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Un Modelo de alerta temprana para el sistema financiero colombiano

Author

Listed:
  • José Eduardo Gómez-González
  • Inés Paola Orozco Hinojosa

Abstract

En este trabajo se presenta un modelo estadístico de alerta temprana que utiliza modelos de duraciónpara evaluar el estado corriente y pronosticar el estado futuro de la salud financiera de los bancos enColombia. En el artículo se discuten las ventajas que tiene utilizar modelos de duración como modelosestadísticos de alerta temprana frente a los más comúnmente utilizados modelos de respuesta binaria.Se argumenta que el modelo aquí presentado, que estudia la probabilidad de deterioro de los créditosa partir la salud financiera de las contrapartes de los bancos, puede ser un buen complemento a un modelo de alerta temprana que estudie directamente la probabilidad de quiebra de las entidades financieras. La capacidad de pronóstico dentro de muestra del modelo es buena, y podría pensarse que la capacidad de pronóstico fuera de muestra también es buena, ya que la muestra de créditos comerciales utilizada en las estimaciones es bastante representativa.

Suggested Citation

  • José Eduardo Gómez-González & Inés Paola Orozco Hinojosa, 2010. "Un Modelo de alerta temprana para el sistema financiero colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 28(62), pages 124-147, June.
  • Handle: RePEc:col:000107:009428
    DOI: 10.32468/Espe.6203
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    1. Mr. Robert Billings & Ms. Brenda Gonzalez-Hermosillo & Ceyla Pazarbasioglu, 1996. "Banking System Fragility: Likelihood Versus Timing of Failure: An Application to the Mexican Financial Crisis," IMF Working Papers 1996/142, International Monetary Fund.
    2. Carree, Martin A., 2003. "A hazard rate analysis of Russian commercial banks in the period 1994-1997," Economic Systems, Elsevier, vol. 27(3), pages 255-269, September.
    3. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    4. Gary Whalen, 1991. "A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool," Economic Review, Federal Reserve Bank of Cleveland, vol. 27(Q I), pages 21-31.
    5. Philip Bunn & Victoria Redwood, 2003. "Company accounts based modelling of business failures and the implications for financial stability," Bank of England working papers 210, Bank of England.
    6. José E. Gómez-Gonzalez & Nicholas M. Kiefer, 2009. "Bank Failure: Evidence From The Colombian Financial Crisis," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 3(2), pages 15-31.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. Daley, J. & Matthews, K. & Whitfield, K., 2008. "Too-big-to-fail: Bank failure and banking policy in Jamaica," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 290-303, July.
    9. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    10. Geroski,Paul A. & Gregg,Paul, 1997. "Coping with Recession," Cambridge Books, Cambridge University Press, number 9780521622769, September.
    11. Henrik Andersen, 2008. "Failure prediction of Norwegian banks: A Logit approach," Working Paper 2008/02, Norges Bank.
    12. Arena, Marco, 2008. "Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 299-310, February.
    13. Olivier BROSSARD (LEREPS-GRES ) & Frédéric DUCROZET (PSE - Crédit Agricole) & Adrian ROCHE (EconomiX - Crédit Agricole), 2007. "An Early Warning Model for EU banks with Detection of the Adverse Selection Effect," Cahiers du GRES (2002-2009) 2007-08, Groupement de Recherches Economiques et Sociales.
    14. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    15. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    16. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    17. E. Nur Ozkan-Gunay & Mehmed Ozkan, 2007. "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, Emerald Group Publishing, vol. 8(5), pages 465-480, November.
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    More about this item

    Keywords

    modelos estadísticos de alerta temprana; modelos de duración; intensidades de transición.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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