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- W2103320194 abstract "This paper aims at explaining the poor forecasting performance of the GARCH(1,1) model reported in many empirical studies. A number of previous applied studies compare the conditional volatility forecasts of the GARCH(1,1) model to those of a model that assumes constant conditional variance (homoscedasticity) and conclude that the GARCH(1,1) model does not outperform the homoscedastic one. In these studies, the comparison of the relative forecasting accuracy of the two models is usually based on a selected formal statistical test that compares the Mean Squared Errors (MSEs) of the volatility forecasts of the two models. Some recent theoretical studies argue that the poor forecasting performance of the GARCH(1,1) model found in empirical studies is deceptive, since the utilization of the squared shocks as a proxy for the unobserved true conditional volatility increases the MSE of the volatility forecasts (MSE-inflation), rendering the MSE criterion invalid for proper evaluation of the forecasting accuracy of GARCH(1,1). However, the analytical results presented in this paper show that the utilization of the squared shocks as a proxy for the unobserved true conditional volatility inflates equally the MSEs of the forecasts of the GARCH(1,1) and the homoscedastic models and thus the MSE criterion remains capable to evaluate properly the relative forecasting performance of the two models. We also provide useful suggestions for proper statistical evaluation of the volatility forecasts of GARCH(1,1), together with an empirical illustration based on five bilateral exchange rates for the US dollar." @default.
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- W2103320194 date "2006-01-01" @default.
- W2103320194 modified "2023-09-27" @default.
- W2103320194 title "Forecasting Volatility with a GARCH(1,1) Model: Some New Analytical and Monte Carlo Results" @default.
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