Description Slots Extends Methods Note Author(s)
Class for the ARFIMA rolling forecast.
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"
Class "ARFIMAspec"
, directly.
Class "ARFIMA"
, directly.
Class "rGARCH"
, by class "ARFIMA", distance 2.
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).
signature(x = "ARFIMAroll")
: extracts various
values from object (see note).
signature(object = "ARFIMAroll")
: roll backtest reports (see 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.
Alexios Ghalanos
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