Forecasting with GARCH models under structural breaks: An approach based on combinations across estimation windows

This paper investigates some weighting schemes to average forecasts across different estimation windows to account for structural changes in the unconditional variance of a GARCH (1,1) model. Each combination is obtained by averaging forecasts generated by recursively increasing an initial estimation window of a fixed number of observations v. Three different choices of the combination weights are proposed. In the first scheme, the forecast combination is obtained by using equal weights to average the individual forecasts; the second weighting method assigns heavier weights to forecasts that use more recent information; the third is a trimmed version of the forecast combination with equal weights where a fixed fraction of the highest and lowest individual forecasts is discarded. Simulation results show that forecast combinations with high values of v are able to perform better than alternative schemes proposed in the literature. An application to real data confirms the simulation results.