Generalized autoregressive conditional heteroskedasticity
AbstractThe 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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Bibliographic InfoPaper provided by Economics and Econometrics Research Institute (EERI), Brussels in its series EERI Research Paper Series with number EERI RP 1986/01.
Date of creation: 01 Sep 1986
Date of revision:
GARCH model; time-varying variance.;
Other versions of this item:
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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