dccspec-methods: function: DCC-GARCH Specification

Description Usage Arguments Details Value Note Author(s) References

Description

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

Usage

1
2
3
4
5
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()) 

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

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.

Details

The robust option allows for a robust version of VAR based on the multivariate Least Trimmed Squares Estimator described in Croux and Joossens (2008).

Value

A DCCspec object containing details of the DCC-GARCH specification.

Note

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.

Author(s)

Alexios Ghalanos

References

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.


rmgarch documentation built on Sept. 13, 2019, 5:04 p.m.