class: Univariate GARCH Rolling Forecast Class

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Description

Class for the univariate GARCH rolling forecast.

Slots

forecast:

Object of class "vector"

model:

Object of class "vector"

Extends

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

Methods

as.data.frame

signature(x = "uGARCHroll"): Extracts various values from object (see note).

plot

signature(x = "uGARCHroll", y = "missing"): Roll result backtest plots (see note).

report

signature(object = "uGARCHroll"): Roll backtest reports (see note).

resume

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

fpm

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

coef

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

quantile

signature(x = "uGARCHroll"): Calculates and returns, given a vector of probabilities (additional argument “probs”), the conditional quantiles of the rolling object as an xts matrix.

pit

signature(object = "uGARCHroll"): Calculates and returns the conditional probability integral transform given the realized data and forecast density.

convergence

signature(object = "uGARCHroll"): Returns the convergence code for the estimation windows, with 0 indicating that all have converged and 1 that there were non-converged windows. In the latter case the ‘nonconverged’ attribute is also printed of those windows which failed to converge.

show

signature(object = "uGARCHroll"): 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 plot method takes the following additional arguments:
1.which allows for either a numeric value of 1:4, else will default to “ask” for interactive printing of the options in the command windows. Additionally, the value of “all” wil create a 2x2 chart with all plots.
2.VaR.alpha for the Value at Risk backtest plot, this is the tail probability and defaults to 0.01.
3.density.support the support for the time varying density plot density, defaults to c(-0.15, 0.15) but you should change this to something more appropriate for your data and period under consideration.
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

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