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.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.

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application/pdf

dc.identifier.issn

0304-4076

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https://hdl.handle.net/10161/1913

dc.language.iso

en_US

dc.relation.ispartof

Journal of Econometrics

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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

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Economics

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Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

52

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