cgarchspec-methods: function: Copula-GARCH Specification

Description Usage Arguments Details Value Author(s) Examples

Description

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

Usage

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cgarchspec(uspec, VAR = FALSE, VAR.opt = list(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), 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())

Arguments

uspec

A uGARCHmultispec object created by calling ‘multispec’ on a list of univariate GARCH specifications.

VAR

Whether to fit a VAR model to the data.

VAR.opt

The VAR model options.

dccOrder

The DCC autoregressive order.

distribution.model

The Copula distribution model. Currently the multivariate Normal and Student Copula are supported. The method is for the static Copula.

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.

Details

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 Student Copula may be estimated either by Kendall's tau transformation or Maximum Likelihood.
The ‘VAR.opt’ allows for a robust version of VAR based on the multivariate Least Trimmed Squares Estimator described in Croux and Joossens (2008). The ‘robust.control’ includes additional 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”).

Value

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

Author(s)

Alexios Ghalanos

Examples

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# same specification for all:
uspec = multispec( replicate(4, ugarchspec()) )
# different univariate specifications
uspec = multispec( c( ugarchspec(variance.model = list(model = "sGARCH")), 
ugarchspec(variance.model = list(model = "gjrGARCH")), ugarchspec(mean.model = list(armaOrder = c(2,1))) ) )
# pass uspec into dccspec
spec = cgarchspec(uspec, dccOrder = c(1,1))
spec

rgarch documentation built on May 2, 2019, 5:22 p.m.