Prediction in dynamic models with time-dependent conditional variances

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Baillie, RT; Bollerslev, T

Published Date

  • January 1, 1992

Published In

Volume / Issue

  • 52 / 1-2

Start / End Page

  • 91 - 113

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/0304-4076(92)90066-Z

Citation Source

  • Scopus