 Simultaneously Modelling Conditional Heteroskedasticity and Scale ChangeNo 02/12, CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE) 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. Keywords: Semiparametric GARCH; conditional heteroskedasticity; scale change; nonparametric regression with dependence; bandwidth selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:zbw:cofedp:0212 Access Statistics for this paper More papers in CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE) Contact information at EDIRC. |
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