Two Stylized Facts and the Garch (1,1) Model
AbstractMany high frequency economic or financial time series display two empirical characteristics: high kurtosis and positive autocorrelation in the centred and squared observations. The first- order autocorrelation is typically low, and the autocorrelation function decays slowly. These series are often modelled with a GARCH (1,1) model. In this paper it is shown why such a model with normal errors cannot adequately characterize these stylized facts. The same seems true for the IGARCH (1,1)model. It is also shown why one can improve the situation by replacing the normal error distribution by a leptokurtic one, although this may not provide a complete remedy.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 96.
Length: 27 pages
Date of creation: Jan 1996
Date of revision:
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Conditional heteroskedasticity; moment condition; IGARCH; t-distribution; high frequency economic data;
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- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
- Eklund, Bruno, 2005.
"Estimating confidence regions over bounded domains,"
Computational Statistics & Data Analysis,
Elsevier, vol. 49(2), pages 349-360, April.
- Eklund, Bruno, 2003. "Estimating confidence regions over bounded domains," Working Paper Series in Economics and Finance 548, Stockholm School of Economics.
- Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
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