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The Effect of Innovation Assumptions on Asymmetric GARCH Models for Volatility Forecasting

  • Diego AcuñaEmail author
  • Héctor Allende-Cid
  • Héctor Allende
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

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.

Keywords

Financial markets GARCH models Asymmetry Innovation processes 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Diego Acuña
    • 1
    Email author
  • Héctor Allende-Cid
    • 2
  • Héctor Allende
    • 3
  1. 1.Universidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Pontificia Universidad Católica de ValparaísoValparaísoChile
  3. 3.Universidad Adolfo IbáñezViña Del MarChile

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