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

被引：0

作者：

Acuna, Diego ^{[1
]}

Allende-Cid, Hector ^{[2
]}

Allende, Hector ^{[3
]}

机构：

[1] Univ Tecn Federico Santa Maria, Valparaiso, Chile

[2] Pontificia Univ Catolica Valparaiso, Valparaiso, Chile

[3] Univ Adolfo Ibnez, Vina Del Mar, Chile

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2015 | 2015年 / 9423卷

关键词：

Financial markets; GARCH models; Asymmetry; Innovation processes; CONDITIONAL HETEROSKEDASTICITY;

D O I：

10.1007/978-3-319-25751-8_63

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：527 / 534

页数：8

共 18 条

[1]

Alberg D., 2008, APPL FINANCIAL EC, V18, P1201, DOI [10.1080/09603100701604225, DOI 10.1080/09603100701604225]

[2] GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY [J].