dccspec-methods: function: DCC-GARCH Specification
In rmgarch: Multivariate GARCH Models

Description Usage Arguments Details Value Note Author(s) References

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

dccspec(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), model = c("DCC", "aDCC", "FDCC"), groups = rep(1, length(uspec@spec)), 
distribution = c("mvnorm", "mvt", "mvlaplace"), 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 re-weighted estimator (“delta”), the number of subsets (“ns”) and the number of C-steps (“nc”.
`dccOrder`	The DCC autoregressive order.
`model`	The DCC model to use, with a choice of the symmetric DCC, asymmetric (aDCC) and the Flexible DCC (FDCC). See notes for more details.
`groups`	The groups corresponding to each asset in the FDCC model, where these are assumed and checked to be contiguous and increasing (unless only 1 group).
`distribution`	The multivariate distribution. Currently the multivariate Normal, Student and Laplace are implemented, and only the Normal for the FDCC model.
`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 `dccfilter`, `dccforecast`, `dccsim` with spec, and `dccroll` methods.

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 DCCspec object containing details of the DCC-GARCH specification.

The FDCC model of Billio, Caporin and Gobbo (2006) allows different DCC parameters to govern the dynamics of the correlation of distinct groups. The drawback is a somewhat larger parameter set, and no correlation targeting. Still, it remains a feasible model for not too large a number of groups, and avoids the unrealistic assumption, particularly for large datasets, of one parameter governing all the dynamics, as in the DCC model. Note that the group indices must be increasing (unless all 1), which means that you should arrange your dataset so that the assets are ordered by their groups.

Alexios Galanos

Billio, M., Caporin, M., & Gobbo, M. 2006, Flexible dynamic conditional correlation multivariate GARCH models for asset allocation, Applied Financial Economics Letters, 2(02), 123–130.
Croux, C. and Joossens, K. 2008, Robust estimation of the vector autoregressive model by a least trimmed squares procedure, COMPSTAT, 489–501.
Cappiello, L., Engle, R.F. and Sheppard, K. 2006, Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Econometrics 4, 537–572.
Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper.