@inproceedings{187addf3fe1549d6ac72c8cceaad72f7,
title = "The effect of innovation assumptions on asymmetric GARCH models for volatility forecasting",
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{\textquoteright}s t, Generalized Error, skew Student{\textquoteright}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 = "Asymmetry, Financial markets, GARCH models, Innovation processes",
author = "Diego Acu{\~n}a and H{\'e}ctor Allende-Cid and H{\'e}ctor Allende",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-25751-8_63",
language = "English",
isbn = "9783319257501",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "527--534",
editor = "Alvaro Pardo and Josef Kittler",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}