IDEAS home Printed from https://ideas.repec.org/p/ise/isegwp/wp032020.html

Modelling credit risk: evidence for EMV methodology on Portuguese mortgage data

Author

Listed:
  • Maria Rosa Borges
  • Raquel Machado

Abstract

Traditional credit risk models failed during the recent financial crisis and revealed weaknesses in forecasting and stress testing procedures. One of the main reasons for this failure was the fact that they did not include lifecycle and macroeconomic adverse selection effects. The Exogenous-Maturity-Vintage (EMV) models emerged in this context, in the credit risk literature. In this article, we assess the applicability of the EMV models to a dataset consisting of Portuguese mortgage data between 2007 and 2017, to study the determinants of default rates. We obtain and examine the exogenous, maturity and vintage curves from the dataset under analysis, plotting defaults rates through time, under each of the three component’s logic (default rates by calendar period, by age and by vintage). We show that these curves follow the expected behavior. Finally, we identify a set of explanatory variables suitable to be incorporated in an EMV model specification, for forecasting purposes, and discuss the rationality for their inclusion in the model.

Suggested Citation

  • Maria Rosa Borges & Raquel Machado, 2020. "Modelling credit risk: evidence for EMV methodology on Portuguese mortgage data," Working Papers Department of Economics 2020/03, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    • Handle: RePEc:ise:isegwp:wp032020
    as

    Download full text from publisher

    File URL: https://depeco.iseg.ulisboa.pt/wp/wp032020.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Breeden, Joseph L., 2007. "Modeling data with multiple time dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4761-4785, May.
    2. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    3. Petrus Strydom, 2017. "Macro economic cycle effect on mortgage and personal loan default rates," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(6), pages 1-1.
    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. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    2. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    3. 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.
    4. Michel Alexandre & Giovani A. S. Brito & Theo C. Martins, . "Default contagion among credit modalities: evidence from Brazilian data," Journal of Credit Risk, Journal of Credit Risk.
    5. Arno Botha & Tanja Verster, 2025. "Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis," Papers 2507.15441, arXiv.org, revised Dec 2025.
    6. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    7. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    8. Andrea Marletta, 2024. "How to control the effectiveness of a campaign of mailing list marketing: a proposal based on survival analysis," Annals of Operations Research, Springer, vol. 342(3), pages 1581-1604, November.
    9. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    10. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    11. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    12. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.
    13. Karol Przanowski, 2013. "Banking Retail Consumer Finance Data Generator – Credit Scoring Data Repository," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 9(1), pages 44-59, May.
    14. Karol Przanowski, 2011. "Banking retail consumer finance data generator - credit scoring data repository," Papers 1105.2968, arXiv.org.
    15. Norliza Che Yahya & Bushra Mohd Zaki & Nik Norain Izzati Nik Azid & Nur Fatihah Mohd Ali & Nor Amanie Najihah Hussain, 2023. "The Determinants of Household Debt in Malaysia," Information Management and Business Review, AMH International, vol. 15(3), pages 183-194.
    16. J L Breeden & D Ingram, 2010. "Monte Carlo scenario generation for retail loan portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 399-410, March.
    17. Breeden, Joseph L. & Parker, Robert & Steinebach, Carsten, 2012. "A through-the-cycle model for retail lending economic capital," International Journal of Forecasting, Elsevier, vol. 28(1), pages 133-138.
    18. Barbara Domotor & Ferenc Ill'es, 2025. "Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach," Papers 2508.15651, arXiv.org.
    19. Bell, Simon, 2012. "DPSIR=A Problem Structuring Method? An exploration from the “Imagine” approach," European Journal of Operational Research, Elsevier, vol. 222(2), pages 350-360.
    20. Andreea Costea, 2017. "A Quantitative Approach to Credit Risk Management in the Underwriting Process for the Retail Portfolio," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 20(63), pages 157-186, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - 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:ise:isegwp:wp032020. 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: Vitor Escaria (email available below). General contact details of provider: https://aquila.iseg.ulisboa.pt/aquila/departamentos/EC .

    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.