ARFIMAroll-class {rugarch}

class: ARFIMA Rolling Forecast Class

Description

Class for the ARFIMA rolling forecast.

Slots

forecast:

Object of class "vector"

model:

Object of class "vector"

Extends

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

Methods

as.data.frame

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

resume

signature(object = "ARFIMAroll"): Resumes a rolling backtest which has non-converged windows using alternative solver and control parameters.

fpm

signature(object = "ARFIMAroll"): Forecast performance measures.

coef

signature(object = "ARFIMAroll"): Extracts the list of coefficients for each estimated window in the rolling backtest.

report

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

show

signature(object = "ARFIMAroll"): Summary.

Note

The as.data.frame extractor method allows the extraction of either the conditional forecast density or the VaR. It takes additional argument which with valid values either “density” or “VaR”.
The coef method will return a list of the coefficients and their robust standard errors (assuming the keep.coef argument was set to TRUE in the ugarchroll function), and the ending date of each estimation window.
The report method takes the following additional arguments:
1.type for the report type. Valid values are “VaR” for the VaR report based on the unconditional and conditional coverage tests for exceedances (discussed below) and “fpm” for forecast performance measures.
2.VaR.alpha (for the VaR backtest report) is the tail probability and defaults to 0.01.
3.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. The fpm method (separately from report) takes additional logical argument summary, which when TRUE will return the mean squared error (MSE), mean absolute error (MAE) and directional accuracy of the forecast versus realized returns. When FALSE, it will return a data.frame of the time series of squared (SE) errors, absolute errors (AE), directional hits (HITS), and a VaR Loss function described in Gonzalez-Rivera, Lee, and Mishra (2004) for each coverage level where it was calculated. This can then be compared, with the VaR loss of competing models using such tests as the model confidence set (MCS) of Hansen, Lunde and Nason (2011).

Author(s)

Alexios Ghalanos