Comparison of nonnested asymmetric heteroskedastic models

被引：28

作者：

Chen, Cathy W. S. ^{[1
]}

Gerlach, Richard

So, Mike K. P.

机构：

[1] Feng Chia Univ, Dept Stat, Taichung, Taiwan

[2] Univ Sydney, Sydney, NSW 2006, Australia

[3] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2006年 / 51卷 / 04期

关键词：

asymmetric volatility model; GJR-GARCH model; double threshold GARCH models; leverage effect; Markov chain Monte Carlo method; reversible jump;

D O I：

10.1016/j.csda.2006.07.025

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The GJR-GARCH model is a popular choice among nonlinear models of the well-known asymmetric volatility phenomenon in financial market data. However, recent work employs double threshold nonlinear models to capture both mean and volatility asymmetry. A Bayesian model comparison procedure is proposed to compare the GJR-GARCH with various double threshold GARCH specifications, by designing a reversible jump Markov chain Monte Carlo algorithm. A simulation experiment illustrates good performance in estimation and model selection over reasonable sample sizes. In a study of seven markets strong evidence is found that the DTGARCH, with US market news as threshold variable, outperforms the GJR-GARCH and traditional self-exciting DTGARCH models. This result was consistent across six markets, excluding Canada. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：2164 / 2178

页数：15

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