rugarch: ugarchpath-methods – R documentation

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ugarchpath-methods

function: Univariate GARCH Path Simulation

Description

Method for simulating the path of a GARCH model from a variety of univariate GARCH models. This is a convenience function which does not require a fitted object (see note below).

Usage

ugarchpath(spec, n.sim=1000, n.start=0, m.sim=1, presigma=NA, prereturns=NA,
preresiduals=NA, rseed=NA, custom.dist=list(name=NA,distfit=NA), mexsimdata=NULL,
vexsimdata=NULL, trunclag=1000, ...)

Arguments

`spec`	A univariate GARCH spec object of class `uGARCHspec` with the required parameters of the model supplied via the fixed.pars list argument or `setfixed<-` method.
`n.sim`	The simulation horizon.
`n.start`	The burn-in sample.
`m.sim`	The number of simulations.
`presigma`	Allows the starting sigma values to be provided by the user.
`prereturns`	Allows the starting return data to be provided by the user.
`preresiduals`	Allows the starting residuals to be provided by the user.
`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. It is now recommended not to provide a value (i.e., keep the default of `rseed == NA`) and to call `set.seed` only once in the beginning of the session, which will ensure reproducibility.
`custom.dist`	Optional density with fitted object from which to simulate. See notes below for details.
`mexsimdata`	List of matrices (size of list m.sim, with each matrix having n.sim rows) of simulated external regressor-in-mean data. If the fit object contains external regressors in the mean equation, this must be provided else will be assumed zero.
`vexsimdata`	List of matrices (size of list m.sim, with each matrix having n.sim rows) of simulated external regressor-in-variance data. If the fit object contains external regressors in the variance equation, this must be provided else will be assumed zero.
`trunclag`	This is the truncation lags for the binomial expansion in the FIGARCH model
`...`	If the model is the “csGARCH”, then `preq` can be used to denote the previous value of the permanent component of the variance model (q, e.g. tail(fit@fit$q,1)) so that the ugarchpath method with all pre-values included will evaluate to the same result as the ugarchsim method with method equal to “sample” (assuming the same random seeding values are used).

Details

This is a convenience method to allow path simulation of various GARCH models without the need to supply a fit object as in the ugarchsim method. Instead, a GARCH spec object is required with the fixed model parameters. The mcsGARCH model is not supported for the path method-use ugarchsim instead.

Value

A uGARCHpath object containing details of the GARCH path simulation.

Author(s)

Alexios Ghalanos

Examples

## Not run: 
# create a basic sGARCH(1,1) spec:
spec=ugarchspec(variance.model=list(model="sGARCH", garchOrder=c(1,1)),
		mean.model=list(armaOrder=c(0,0), include.mean=TRUE, garchInMean =
		FALSE, inMeanType = 2), distribution.model="sstd",
		fixed.pars=list(mu=0.001,omega=0.00001, alpha1=0.05, beta1=0.90,
		shape=4,skew=2))
# simulate the path
path.sgarch = ugarchpath(spec, n.sim=3000, n.start=1, m.sim=1)

## End(Not run)

rugarch

Univariate GARCH Models

v1.4-4

GPL-3

Authors

Alexios Ghalanos [aut, cre], Tobias Kley [ctb]

Initial release

2020-07-14