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Prediction in dynamic models with time-dependent conditional variances

dc.contributor.author Bollerslev Richard, T
dc.contributor.author Baillie, T
dc.date.accessioned 2010-03-09T15:29:35Z
dc.date.issued 1992-01-01
dc.identifier.issn 0304-4076
dc.identifier.uri https://hdl.handle.net/10161/1913
dc.description.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.
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Journal of Econometrics
dc.relation.isversionof 10.1016/0304-4076(92)90066-Z
dc.title Prediction in dynamic models with time-dependent conditional variances
dc.type Journal article
pubs.begin-page 91
pubs.end-page 113
pubs.issue 1-2
pubs.organisational-group Duke
pubs.organisational-group Economics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 52


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