Class for the GO-GARCH portfolio density
The class is returned by calling the function convolution on
objects of class '>goGARCHfit, '>goGARCHfilter,
'>goGARCHforecast, '>goGARCHsim and
'>goGARCHroll
dist:A list with the portfolio density and other details.
model:A list with the model details carried across objects.
signature(object = "goGARCHfft"):
The takes additional argument “index” to indicate the particular time
point, and returns an interpolated density function which may be called like
any other “d” type density function.
signature(object = "goGARCHfft")
The takes additional argument “index” to indicate the particular time
point, and returns an interpolated distribution function which may be called
like any other “p” type distribution function.
signature(object = "goGARCHfft")
This takes additional argument “index” to indicate the particular time
point, and returns an interpolated quantile function which may be called like
any other “q” type quantile function. This may also be used to generate
pseudo-random variables from the distribution by using random standard uniform
numbers as inputs.
signature(object = "goGARCHfft"):
Calculate and returns a matrix of the first 4 standardized moments by evaluation
of the portfolio density using quadrature based method (i.e. calling R's
“integrate” function on the portfolio FFT based density). Depending on
the GOGARCH class the density was based (e.g. goGARCHfit vs goGARCHforecast),
the format of the output will be different, and generally follow the
format ‘rules’ of that class.
In the case that convolution was called on a '>goGARCHforecast
or '>goGARCHroll object, the dist slot will contain the max
of n.ahead or n.roll. There should be no confusion here since the multivariate
forecast methods in rmgarch only allow either n.ahead>1 with n.roll = 0
(pure unconditional), or n.ahead = 1 with n.roll>=0 (pure rolling), and only the
latter in the case of a gogarchroll. While the nportmoments
method reconstitutes the forecasts into a more familiar form (n.ahead x n.moments x
(n.roll+1)), this does not make sense for the distribution methods (d*, p*,
and q*), and it is understood that when the user calls for example
dfft(object, index=5) on an object created from a forecast with
n.ahead=10 and n.roll=0, the index is meant to indicate the unconditional
density forecast at time T+5. Similarly, when calling
codedfft(object, index=0) on an object created from a forecast with
n.ahead=1 and n.roll = 1 (remember that n.roll is zero based), the index is
meant to indicate the first (of two, since rolls = 0:1) rolling forecast
density.