The rugarch package is the premier open source software for univariate GARCH modelling. It is written in R using S4 methods and classes with a significant part of the code in C and C++ for speed. It contains a number of GARCH models beyond the vanilla version including IGARCH, EGARCH, GJR, APARCH, FGARCH, Component-GARCH, as well as a very large number of conditional distributions including (Skew)-Normal, (Skew)-GED, (Skew)-Student (Fernandez/Steel), (Skew)-Student (GH), Normal Inverse Gaussian (NIG), Generalized Hyperbolic (GH) and Johnson’s SU (JSU). The conditional mean equation includes ARFIMA and ARCH-in-mean, and is estimated in a joint step with the GARCH model. Both the conditional mean and variance parts allow for external regressors to be used. A comprehensive set of methods to work with these models are implemented, and include estimation, filtering, forecasting, simulation, inference tests and plots, with additional functionality in the form of the GARCH bootstrap, parameter uncertainty via the GARCH distribution function, misspecification tests (Hansen’s GMM and Hong&Li Portmanteau type test), predictive accuracy tests (Pesaran&Timmermann, Anatolyev&Gerko), and Value at Risk tests (VaR Exceedances and Expected Shortfall tests).

The package has been in active development for over 5 years, with new functionality added as and when the need arises, and time allows. Releases to CRAN usually occur once every one or two months.

An html version of the documentation is available on inside-r.

Some individuals have also provided posts and documentation on rugarch, including Pat Burns in his excellent and informative blog, Professor Eric Zivot in his courses at the University of Washington, and Jesper Hybel Pedersen who was kind enough to share documentation he wrote through a post in the R-SIG-FINANCE mailing list and agreed to also have it hosted here.