Ajuste de modelos GARCH Clásico y Bayesiano con innovaciones t-Student para el índice COLCAP
Palavras-chave:
Modelos de Heterocedasticidad Condicional, Algoritmo de Metropolis—Hastings, Índice COLCAPResumo
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. A partir de diferentes criterios de información, se observa una mejor representación desde la aproximación Bayesiana, sin llegar a ser tan contundente sobre el análisis Clásico.
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