The effect of innovation assumptions on asymmetric GARCH models for volatility forecasting

Diego Acuña, Héctor Allende-Cid, Héctor Allende

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The modelling and forecasting of volatility in Time Series has been receiving great attention from researchers over the past years. In this topic, GARCH models are one of the most popular models. In this work, the effects of choosing different distribution families for the innovation process on asymmetric GARCH models are investigated. In particular, we compare A-PARCH models for the IBM stock data with Normal, Student’s t, Generalized Error, skew Student’s t and Pearson type-IV distributions. The main findings indicate that distributions with skewness have better performance than non-skewed distributions and that the Pearson IV distribution arises as a great candidate for the innovation process on asymmetric models.

Idioma originalInglés
Título de la publicación alojadaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditoresAlvaro Pardo, Josef Kittler
EditorialSpringer Verlag
Páginas527-534
Número de páginas8
ISBN (versión impresa)9783319257501
DOI
EstadoPublicada - 2015
Evento20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay
Duración: 9 nov. 201512 nov. 2015

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen9423
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
País/TerritorioUruguay
CiudadMontevideo
Período9/11/1512/11/15

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