fGarch (version 260.72)

garchSpec: Univariate GARCH Time Series Specification

Description

Specifies an univariate GARCH time series model.

Usage

garchSpec(model = list(omega = 1.0e-6, alpha = 0.1, beta = 0.8), 
    presample = NULL, cond.dist = c("rnorm", "rged", "rstd", "rsnorm", 
    "rsged", "rsstd"), rseed = NULL)
    
show.garchSpec(object)

Arguments

cond.dist

a character string naming the desired conditional distribution. Valid values are "dnorm", "dged", "dstd", "dsnorm", "dsged", "dsstd". The default value

model

a list of GARCH model parameters: omega - the constant coefficient of the variance equation, by default 1e-6; alpha - the value or vector of autoregressive coefficients, by default 0.1, specifying a mod

object

an object of class garchSpec as returned from the function garchSpec().

presample

a numeric three column matrix with start values for the series, for the innovations, and for the conditional variances. For an ARMA(m,n)-GARCH(p,q) process the number of rows must be at least max(m,n,p,q), longer presamples are

rseed

single integer argument, the seed for the intitialization of the random number generator for the innovations.

Value

• garchSpec returns a S4 object of class garchSpec with the following slots:
• @call
  the call of the garch function.
• @formula
  a list with two formula entries for the mean and variance equation.
• @model
  a list with the model parameters.
• @presample
  a numeric matrix with presample values.
• @distribution
  a character string with the name of the conditional distribution.
• @rseed
  an integer with the random number generator seed.

Examples

## garchSpec -

   # Normal Conditional Distribution:
   spec = garchSpec()
   spec
   
   # Skewed Normal Conditional Distribution:
   spec = garchSpec(model = list(skew = 0.8), cond.dist = "rsnorm")
   spec
   
   # Skewed GED Conditional Distribution:
   spec = garchSpec(model = list(skew = 0.9, shape = 4.8), cond.dist = "rsged")
   spec