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

Diego Acuña, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlvaro Pardo, Josef Kittler
PublisherSpringer Verlag
Pages527-534
Number of pages8
ISBN (Print)9783319257501
DOIs
StatePublished - 2015
Externally publishedYes
Event20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9423
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
CountryUruguay
CityMontevideo
Period9/11/1512/11/15

Keywords

  • Asymmetry
  • Financial markets
  • GARCH models
  • Innovation processes

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