goGARCHfft-class: Class: GO-GARCH portfolio density

In rmgarch: Multivariate GARCH Models

Class: GO-GARCH portfolio density

Description

Class for the GO-GARCH portfolio density

Objects from the Class

The class is returned by calling the function convolution on objects of class goGARCHfit, goGARCHfilter, goGARCHforecast, goGARCHsim and goGARCHroll

Slots

dist:

A list with the portfolio density and other details.

model:

A list with the model details carried across objects.

Methods

dfft

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.

pfft

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.

qfft

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.

nportmoments

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.

Note

In the case that convolution was called on a goGARCHforecast or goGARCHroll object, the dist slot will contain the maximum 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 * n.moments * (n.roll + 1)), this does not make sense for the distribution methods (d*, p*, q*). It is therefore 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 indicates the unconditional density forecast at time T + 5. Similarly, when calling dfft(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 indicates the first (of two, since rolls = 0:1) rolling forecast density.

Author(s)

Alexios Galanos

rmgarch documentation built on Sept. 1, 2025, 1:09 a.m.