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Generalized autoregressive conditional heteroskedasticity

  • Tim Bollerslev

The present paper proposes a generalization of the canonical AutoRegressive Conditional Heteroskedasticity (ARCH) model by extending the conditional variance equation toward past conditional variances. The stationarity conditions and autocorrelation structure of the Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model are derived. Using an empirical example of uncertainty of the inflation rate the paper demonstrates that the GARCH model provides a better fit and a more plausible learning mechanism than the ARCH model.

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Paper provided by Economics and Econometrics Research Institute (EERI), Brussels in its series EERI Research Paper Series with number EERI RP 1986/01.

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Date of creation: 01 Sep 1986
Date of revision:
Handle: RePEc:eei:rpaper:eeri_rp_1986_01
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