marginal_settings-class {portvine}

S4 class for the marginal settings

Description

Specify which marginal models (individual_spec & default_specs) are fitted and how often they are refit as well as how big the training data set is. Remember that the forecasting is done in a rolling window fashion and the arguments (train and refit size) will have to match with the arguments of the also to be specified vine_settings.

Usage

marginal_settings(
  train_size,
  refit_size,
  individual_spec = list(),
  default_spec = default_garch_spec()
)

## S4 method for signature 'marginal_settings'
show(object)

Arguments

train_size

equivalent to the slot definition below

refit_size

equivalent to the slot definition below

individual_spec

equivalent to the slot definition below

default_spec

equivalent to the slot definition below

object

An object of class marginal_settings

Details

For specifying the list for individual_spec or the argument default_spec the function default_garch_spec() might come in handy.

Value

Object of class marginal_settings

Functions

• marginal_settings(): Class constructor taking the arguments specified in the slots below

Slots

train_size

Positive count specifying the training data size.

refit_size

Positive count specifying size of the forecasting window.

individual_spec

A named list. Specify ARMA-GARCH models for individual assets by naming the list entry as the asset and providing a rugarch::ugarchspec object.

default_spec

rugarch::ugarchspec object specifying the default marginal model (used if the marginal model is not specified through individual_spec)

See Also

default_garch_spec(), vine_settings

Examples

# the most basic initialization
marginal_settings(train_size = 100, refit_size = 10)
# some individualism
marginal_settings(
  train_size = 100, refit_size = 10,
  individual_spec = list("GOOG" = default_garch_spec(ar = 3)),
  default_spec = default_garch_spec(dist = "norm")
)