Conditional Heteroscedasticity and Garch Models
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چکیده
a for forecasting purposes arises from the fact that this conditional mean is allowed to be a random varible which depends on the available data, and evolves with time. The conditional variance, however, is r simply var [x e x ] = var [ε ] =σ , which remains constant regardless of the given data. Thus, the linea t t −1 t ε AR (1) model fails to adequately describe the conditional variance. In particular, note that the one-step . T forecast intervals will always have the same width, regardless of the value of the given observations his seems unrealistic.
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