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
|Depends: R (>= 3.0.2), methods, rugarch|
|Imports: Rsolnp, MASS, parallel, Matrix, zoo, xts, Bessel, ff, fftw, shape, Kendall, spd, Rcpp|
The main package functionality, currently supports the GO-GARCH with ICA
method, and is available through the
gogarchroll functions. The DCC
with multivariate Normal, Laplace and Student distributions is also supported
with the main functionality in
dccroll. The Normal and Student Copula-GARCH, with dynamic or
static correlation, is implemented with the main functionality in
cgarchsim. Usual extractor and support methods for the
multivariate GARCH models are documented in the class of the returned objects.
Whenever using this package, please cite as
1 2 3 4 5
The releases of this package is licensed under GPL version 3.
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