ARFIMAroll-class: class: ARFIMA Rolling Forecast Class

In rgarch: Flexible GARCH modelling in R

Description

Class for the ARFIMA rolling forecast.

Slots

roll:

Object of class "vector"

forecast:

Object of class "vector"

mean.model:

Object of class "vector"

distribution.model:

Object of class "vector"

optimization.model:

Object of class "vector"

Extends

Class "ARFIMAspec", directly. Class "ARFIMA", directly. Class "rGARCH", by class "ARFIMA", distance 2.

Methods

as.ARFIMAforecast

signature(object = "ARFIMAroll"): extracts and converts the forecast object contained in the roll object to one of ARFIMAforecast given the refit number supplied by additional argument ‘refit’ (defaults to 1).

as.data.frame

signature(x = "ARFIMAroll"): extracts various values from object (see note).

report

signature(object = "ARFIMAroll"): roll backtest reports (see note).

Note

The as.data.frame extractor method allows the extraction of a variety of values from the object. Additional arguments are: which indicates the type of value to return. Valid values are “coefs” returning the parameter coefficients for all refits, “density” for the parametric density, “coefmat” for the parameter coefficients with their respective standard errors and t- and p- values, “LLH” for the likelihood across the refits, and “VaR” for the Value At Risk measure if it was requested in the roll function call.
n.ahead for the n.ahead forecast horizon to return if which was used with arguments “density” or “VaR”.
refit indicates which refit window to return the “coefmat” if that was chosen.
The report method takes the following additional arguments:
type for the report type. Valid values are “VaR” for the Value at Risk report based on the unconditional and conditional coverage tests for VaR exceedances (discussed below) and “fpm” for forecast performance measures.
n.ahead for the rolling n.ahead forecasts (defaults to 1).
VaR.alpha for the Value at Risk backtest report, this is the tail probability and defaults to 0.01.
conf.level the confidence level upon which the conditional coverage hypothesis test will be based on (defaults to 0.95).
Kupiec's unconditional coverage test looks at whether the amount of expected versus actual exceedances given the tail probability of VaR actually occur as predicted, while the conditional coverage test of Christoffersen is a joint test of the unconditional coverage and the independence of the exceedances. Both the joint and the separate unconditional test are reported since it is always possible that the joint test passes while failing either the independence or unconditional coverage test.

Author(s)

Alexios Ghalanos

rgarch documentation built on May 2, 2019, 5:22 p.m.