gogarch_modelspec: GOGARCH Model specification

In tsmarch: Multivariate ARCH Models

GOGARCH Model specification

Description

GOGARCH Model specification

Usage

gogarch_modelspec(
  y,
  distribution = c("norm", "nig", "gh"),
  model = "garch",
  order = c(1, 1),
  ica = "radical",
  components = NCOL(y),
  lambda_range = c(-5, 5),
  shape_range = c(0.1, 25),
  cond_mean = NULL,
  ...
)

Arguments

y	an xts matrix of pre-filtered (residuals) stationary data.
distribution	a choice for the component distributions. Valid choices are normal, normal inverse gaussian or generalized hyperbolic distribution.
model	the GARCH model to use for each factor.
order	the GARCH model order.
ica	the Independent Component Analysis algorithm. Current only the RADICAL algorithm is available.
components	the number of components to extract in the pre-whitening phase,
lambda_range	for the generalized hyperbolic distribution, the range of the lambda parameter.
shape_range	for the generalized hyperbolic distribution, the range of the shape parameter (zeta).
cond_mean	an optional matrix of the conditional mean for the series.
...	additional arguments passed to the radical function.

Value

an object of class “gogarch.spec”.

tsmarch documentation built on April 3, 2025, 7:40 p.m.