Prediction in dynamic models with time-dependent conditional variances
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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.
Published Version (Please cite this version)10.1016/0304-4076(92)90066-Z
Publication InfoBaillie, Richard T; & Bollerslev, Tim (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.
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Juanita and Clifton Kreps Distinguished Professor of Economics, in Trinity College of Arts and Sciences
Professor Bollerslev conducts research in the areas of time-series econometrics, financial econometrics, and empirical asset pricing finance. He is particularly well known for his developments of econometric models and procedures for analyzing and forecasting financial market volatility. Much of Bollerslev’s recent research has focused on the analysis of newly available high-frequency intraday, or tick-by-tick, financial data and so-called realized volatility measures, macroeconomic news annou