Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/85216
Authors: 
Feng, Yuanhua
Year of Publication: 
2002
Series/Report no.: 
CoFE Discussion Paper 02/12
Abstract: 
This paper proposes a semiparametric approach by introducing a smooth scale function into the standard GARCH model so that conditional heteroskedasticity and scale change in a financial time series can be modelled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic proper- ties of the kernel estimator are investigated in detail. An iterative plug-in algorithm is developed for selecting the bandwidth. Practical performance of the proposal is illustrated by simulation. The proposal is applied to the daily S&P 500 and DAX 100 returns. It is shown that there are simultaneously significant conditional heteroskedasticity and scale change in these series.
Subjects: 
Semiparametric GARCH
conditional heteroskedasticity
scale change
nonparametric regression with dependence
bandwidth selection
JEL: 
C22
C14
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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