¿Cómo Implementar un Modelo de Volatilidad Usando Lenguaje R?
DOI:
https://doi.org/10.21501/21454086.832Keywords:
Volatilidad, Modelo, Implementación, GARCH, Lenguaje RAbstract
El modelado y pronóstico de series de tiempo financieras es una actividad de interés económico para los agentes del mercado. Las series de tiempo provenientes de ésta área a menudo presentan relaciones dinámicas complejas entre sus variables las cuales pueden ser capturadas mediante modelos de volatilidad. Estos pueden ser implementados en la mayoría de entornos de programación existentes. Sin embargo, la implementación de los modelos es compleja por no tener pautas para diseñar e implementar su código. Dado que R es un entorno de programación gratuito y estable, en este trabajo se proponen algunas pautas para diseñar e implementar el código de un modelo de volatilidad en R; como caso de ejemplo, se propone la creación del paquete Volatility que incluye el modelo de volatilidad GARCH y se mostrará su aplicabilidad al modelar la volatilidad de una serie de tiempo real.Downloads
References
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heterocedasticity. Journal of Econometrics , 31 (3 (April)), 307-327.
Casas Monsegny, M., & Cepeda, E. (2008). Modelos ARCH, GARCH y EGARCH: aplicaciones a series financieras. Revista Cuadernos de Economía , 27 (48), 287 - 319.
Engle, R. F. (1982). Autoregressive Conditional Heterocedasticity whit Estimates of the Variance of United Kingdom Inflation. Econometrica , 50 (4), 987-1008.
Development Core Team. (2009). Writing R Extensions. ISBN 3-900051-11-9: Version 2.10.0 (2009-10-26).
Venables, W. N., & Smith, D. M. (2009). An Introduction to R. ISBN 3-900051-12-7 : Version 2.10.0 (2009-10-26).
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