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Prediction in dynamic models with time-dependent conditional variances
Abstract
This paper considers forecasting the conditional mean and variance from a single-equation
dynamic model with autocorrelated disturbances following an ARMA process, and innovations
with time-dependent conditional heteroskedasticity as represented by a linear GARCH
process. Expressions for the minimum MSE predictor and the conditional MSE are presented.
We also derive the formula for all the theoretical moments of the prediction error
distribution from a general dynamic model with GARCH(1, 1) innovations. These results
are then used in the construction of ex ante prediction confidence intervals by means
of the Cornish-Fisher asymptotic expansion. An empirical example relating to the uncertainty
of the expected depreciation of foreign exchange rates illustrates the usefulness
of the results. © 1992.
Type
Journal articlePermalink
https://hdl.handle.net/10161/1913Published Version (Please cite this version)
10.1016/0304-4076(92)90066-ZPublication Info
Bollerslev Richard, T; & Baillie, T (1992). Prediction in dynamic models with time-dependent conditional variances. Journal of Econometrics, 52(1-2). pp. 91-113. 10.1016/0304-4076(92)90066-Z. Retrieved from https://hdl.handle.net/10161/1913.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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