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Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales

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  • Christian A. Johnson

    (Universidad Adolfo Ibañez)

Abstract

This paper reviews alternative methodologies and models to design systems to help in the early detection of banking distress (EWS). The pro-posed methodologies are aimed to the early identification of financial distress for countries without an important recent history of banking failure. This paper presents traditional models often used to predict currency crisis, and more advanced approaches, such as non linear neural networks models.

Suggested Citation

  • Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 95-121, June.
    • Handle: RePEc:ila:anaeco:v:20:y:2005:i:1:p:95-121
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    More about this item

    Keywords

    E44; G21; C23; C25; C45;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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