Modelo VAR integrado con volatilidad estocástica multivariada y errores de cola pesada

Cruz Torres, Cristian Andrés; Villafranca Rivera, Marvin Levi

doi:10.15517/hfb1sv15

Autores/as

Cristian Andrés Cruz Torres Universidad Nacional Autónoma de Honduras, Departamento de Estadística Matemática, Tegucigalpa, Honduras Autor/a
Marvin Levi Villafranca Rivera Universidad Nacional Autónoma de Honduras, Departamento de Estadística Matemática, Tegucigalpa, Honduras Autor/a

DOI:

https://doi.org/10.15517/hfb1sv15

Palabras clave:

Volatilidad estocástica multivariada, Asimetría multivariada, Curtosis multivariada, Error de colas pesadas

Resumen

Los modelos autorregresivos vectoriales (VAR) son usados para capturar las relaciones dinámicas de series de tiempo multivariadas. Por otro lado, los modelos de volatilidad estocástica multivariada (MSV) permiten modelar la varianza cuando cambia en el tiempo. La distribución t de Student es usada para modelar valores en las series de tiempo que a menudo son de magnitud extrema. Por lo anterior, en este artículo se propone la integración de un modelo VAR, un modelo MSV y una distribución t de Student (VAR-MSV-t). La elección del orden VAR-MSV-t más adecuado se lleva a cabo por medio del Criterio de Información de Desviación (DIC). Se presentan fórmulas para estimar la asimetría de Mardia y la curtosis de Koziol del modelo. Se hizo una aplicación a tres variables macroeconómicas clave para los Estados Unidos. Agregamos el índice del mercado de valores S&P 500 y se interpretaron los resultados. Para estimar los parámetros se usan métodos de Monte Carlo vía Cadenas de Markov (MCMC). Los resultados indican que el modelo captura las relaciones dinámicas, así como la varianza cambiando en el tiempo y los valores de magnitud extrema de manera eficaz.

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