Description Usage Arguments Details Value Author(s) Examples
Method for creating a DCC-GARCH specification object prior to fitting.
1 2 3 4 5 | dccspec(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 = c("mvnorm", "mvt", "mvlaplace"),
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 |
The multivariate distribution. Currently the multivariate Normal, Student and Laplace are implemented. |
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. This is required in the |
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 DCCspec
object containing details of the DCC-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 = dccspec(uspec, dccOrder = c(1,1))
spec
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