goGARCHsim-class {rmgarch}

class: GO-GARCH Simultion Class

Description

Class for the GO-GARCH Simulation.

Objects from the Class

The class is returned by calling the function gogarchsim.

Slots

msim:

Object of class "vector" The multivariate simulation list.

model:

Object of class "vector" containing details of the GOGARCH model specification.

Extends

Class "mGARCHsim", directly. Class "GARCHsim", by class "mGARCHsim", distance 2. Class "rGARCH", by class "mGARCHsim", distance 3.

Methods

convolution

signature(object = "goGARCHsim"):
function:
convolution(object, weights, fft.step = 0.001, fft.by = 0.0001, fft.support = c(-1, 1), support.method = c("user", "adaptive"), use.ff = TRUE, sim = 1, cluster = NULL, trace = 0,...)
The convolution method takes a goGARCHsim object and a weights vector and calculates the weighted density. The vector must be the same length as the number of assets. The “sim” option indicates the simulation index to use, given the “m.sim” option chosen in the call to the simulation function. In the case of the multivariate normal distribution, this simply returns the linear and quadratic transformation of the mean and covariance matrix, while in the multivariate affine NIG distribution this is based on the numerical inversion by FFT of the characteristic function. In that case, the “fft.step” option determines the stepsize for tuning the characteristic function inversion, “fft.by” determines the resolution for the equally spaced support given by “fft.support”, while the use of the “ff” package is recommended to avoid memory problems on some systems and is turned on via the “use.ff” option. The “support.method” option allows either a fixed support range to be given (option ‘user’), else an adaptive method is used based on the min and max of the assets at each point in time at the 0.00001 and 1-0.00001 quantiles. The range is equally spaced subject to the “fft.by” value but the returned object no longer makes use of the “ff” package returning instead a list. Finally, the option for parallel computation is available via the use of a cluster object as elsewhere in this package.

gportmoments

signature(object = "goGARCHsim"):
function:
gportmoments(object, weights, sim = 1)
Calculates the first 3 portfolio moments using the geometric properties of the model, given a matrix of asset weights with row dimension equal to the row dimension of the filtered dataset (i.e. less any lags). The “sim” option indicates the simulation index to use, given the “m.sim” option chosen in the call to the simulation function.

rcoskew

signature(object = "goGARCHsim"):
function:
rcoskew(object, from = 1, to = 1, sim = 1)
Returns the 'time-varying' NxN^2 coskewness tensor in array format. The “from” and “to” options indicate the time indices for which to return the arrays. Because of memory issues, this is limited to 100 indices. The “sim” option indicates the simulation index to use, given the “m.sim” option chosen in the call to the simulation function.

rcokurt

signature(object = "goGARCHsim"):
function:
rcokurt(object, standardize = TRUE, from = 1, to = 1)
Returns the 'time-varying' NxN^3 cokurtosis tensor in array format. The “from” and “to” options indicate the time indices for which to return the arrays. Because of memory issues, this is limited to models with less than 20 assets.

rcov

signature(object = "goGARCHsim"): Returns the time-varying NxN covariance matrix in array format. There is an additional “sim” option which indicates the simulation index to use, given the “m.sim” option chosen in the call to the simulation function. A further argument ‘output’ allows to switch between “array” and “matrix” returned object.

rcor

signature(object = "goGARCHsim"): Returns the time-varying NxN correlation matrix in array format. There is an additional “sim” option which indicates the simulation index to use, given the “m.sim” option chosen in the call to the simulation function. A further argument ‘output’ allows to switch between “array” and “matrix” returned object.

as.matrix

signature(x = "goGARCHsim"):
function:
as.matrix(x, which = "A")
This returns four types of matrices relating to the estimation of the independent components in the GO-GARCH model. Valid choices are “A” for the mixing matrix, “W” for the unmixing matrix, “U” for the rotational matrix and “K” for the whitening matrix, “Kinv” for the de-whitening matrix.

Author(s)

Alexios Galanos