variables with a time varying mean given by a GARCH type equation. After that, in 2000, Engle incorporated duration into a … volatility context (UHF-GARCH). Then, in 2001, Zhang, Russell and Tsay extended the original models (Exponencial and Weibull ACD …

This paper discusses and documents G@RCH 2.0, an Ox package dedicated to the estimation and forecasting of various … univariate ARCH-type models including the GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, FIEGARCH and FIAPARCH specifications of …

Though multivariate GARCH models are widely used in empirical research, their computational aspects still represent a … simultaneous equations model (SEM) with GARCH errors was considered by Engle and Kroner (1995). While there are many applications … GARCH errors to compute the corresponding ML estimators. We compare different gradient algorithms in a simulation framework …

We investigate several aspects of GARCH(p,q) models which are relevant for empirical applications. In particular, we … note that the inclusion of a dummy variable as regressor can lead to multimodality in the GARCH likelihood. This makes … on the GARCH parameter space. We present a small refinement to the Nelson-Cao (1992) conditions for a GARCH(2,q) model …

highly dependent on the magnitude of shocks themselves. Markov-Switching GARCH (MS-GARCH) models are a valuable tool for … dynamics, it is here suggested to use a modification of the component GARCH model proposed by Ding and Granger (1996) in which … lagged values of the conditional standard deviation. Differently from MS-GARCH models, likelihood based inference for the …

volatility series can be modelled as an ARFIMA process. The ARFIMA's forecasting performance is assessed in a simulation study …, and, although it outperforms representative GARCH models, it does so with greater complexity and data intensiveness that … may not be worthwhile relative to GARCH's simplicity and flexibility. …