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Augmented ARCH models for financial time series: stability conditions and empirical evidence

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  • Robert Kunst

Abstract

The class of conditionally heteroscedastic models known as 'augmented ARCH' encompasses most liear 'ARCH'-type models found in the literature and, in particular, two basic ARCH variants for autocorrelated series: Engle (1982) explains conditional variance by lagged errors, Weiss (1984) also by lagged observations. The framework permits an evaluation of whether the restrictions evolving from the Engle or the Weiss models are valid in practice. Time series of stock market indexes for some major stock exchanges yield empirical examples. In most cases, the statistical approximation to actual dynamic behaviour is improved substantially by considering augmented ARCH structures

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  • Robert Kunst, 1997. "Augmented ARCH models for financial time series: stability conditions and empirical evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 7(6), pages 575-586.
    • Handle: RePEc:taf:apfiec:v:7:y:1997:i:6:p:575-586
    DOI: 10.1080/758533849
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    References listed on IDEAS

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    1. Bera, Anil K & Higgins, Matthew L & Lee, Sangkyu, 1992. "Interaction between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 133-142, April.
    2. Kleibergen, F & Van Dijk, H K, 1993. "Non-stationarity in GARCH Models: A Bayesian Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 41-61, Suppl. De.
    3. Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
    6. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(3), pages 318-334, September.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Andreas Brunhart, 2014. "Stock Market's Reactions to Revelation of Tax Evasion: An Empirical Assessment," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(III), pages 161-190, September.
    2. Kunst, Robert M., 2003. "Testing for Relative Predictive Accuracy: A Critical Viewpoint," Economics Series 130, Institute for Advanced Studies.
    3. Brunhart, Andreas, 2011. "Evaluating the effect of "Zumwinkel-Affair" and financial crisis on stock prices in Liechtenstein: An unconventional augmented GARCH-approach," KOFL Working Papers 9, Konjunkturforschungsstelle Liechtenstein (KOFL), Vaduz.

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