acdsim-methods: function: ACD Simulation
In racd: Autoregressive Conditional Density Models

Description Usage Arguments Details Value Author(s)

Method for simulation from a variety of univariate ACD models.

acdsim(fit, n.sim = 1000, n.start = 0, m.sim = 1, 
presigma = NA, prereturns = NA, preresiduals = NA, preskew = NA, 
preshape = NA, rseed = NA, mexsimdata = NULL, vexsimdata = NULL, 
skxsimdata = NULL, shxsimdata = NULL, ...)

`fit`	A ACD fit object of class `ACDfit`.
`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.
`preskew`	Allows the starting skew dynamic's parameter to be provided by the user.
`preshape`	Allows the starting shape dynamic's parameter 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.
`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 mean equation, this must be provided else will be assumed zero.
`skxsimdata`	List of matrices (size of list m.sim, with each matrix having n.sim rows) of simulated external regressor-in-skew data. If the fit object contains external regressors in the skew dynamics, this must be provided else will be assumed zero.
`shxsimdata`	List of matrices (size of list m.sim, with each matrix having n.sim rows) of simulated external regressor-in-shape data. If the fit object contains external regressors in the shape dynamics, this must be provided else will be assumed zero.
`...`	For the multiplicative component sGARCH model (mcsGARCH), the additional argument ‘DailyVar’ is required and should be an xts object of length floor(n.sim/increments-per-day) by m.sim of the the daily simulated variance to use with the intraday data.

Unlike GARCH models, the conditional standardized innovations cannot be pre-generated but are dependent on the value of the conditional simulated skew and shape values at each point in time. This means that simulation is somewhat slower and there are some additional issues discussed in the vignette,

A ACDsim object containing details of the ACD simulation.

Alexios Ghalanos

racd documentation built on May 2, 2019, 4:47 p.m.