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...

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form and additional deterministic mean and trend terms are allowed for. Prior to the tests the deterministic parts and other nuisance parameters of the...

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the paper and further informal checks the model is capable of capturing both of the typical characteristics of the...