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Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales Author info | Abstract | Publisher info | Download info | Related research | Statistics Christian A. Johnson () (Universidad Adolfo Ibañez)
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This paper reviews alternative methodologies and models to design sys-tems 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.
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Article provided by Ilades-Georgetown University, Economics Department in its journal Revista de Analisis Economico .
Volume (Year): 20 (2005)
Issue (Month): 1 (June)
Pages: 95-121
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Handle: RePEc:ila:anaeco:v:20:y:2005:i:1:p:95-121Contact details of provider: Postal: Erasmo Escala 1835, 6500620 Santiago Phone: (562) 692-0265 Fax: (562) 692-0303 Email: Web page: http://www.economia.uahurtado.cl/ More information through EDIRC
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Keywords: E44 G21 C23 C25 C45 Other versions of this item:
Find related papers by 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; Mortgages C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
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