Conditional Variance Models
GARCH, exponential GARCH (EGARCH), and GJR models
条件差异模型试图解决单变量时间序列模型中的波动性聚类,以提高参数估计和预测精度。为了模拟波动率,计量经济器Toolbox™支持标准的广义自动回归有条件的异质机(Arc金宝apph/Garch)模型,指数GARCH(EGARCH)模型以及Glosten,Jagannathan和Runkle和Runkle(GJR)模型。
To convert from the previous conditional variance model analysis syntaxes, seeConverting from GARCH Functions to Model Objects.
Conditional Variance Model Basics
- Garch模型
Generalized, autoregressive, conditional heteroscedasticity models for volatility clustering - EGARCH Model
Exponential, generalized, autoregressive, conditional heteroscedasticity models for volatility clustering - GJR Model
Glosten-Jagannathan-Runkle GARCH model for volatility clustering