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Redes neuronales para predecir el tipo de cambio diario

  • Barrera, Carlos R.


    (Banco Central de Reserva del Perú)

Un problema recurrente es que los modelos estructurales de determinación del tipo de cambio no logran predecirlo con mayor precisión que un camino aleatorio. El objetivo de la presente investigación es verificar si es posible obtener proyecciones relativamente precisas generadas por un grupo de modelos econométricos para el tipo de cambio diario sobre la base de la muestra disponible enero 2004 - setiembre 2008. Los modelos a compararse en términos predictivos son: (a) camino aleatorio en el nivel del tipo de cambio; (b) auto-regresión con p rezagos en la variación del tipo de cambio; (c) perceptrones con p rezagos en la variación del tipo de cambio y (d) auto-regresión fraccional con p rezagos en el nivel del tipo de cambio. Los resultados obtenidos confirman que los perceptrones poseen la capacidad para anticipar el patrón de los movimientos diarios en el tipo de cambio, especialmente cuando se utiliza el spread entre el tipo de cambio venta y compra como porcentaje del tipo de cambio promedio de estas dos cotizaciones, la depreciación diaria del yen contra el dólar americano y el diferencial de tasas domésticas de interés interbancarias en ambas monedas.

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Paper provided by Banco Central de Reserva del Perú in its series Working Papers with number 2010-001.

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Date of creation: Jan 2010
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Handle: RePEc:rbp:wpaper:2010-001
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