This paper offers a new method for estimation and forecasting of the linear and nonlinear time series when the stationarity assumption is violated. Our general local parametric approach particularly applies to general varying-coefficient parametric models, such as AR or GARCH, whose coefficients...

The paper considers the Markov-Switching GARCH(1,1)-model with time-varying transition probabilities. It derives su±cient conditions for the square of the process to display long memory and provides some additional intuition for the empirical observation that estimated GARCH-parameters often...