Forecasting volatility with switching persistence GARCH models

In this paper we examine the forecasting performance of five nonlinear GARCH(1,1) models. Four of these have recently been proposed in literature, while the fifth model is a new one. All five models allow for switching persistence of shocks, depending on the value and/or sign of recent returns. We consider the models for weekly data on 5 major stock markets. Our results indicate that all models improve upon the linear GARCH(1,1) model and that our new model sometimes yields favorable forecasting results.