In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility. For such series, conventional GARCH models have often proved unsatisfactory because they tend to exaggerate volatility persistence and exhibit poor forecasting ability. Our main...

We develop likelihood-based tests for autocorrelation and predictability in a first order non-Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated...

The use of asymptotic critical values in stationarity tests against the alternative of a unit rot process is known to lead to overrejections in finite samples when the considered process is stationary but highly persistent. We claim that in recent parametric tests this is caused by estimation...