Description Usage Arguments Details Value Author(s) Examples
Method for creating a Copula-GARCH specification object prior to fitting.
1 2 3 4 5 6 | 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())
|
uspec |
A |
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. |
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”).
A cGARCHspec
object containing details of the Copula-GARCH specification.
Alexios Ghalanos
1 2 3 4 5 6 7 8 | # 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
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