IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1154.html
   My bibliography  Save this paper

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

Author

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

Abstract

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
    DOI: https://doi.org/10.32468/be.1154
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1154
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.32468/be.1154?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Nir Klein, 2013. "Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance," IMF Working Papers 2013/072, International Monetary Fund.
    4. 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.
    5. 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).
    6. 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.
    7. Daniel Rösch & Harald Scheule, 2004. "Forecasting Retail Portfolio Credit Risk," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 5(2), pages 16-32, February.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. repec:uts:ppaper:2004:1 is not listed on IDEAS
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gamba-Santamaria, Santiago & Melo-Velandia, Luis Fernando & Orozco-Vanegas, Camilo, 2024. "Decomposition of non-performing loans dynamics into a debt-servicing capacity and a risk taking indicators," The Quarterly Review of Economics and Finance, Elsevier, vol. 96(C).
    2. Sascha Tobias Wengerek & Benjamin Hippert & André Uhde, 2019. "Risk allocation through securitization - Evidence from non-performing loans," Working Papers Dissertations 58, Paderborn University, Faculty of Business Administration and Economics.
    3. Laxmi Koju & Ram Koju & Shouyang Wang, 2018. "Does Banking Management Affect Credit Risk? Evidence from the Indian Banking System," IJFS, MDPI, vol. 6(3), pages 1-11, July.
    4. Ardit Gjeçi & Matej Marinč & Vasja Rant, 2023. "Non-performing loans and bank lending behaviour," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-26, March.
    5. Cucinelli, Doriana & Battista, Maria Luisa Di & Marchese, Malvina & Nieri, Laura, 2018. "Credit risk in European banks: The bright side of the internal ratings based approach," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 213-229.
    6. Teodor Hada & Nicoleta Bărbuță-Mișu & Iulia Cristina Iuga & Dorin Wainberg, 2020. "Macroeconomic Determinants of Nonperforming Loans of Romanian Banks," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    7. Florian Manz, 2019. "Determinants of non-performing loans: What do we know? A systematic review and avenues for future research," Management Review Quarterly, Springer, vol. 69(4), pages 351-389, November.
    8. Evangelos Charalambakis & Yiannis Dendramis & Elias Tzavalis, 2017. "On the determinants of NPLS: lessons from Greece," Working Papers 220, Bank of Greece.
    9. Mehmet Levent Erdas & Zeynep Ezanoglu, 2022. "How Do Bank-Specific Factors Impact Non-Performing Loans: Evidence from G20 Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(2), pages 97-122.
    10. Maria Karadima & Helen Louri, 2021. "Determinants of Non-Performing Loans in Greece: the intricate role of fiscal expansion," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 160, Hellenic Observatory, LSE.
    11. Vincenzo D'Apice & Franco Fiordelisi & Giovanni W. Puopolo, 2020. "Judicial Efficiency and Lending Quality," CSEF Working Papers 588, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    12. Gulati, Rachita & Goswami, Anju & Kumar, Sunil, 2019. "What drives credit risk in the Indian banking industry? An empirical investigation," Economic Systems, Elsevier, vol. 43(1), pages 42-62.
    13. Ionut – Daniel Pop, 2019. "Systemic Sustainability of European Banking Activity: A Multi-Perspective Approach," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 9(3), pages 49-58, July.
    14. Saom Shawleen Anita & Nishat Tasnova & Nousheen Nawar, 2022. "Are non-performing loans sensitive to macroeconomic determinants? an empirical evidence from banking sector of SAARC countries," Future Business Journal, Springer, vol. 8(1), pages 1-16, December.
    15. Pop, Ionuț Daniel & Cepoi, Cosmin Octavian & Anghel, Dan Gabriel, 2018. "Liquidity-threshold effect in non-performing loans," Finance Research Letters, Elsevier, vol. 27(C), pages 124-128.
    16. Changjun Zheng & Probir Kumar Bhowmik & Niluthpaul Sarker, 2019. "Industry-Specific and Macroeconomic Determinants of Non-Performing Loans: A Comparative Analysis of ARDL and VECM," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
    17. Koju Laxmi & Abbas Ghulam & Wang Shouyang, 2018. "Do Macroeconomic Determinants of Non-Performing Loans Vary with the Income Levels of Countries?," Journal of Systems Science and Information, De Gruyter, vol. 6(6), pages 512-531, December.
    18. Imad Jabbouri & Maryem Naili, 2020. "Determinants of Nonperforming Loans in Emerging Markets: Evidence from the MENA Region," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-33, February.
    19. Nikola Fabris & Nina Vujanović, 2022. "What Drives Credit Risk? Empirical Evidence from Southeast Europe," wiiw Working Papers 214, The Vienna Institute for International Economic Studies, wiiw.
    20. Ciukaj Radosław & Kil Krzysztof, 2020. "Determinants of the non-performing loan ratio in the European Union banking sectors with a high level of impaired loans," Economics and Business Review, Sciendo, vol. 6(1), pages 22-45, March.

    More about this item

    Keywords

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:borrec:1154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.