IDEAS home Printed from https://ideas.repec.org/p/ise/isegwp/wp032020.html
   My bibliography  Save this paper

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 & 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.
    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. Alexandre, Michel & Antônio Silva Brito, Giovani & Cotrim Martins, Theo, 2017. "Default contagion among credit modalities: evidence from Brazilian data," MPRA Paper 76859, University Library of Munich, Germany.
    4. Ismail Tijjani Idris & Sabri Nayan, 2016. "The Moderating Role of Loan Monitoring on the Relationship between Macroeconomic Variables and Non-performing Loans in Association of Southeast Asian Nations Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 402-408.
    5. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    6. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    7. Adithi Ramesh & C. B Senthil Kumar, 2017. "Structure and Intensity Based Approach in Credit Risk Models: A Literature Review," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 609-612.
    8. 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.
    9. Ghulam, Yaseen & Derber, Julian, 2018. "Determinants of sovereign defaults," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 43-55.
    10. Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen, 2022. "Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1669-1690, December.
    11. Djeundje, Viani Biatat & Crook, Jonathan, 2018. "Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards," European Journal of Operational Research, Elsevier, vol. 271(2), pages 697-709.
    12. 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.
    13. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    14. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    20. 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.
    21. Karol Przanowski, 2011. "Banking retail consumer finance data generator - credit scoring data repository," Papers 1105.2968, arXiv.org.

    More about this item

    Keywords

    credit risk; EMV models; mortgage loans; default rates; vintages. JEL Classification: G20; G21;
    All these 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.