paper is to present an approach for classifying applicants, based on duration data - survival analysis. Including an extra dimension - time - in the analysis allows the lending institution to take into account the profitability of a loan. Moreover, failure models give an opportunity to include in the model time-varying covariates - regressors that change in time of the repayment. They make it possible to use in the estimation data about the credits that are still being repayed - censored durations. This method is also a useful tool to predict the influence of the particular characteristics on the probability and the expected time of exit to different kinds of states - complete repayment on time, default but also e.g. an early repayment. (original abstract)
Rocznik
Numer
Strony
203--224
Opis fizyczny
Twórcy
  • Szkoła Główna Handlowa w Warszawie
Bibliografia
  • Banasik J., Crook J. N., Thomas L. C. [1999], Not if but when will borrowers default, The Journal of the Operational Research Society, Vol. 50, No. 12., pp. 1185-1190.
  • Cameron A. C., Trivedi P. K. [2005], Microeconometrics. Methods and Applications, Cambridge University Press.
  • Cox D. R. [1972], Partial likelihood, Biometrika, Vol. 62, No. 2., pp. 269-276.
  • Cox D. R. [1972], Regression models and life-tables, Journal of the Royal Statistical Society, Series B, Vol. 34, No. 2, pp. 187-220.
  • Gray R. [1996], Hazard rate regression using ordinary nonparametric regression smoothers, Journal of Computational and Graphical Statistics, Vol. 5, No. 2, pp. 190-207.
  • Kalbfleisch J.D. and Prentice R.L. [1980], The Statistical Analysis of Failure Time Data, Wiley.
  • Kiefer N. M. [1988], Economic duration data and hazardfunctions, Journal of Economic Literature, Vol. 26, No. 2., pp. 646-679.
  • Klein J.P. Moeschberger M.L., [1997], Survival Analysis: Techniques for Censored and Truncated Data, Springer.
  • Narain B. [1992], Survival analysis and the credit granting decision, In: Thomas LC, Crook JN and Edelman DB (eds). Credit Scoring and Credit Control, Oxford University Press, pp. 109-121.
  • Rosenberg E., Gleit A. [1994], Quantitative Methods in Credit Management: A Survey, The Journal of the Operations Research, Vol. 42, No. 4., pp. 589-613.
  • Stepanova M., Thomas L. C. [2001], PHAB scores:proportional hazards analysis behavioural scores, The Journal of the Operational Research Society, Vol. 52, No. 2., pp. 1007-1016.
  • Stepanova M., Thomas L. C. [2002], Survival analysis methods for personal loan data, The Journal of the Operational Research Society, Vol. 50, No. 2.,pp. 277-289.
  • http://ftp.ics.uci.edu/pub/machine-leaming-databases/statlog/german
  • http://www.r-project.org
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171217487

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