Description Objects from the Class Slots Extends Methods Author(s)
Class for the GO-GARCH Simulation.
The class is returned by calling the function gogarchsim
.
msim
:Object of class "vector"
The multivariate simulation list.
usim
:Object of class "vector"
The univariate simulation list.
Class "mGARCHsim"
, directly.
Class "GARCHsim"
, by class "mGARCHsim", distance 2.
Class "rGARCH"
, by class "mGARCHsim", distance 3.
signature(object = "goGARCHsim")
:
function:
convolution(object, weights, fft.step = 0.01, fft.by = 0.0001, fft.support = c(-1, 1),
support.method = c("user", "adaptive"), use.ff = TRUE, sim = 1, parallel = FALSE,
parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2), trace = 0,...)
The convolution method takes a goGARCHsim object and a weights vector or matrix and calculates the weighted
density. If a vector is given, it must be the same length as the number of assets, otherwise a matrix 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.
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 of the “ff” package returning instead a list.
Finally, the option for parallel computation is available, though it is far more efficient to use (in unix based systems only)
using the “multicore” package than “snowfall”.
signature(object = "goGARCHsim")
:
function:
gportmoments(object, weights, sim = 1)
Calculates the first 4 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 kurtosis is not returned for systems of dimensions greater
than 11 because of the memory issues related to calculations on the co-kurtosis tensor.
The “sim” option indicates the simulation index to use, given the “m.sim”
option chosen in the call to the simulation function.
signature(object = "goGARCHsim")
:
function:
rcokurt(object, from = 1, to = 1, sim = 1)
Returns the 'time-varying' NxN^4 cokurtosis tensor in array format and is limited to a dimensions of
11 assets. 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.
signature(object = "goGARCHsim")
:
function:
rcoskew(object, from = 1, to = 1, sim = 1)
Returns the 'time-varying' NxN^3 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.
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.
signature(object = "goGARCHforecast")
: 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.
Alexios Ghalanos
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.