Modelling Different Volatility Components in High-Frequency Financial Returns
AbstractThis paper considers simultaneous modelling of seasonality, slowly changing un- conditional variance and conditional heteroskedasticity in high-frequency financial returns. A new approach, called a seasonal SEMIGARCH model, is proposed to perform this by introducing multiplicative seasonal and trend components into the GARCH model. A data-driven semiparametric algorithm is developed for estimating the model. Asymptotic properties of the proposed estimators are investigated brie y. An approximate significance test of seasonality and the use of Monte Carlo confidence bounds for the trend are proposed. Practical performance of the proposal is investigated in detail using some German stock price returns. The approach proposed here provides a useful semiparametric extension of the GARCH model.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-18.
Length: 26 pages
Date of creation: Nov 2002
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-26 (All new papers)
- NEP-ECM-2006-08-26 (Econometrics)
- NEP-ETS-2006-08-26 (Econometric Time Series)
- NEP-FMK-2006-08-26 (Financial Markets)
- NEP-MST-2006-08-26 (Market Microstructure)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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