SPLINE ESTIMATION OF A SEMIPARAMETRIC GARCH MODEL

被引：7
|
作者：
Liu, Rong [1 ,2 ]
Yang, Lijian [1 ]
机构：
[1] Soochow Univ, Suzhou, Peoples R China
[2] Univ Toledo, 2801 W Bancroft St, Toledo, OH 43606 USA
来源：
ECONOMETRIC THEORY | 2016年 / 32卷 / 04期
基金：
美国国家科学基金会; 高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词：
VOLATILITY; AGGREGATION; REGRESSION;
D O I：
10.1017/S0266466615000055
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
The semiparametric GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model of Yang (2006, Journal of Econometrics 130, 365-384) has combined the flexibility of a nonparametric link function with the dependence on infinitely many past observations of the classic GARCH model. We propose a cubic spline procedure to estimate the unknown quantities in the semiparametric GARCH model that is intuitively appealing due to its simplicity. The theoretical properties of the procedure are the same as the kernel procedure, while simulated and real data examples show that the numerical performance is either better than or comparable to the kernel method. The new method is computationally much more efficient than the kernel method and very useful for analyzing large financial time series data.
引用
收藏
页码：1023 / 1054
页数：32
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