dccsim-methods: function: DCC-GARCH Simulation
In rmgarch: Multivariate GARCH models

Description Usage Arguments Details Value Author(s)

Method for creating a DCC-GARCH simulation object.

dccsim(fitORspec, n.sim = 1000, n.start = 0, m.sim = 1, 
startMethod = c("unconditional", "sample"), presigma = NULL, preresiduals = NULL, 
prereturns = NULL, preQ = NULL, preZ = NULL, Qbar = NULL, Nbar = NULL, 
rseed = NULL, mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, 
VAR.fit = NULL, prerealized = NULL, ...)

`fitORspec`	A `DCCspec` or `DCCfit` object created by calling either `dccspec` with fixed parameters or `dccfit`.
`n.sim`	The simulation horizon.
`n.start`	The burn-in sample.
`m.sim`	The number of simulations.
`startMethod`	Starting values for the simulation. Valid methods are “unconditional” for the expected values given the density, and “sample” for the ending values of the actual data from the fit object (for the dispatch method using a specification, “sample” is not relevant).
`presigma`	Allows the starting sigma values to be provided by the user for the univariate GARCH dynamics.
`prereturns`	Allows the starting return data to be provided by the user for the conditional mean simulation.
`preresiduals`	Allows the starting residuals to be provided by the user and used in the GARCH dynamics simulation.
`preQ`	Allows the starting ‘DCC-Q’ value to be provided by the user and though unnecessary for the first 1-ahead simulation using the “sample” option in the `startMethod`, this is key to obtaining a rolling n-ahead forecast type simulation (see details below).
`preZ`	Allows the starting standardized residuals to be provided by the user and though unnecessary for the first 1-ahead simulation using the “sample” option in the `startMethod`, this is key to obtaining a rolling n-ahead forecast type simulation (see details below).
`Qbar`	The DCC dynamics unconditional Q matrix, required for the specification dispatch method.
`Nbar`	The aDCC dynamics unconditional asymmetry matrix, required for the specification dispatch method.
`rseed`	Optional seeding value(s) for the random number generator. For m.sim>1, it is possible to provide either a single seed to initialize all values, or one seed per separate simulation (i.e. m.sim seeds). However, in the latter case this may result in some slight overhead depending on how large m.sim is.
`mexsimdata`	A list (equal to the number of asset) of matrices of simulated external regressor-in-mean data with row length equal to n.sim + n.start. If the fit object contains external regressors in the mean equation, this must be provided else will be assumed to be zero.
`vexsimdata`	A list (equal to the number of asset) of matrices of simulated external regressor-in-variance data with row length equal to n.sim + n.start. If the fit object contains external regressors in the variance equation, this must be provided else will be assumed to be zero.
`cluster`	A cluster object created by calling `makeCluster` from the parallel package. If it is not NULL, then this will be used for parallel estimation (remember to stop the cluster on completion).
`VAR.fit`	An VAR.fit list returned from calling the `varxfilter` or `varxfit` function with `postpad` set to “constant”. This is required for the specification dispatch method.
`prerealized`	Allows the starting realized volatility values to be provided by the user for the univariate GARCH dynamics.
`...`	.

In order to pass a correct specification to the filter routine, you must ensure that it contains the appropriate ‘fixed.pars’ in both the multivariate DCC part of the specification as well as the multiple univariate specification part, for which the method setfixed<- should be used.

A DCCsim object containing details of the DCC-GARCH simulation.

Alexios Ghalanos

rmgarch documentation built on May 2, 2019, 5:56 p.m.