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Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP

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  • Oscar Andrés Espinosa Acuna
  • Paola Andrea Vaca González

Abstract

Resumen En este artículo se ajustan dos modelos de Heterocedasticidad Condicional Generalizados (GARCH) para el índice financiero COLCAP. El primero, desde una perspectiva clásica (o frecuentista) estimando los parámetros mediante Máxima Verosimilitud y el segundo, a partir de un enfoque bayesiano haciendo uso del algoritmo de Metropolis—Hastings. Para ambos casos se asumen las innovaciones con distribución t—Student. Mediante distintos criterios de información se evalúa el ajuste del modelo bajo las aproximaciones bayesiana y clásica.

Suggested Citation

  • Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17172, Universidad del Norte.
    • Handle: RePEc:col:000382:017172
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