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Two Stylized Facts and the Garch (1,1) Model

Author

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  • Teräsvirta, Timo

    () (Department of Economic Statistics)

Abstract

Many 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.

Suggested Citation

  • Teräsvirta, Timo, 1996. "Two Stylized Facts and the Garch (1,1) Model," SSE/EFI Working Paper Series in Economics and Finance 96, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0096
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    Citations

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    Cited by:

    1. Eklund, Bruno, 2005. "Estimating confidence regions over bounded domains," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 349-360, April.
    2. 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.
    3. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.

    More about this item

    Keywords

    Conditional heteroskedasticity; moment condition; IGARCH; t-distribution; high frequency economic data;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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