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What can credit vintages tell us about non-performing loans?


  • Santiago Gamba-Santamaria
  • Luis Fernando Melo-Velandia
  • Camilo Orozco-Vanegas


Using Colombian credit vintage data, we decompose the non-performing loans into one component that captures the evolution of the payment capacity of borrowers, and other component that captures changes in the credit risk taken by the financial system at the time of loan disbursement. We use intrinsic estimators and penalized regression techniques to overcome the perfect multicollinearity problem that the model entails. We find that these two type of components have evolved differently over time, and that good economic conditions and loose financial conditions improve the payment capacity of borrowers to meet their obligations, and in turn, they tend to coincide with the financial system engaging in riskier loans. Finally, we advocate the use of this methodology as a policy tool that is easy to apply by financial and economic authorities that dispose of a constant flow of credit vintage information. Through it, they will be able to identify the origin of the credit risk materialization and curb the risk taken by the financial system. **** RESUMEN: Usando información de cosechas de crédito, en este documento descomponemos la cartera en mora en un componente que captura la evolución de la capacidad de pago de los deudores y otro componente que captura los cambios en la toma de riesgo de crédito del sistema financiero al momento del desembolso. Utilizamos estimadores intrínsecos y técnicas de regresión penalizadas para solucionar el problema de multicolinealidad perfecta asociado a la estimación de los parámetros de los modelos. Encontramos que estos dos tipos de componentes han evolucionado de manera diferente a lo largo del tiempo y que buenas condiciones económicas y condiciones financieras laxas mejoran la capacidad de pago de los deudores para cumplir con sus obligaciones y, a su vez, tienden a coincidir con el otorgamiento de préstamos de mayor riesgo por parte del sistema financiero. Finalmente, recomendamos el uso de esta metodología como herramienta de política de fácil aplicación por parte de las autoridades financieras y económicas que disponen de un flujo constante de información de cosechas de crédito. A través de ella las autoridades podrían identificar el origen de la materialización del riesgo crediticio y contener la toma de riesgo del sistema financiero.

Suggested Citation

  • Santiago Gamba-Santamaria & Luis Fernando Melo-Velandia & Camilo Orozco-Vanegas, 2021. "What can credit vintages tell us about non-performing loans?," Borradores de Economia 1154, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1154

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    References listed on IDEAS

    1. Wenjiang Fu, 2016. "Constrained Estimators and Consistency of a Regression Model on a Lexis Diagram," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 180-199, March.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Vicente Salas & Jesús Saurina, 2002. "Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 22(3), pages 203-224, December.
    5. Shin, Yongcheol, 1994. "A Residual-Based Test of the Null of Cointegration Against the Alternative of No Cointegration," Econometric Theory, Cambridge University Press, vol. 10(1), pages 91-115, March.
    6. Dimitrios, Anastasiou & Helen, Louri & Mike, Tsionas, 2016. "Determinants of non-performing loans: Evidence from Euro-area countries," Finance Research Letters, Elsevier, vol. 18(C), pages 116-119.
    7. Nir Klein, 2013. "Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance," IMF Working Papers 2013/072, International Monetary Fund.
    8. Vasiliki Makri & Athanasios Tsagkanos & Athanasios Bellas, 2014. "Determinants of Non-Performing Loans: The Case of Eurozone," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 61(2), pages 193-206, March.
    9. Breeden, Joseph L., 2016. "Incorporating lifecycle and environment in loan-level forecasts and stress tests," European Journal of Operational Research, Elsevier, vol. 255(2), pages 649-658.
    10. Joseph L Breeden & Lyn Thomas, 2016. "Solutions to specification errors in stress testing models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(6), pages 830-840, June.
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    12. Ghosh, Amit, 2015. "Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states," Journal of Financial Stability, Elsevier, vol. 20(C), pages 93-104.
    13. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    14. repec:uts:ppaper:2004:1 is not listed on IDEAS
    15. Daniel Roesch & Harald Scheule, 2004. "Forecasting retail portfolio credit risk," Published Paper Series 2004-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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    More about this item


    Cosechas de crédito; cartera en mora; regresiones penalizadas; estimadores intrínsecos; credit vintages; non-performing loans; elastic net regressions; intrinsic estimators.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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