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Activity Number: 180
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #321502 View Presentation
Title: Spline Estimation of a Semiparametric GARCH Model
Author(s): Rong Liu* and Lijian Yang
Companies: University of Toledo and Soochow University
Keywords: Spline ; Garch ; Semiparametric ; Time series ; Financial

The semiparametric GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model 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.

Authors who are presenting talks have a * after their name.

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