garch_modelspec {tsgarch}

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.

Details

The specification object holds the information and data which is then passed to the maximum likelihood estimation routines.

Value

An object of class “tsgarch.spec”.

Author(s)

Alexios Galanos