garch_modelspec: GARCH Model Specification
In tsgarch: Univariate GARCH Models

garch_modelspec

R Documentation

GARCH Model Specification

Description

Specifies a GARCH model prior to estimation.

Usage

garch_modelspec(
  y,
  model = "garch",
  constant = FALSE,
  order = c(1, 1),
  variance_targeting = FALSE,
  vreg = NULL,
  multiplicative = FALSE,
  init = c("unconditional", "sample", "backcast"),
  backcast_lambda = 0.7,
  sample_n = 10,
  distribution = "norm",
  ...
)

Arguments

`y`	an xts vector.
`model`	the type of GARCH model. Valid choices are “garch” for vanilla GARCH, “gjr” for asymmetric GARCH, “egarch” for exponential GARCH, “aparch” for asymmetric power ARCH, “csGARCH” for the component GARCH, “igarch” for the integrated GARCH.
`constant`	whether to estimate a constant (mean) for y,
`order`	the (p,q) GARCH order.
`variance_targeting`	whether to use variance targeting rather than estimating the conditional variance intercept.
`vreg`	an optional xts matrix of regressors in the conditional variance equation.
`multiplicative`	whether to exponentiate the contribution of the regressors else will be additive. In the case of the “egarch” model, since this is already a multiplicative model, the regressors are additive irrespective of the choice made.
`init`	the method to use to initialize the recursion of the conditional variance.
`backcast_lambda`	the decay power for the exponential smoothing used when initializing the recursion using the backcast method.
`sample_n`	the number of data points to use when initializing the recursion using the sample method.
`distribution`	a valid distribution from the available re-parameterized distributions of the package.
`...`	not used.