cgarchspec-methods: function: Copula-GARCH Specification
In rmgarch: Multivariate GARCH Models

Description Usage Arguments Details Value Author(s)

Method for creating a Copula-GARCH specification object prior to fitting.

cgarchspec(uspec, VAR = FALSE, robust = FALSE, lag = 1, lag.max = NULL, 
lag.criterion = c("AIC", "HQ", "SC", "FPE"), external.regressors = NULL, 
robust.control = list(gamma = 0.25, delta = 0.01, nc = 10, ns = 500), 
dccOrder = c(1, 1), asymmetric = FALSE, 
distribution.model = list(copula = c("mvnorm", "mvt"), 
method = c("Kendall", "ML"), time.varying = FALSE, 
transformation = c("parametric", "empirical", "spd")), 
start.pars = list(), fixed.pars = list())

`uspec`	A `uGARCHmultispec` object created by calling `multispec` on a list of univariate GARCH specifications.
`VAR`	Whether to fit a VAR model for the conditional mean.
`robust`	Whether to use the robust version of VAR.
`lag`	The VAR lag.
`lag.max`	The maximum VAR lag to search for best fit.
`lag.criterion`	The criterion to use for choosing the best lag when lag.max is not NULL.
`external.regressors`	Allows for a matrix of common pre-lagged external regressors for the VAR option.
`robust.control`	The tuning parameters to the robust regression including the proportion to trim (“gamma”), the critical value for reweighted estimator (“delta”), the number of subsets (“ns”) and the number of C-steps (“nc”.
`dccOrder`	The DCC autoregressive order.
`asymmetric`	Whether to include an asymmetry term to the DCC model (thus estimating the aDCC).
`distribution.model`	The Copula distribution model. Currently the multivariate Normal and Student Copula are supported.
`time.varying`	Whether to fit a dynamic DCC Copula.
`transformation`	The type of transformation to apply to the marginal innovations of the GARCH fitted models. Supported methods are parametric (Inference Function of Margins), empirical (Pseudo ML), and Semi-Parametric using a kernel interior and GPD tails (via the ‘spd’ package).
`start.pars`	(optional) Starting values for the DCC parameters (starting values for the univariate garch specification should be passed directly via the ‘uspec’ object).
`fixed.pars`	(optional) Fixed DCC parameters.

The transformation method allows for parametric (Inference-Functions for Margins), empirical (Pseudo-Likelihood) and semi-parametric (via the spd package).
When the Student Copula is jointly estimated with student margins having so that a common shape parameter is obtained, this results in the multivariate Student distribution. When estimating the Student Copula with disparate margins, a meta-student distribution is obtained. Additionally, the correlation parameter in the static Student Copula may be estimated either by Kendall's tau transformation or Maximum Likelihood.
The robust option allows for a robust version of VAR based on the multivariate Least Trimmed Squares Estimator described in Croux and Joossens (2008).

A cGARCHspec object containing details of the Copula-GARCH specification.

Alexios Galanos

rmgarch documentation built on Feb. 5, 2022, 1:07 a.m.