ugarchpath-methods: function: Univariate GARCH Path Simulation

In rgarch: Flexible GARCH modelling in R

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, ...)

Arguments

spec	A univariate GARCH spec object of class uGARCHspec with the required parameters of the model supplied via the fixed.pars list argument.
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.
custom.dist	Optional density with fitted object from which to simulate. See notes below for details.
mexsimdata	Matrix of simulated external regressor-in-mean data. If the fit object contains external regressors in the mean equation, this must be provided.
vexsimdata	Matrix of simulated external regressor-in-variance data. If the fit object contains external regressors in the variance equation, this must be provided.
...	.

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 model parameters supplied via the fixed.pars argument.

Value

A uGARCHpath object containing details of the GARCH path simulation.

Author(s)

Alexios Ghalanos

See Also

For specification ugarchspec, fitting ugarchfit, filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, rolling forecast and estimation ugarchroll, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.

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)

rgarch documentation built on May 2, 2019, 5:22 p.m.