Simultaneously Modelling Conditional Heteroskedasticity and Scale Change

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CoFE Discussion Paper ; No. 02/12

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Semiparametric and Nonparametric Methods: General
• Subject: Semiparametric GARCH ; conditional heteroskedasticity ; scale change ; nonparametric regression with dependence ; bandwidth selection ; ARCH-Modell ; Nichtparametrisches Verfahren ; Schätztheorie ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Feng, Yuanhua
• Event: Veröffentlichung
• (who): University of Konstanz, Center of Finance and Econometrics (CoFE)
• (where): Konstanz
• (when): 2002

Further information

• Handle: http://hdl.handle.net/10419/85216
• URN: urn:nbn:de:bsz:352-opus-8289
• Last update: 10.03.2025, 11:43 AM CET