Vol. 15 Núm. 2 (2024): REVISTA AGLALA
Artículos Cientificos

Modelos autorregresivos vectoriales integrados con volatilidad estocástica multivariada aplicado a la economía de estados unidos en el periodo de 1948-2019

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  • Cristian Andrés Cruz Torres,
  • Marvin Levi Villafranca Rivera
Cristian Andrés Cruz Torres
niversidad Nacional Autónoma de Honduras
Marvin Levi Villafranca Rivera
Universidad Nacional Autónoma de Honduras

Publicado 2024-11-20

Palabras clave

  • Volatilidad Estocástica, VAR-MSV, Muestreador multi-move

Cómo citar

Cruz Torres, C. A., & Villafranca Rivera, M. L. (2024). Modelos autorregresivos vectoriales integrados con volatilidad estocástica multivariada aplicado a la economía de estados unidos en el periodo de 1948-2019. Aglala, 15(2), 116–142. Recuperado a partir de https://revistas.uninunez.edu.co/index.php/aglala/article/view/2523
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Resumen

Los modelos autorregresivos vectoriales (VAR) han demostrado ser eficientes para capturar las relaciones dinámicas de las series de tiempo multivariadas. Los modelos de volatilidad estocástica multivariada (MSV) muestran ser útiles para modelar la varianza cuando cambia en el tiempo. Por lo anterior, en este artículo se propone la integración de un modelo VAR con un modelo MSV (VAR-MSV). La elección del VAR-MSV más adecuado se lleva a cabo por medio del Criterio de Información de Desviación (DIC). Se hizo una aplicación a dos variables macroeconómicas clave para los Estados Unidos. Se agregó el índice del mercado de valores SP500 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 VAR-MSV captura las relaciones dinámicas, así como la varianza cambiando en el tiempo de manera eficaz.

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