IDEAS home Printed from https://ideas.repec.org/a/nax/conyad/v62y2017i2p11-12.html
   My bibliography  Save this article

Administración del riesgo crediticio al menudeo en México: una mejora econométrica en la selección de variables y cambios en sus características

Author

Listed:
  • José Carlos Trejo-García

    (Instituto Politécnico Nacional, México)

  • Miguel Ángel Martínez-García

    (Instituto Politécnico Nacional, México)

  • Francisco Venegas-Martínez

    (Instituto Politécnico Nacional, México)

Abstract

La predicción temprana de malos deudores para créditos revolventes en México es un asunto de relevancia actual. El modelo econométrico propuesto de comportamiento crediticio considera los cambios en las características de los acreditados consolidados y proporciona mejores resultados que los obtenidos con la metodología utilizada por la CNBV en materia de provisiones. Los resultados obtenidos muestran que la posibilidad de reemplazar el modelo vigente, minimizando la pérdida esperada y aumentando el ROA por entidad financiera a nivel nacional en un 2.20%, cumple con los criterios metodológicos y pruebas estadísticas de acuerdo a la Circular Única de Bancos y lineamientos de Basilea II en materia de riesgo crediticio.

Suggested Citation

  • José Carlos Trejo-García & Miguel Ángel Martínez-García & Francisco Venegas-Martínez, 2017. "Administración del riesgo crediticio al menudeo en México: una mejora econométrica en la selección de variables y cambios en sus características," Contaduría y Administración, Accounting and Management, vol. 62(2), pages 11-12, Abril-Jun.
  • Handle: RePEc:nax:conyad:v:62:y:2017:i:2:p:11-12
    as

    Download full text from publisher

    File URL: http://www.cya.unam.mx/index.php/cya/article/view/835
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978.
    2. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, March.
    3. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2, 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. Thomas Wainwright, 2011. "Elite Knowledges: Framing Risk and the Geographies of Credit," Environment and Planning A, , vol. 43(3), pages 650-665, March.
    2. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    3. Kiviat, Barbara, 2019. "Credit Scoring in the United States," economic sociology. perspectives and conversations, Max Planck Institute for the Study of Societies, vol. 21(1), pages 33-42.
    4. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    5. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 2020. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 22(5), pages 1009-1019, October.
    6. Victor Olkhov, 2020. "Price, Volatility and the Second-Order Economic Theory," Papers 2009.14278, arXiv.org, revised Apr 2021.
    7. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    8. Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021. "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers 2104.06735, arXiv.org.
    9. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    10. Olkhov, Victor, 2021. "Theoretical Economics and the Second-Order Economic Theory. What is it?," MPRA Paper 110893, University Library of Munich, Germany.
    11. Eduard Sariev & Guido Germano, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 311-328, February.
    12. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    13. Igor Livshits & James C. Mac Gee & Michèle Tertilt, 2016. "The Democratization of Credit and the Rise in Consumer Bankruptcies," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1673-1710.
    14. Büşra Alma Çallı & Erman Coşkun, 2021. "A Longitudinal Systematic Review of Credit Risk Assessment and Credit Default Predictors," SAGE Open, , vol. 11(4), pages 21582440211, November.
    15. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    16. Hossein Rezayi Dolatabadi & Avaz Yari & Fatemeh Faghani & Ali Akbar Abedi Sharabiany & Mohammad Hossein Forghani & Mohammad Kazem Emadzadeh, 2013. "Prioritizing of Credit Ranking Criterions of Isfahan State banks' Costumers by Using AHP Fuzzy Method," 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. 3(1), pages 303-313, January.
    17. Olkhov, Victor, 2022. "Why Economic Theories and Policies Fail? Unnoticed Variables and Overlooked Economics," MPRA Paper 114187, University Library of Munich, Germany.
    18. Olkhov, Victor, 2020. "Business Cycles as Collective Risk Fluctuations," MPRA Paper 104598, University Library of Munich, Germany.
    19. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
    20. George Bouchagiar, 2019. "The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias," Review of European Studies, Canadian Center of Science and Education, vol. 11(1), pages 1-27, December.

    More about this item

    Keywords

    Banca; Crédito; Modelos econométricos; Metodología de estimación de datos; Técnicas de optimización;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    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:nax:conyad:v:62:y:2017:i:2:p:11-12. 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: Alberto García-Narvaez (Technical Editor) (email available below). General contact details of provider: https://edirc.repec.org/data/fcunamx.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.