G@RCH 2.0: An Ox Package for Estimating and Forecasting Various ARCH Models

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 the conditional variance and an AR(FI)MA specification of the conditional mean. These models can be estimated by approximate (quasi-) maximum likelihood under four assumptions: normal, Student-t, GED, or skewed Student errors. Explanatory variables can enter both the conditional-mean and the conditional-variance equations. One-step-ahead (density) forecasts of both the conditional mean and variance are available as well as some misspecification tests and several graphical techniques.