The rmgarch provides a selection of multivariate GARCH models with methods for fitting, filtering, forecasting and simulation with additional support functions for working with the returned objects. At present, the Generalized Orthogonal GARCH using Independent Components Analysis (ICA) and Dynamic Conditional Correlation (with multivariate Normal, Laplace and Student distributions) models are fully implemented, with methods for spec, fit, filter, forecast, simulation, and rolling estimation and forecasting, as well as specialized functions to calculate and work with the weighted portfolio conditional density. The Copula-GARCH model is also implemented with the multivariate Normal and Student distributions, with dynamic (DCC) and static estimation of the correlation.
Whenever using this package, please cite as
@Manual{Ghalanos_2014,
author = {Alexios Ghalanos},
title = {{rmgarch}: Multivariate GARCH models.},
year = {2019},
note = {R package version 1.3-6.}}The releases of this package is licensed under GPL version 3.
The main package functionality, currently supports the GO-GARCH with ICA
method, and is available through the gogarchspec,
gogarchfit, gogarchfilter, gogarchforecast,
gogarchsim and gogarchroll functions. The DCC
with multivariate Normal, Laplace and Student distributions is also supported
with the main functionality in dccspec, dccfit,
dccfilter, dccforecast, dccsim and
dccroll. The Normal and Student Copula-GARCH, with dynamic or
static correlation, is implemented with the main functionality in
cgarchspec, cgarchfit, cgarchfilter,
and cgarchsim. Usual extractor and support methods for the
multivariate GARCH models are documented in the class of the returned objects..
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