Usage
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())Arguments
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.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-ste
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
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.