goGARCHroll-class: class: GO-GARCH Roll Class
In rmgarch: Multivariate GARCH models

Description Objects from the Class Slots Extends Methods Author(s)

Class for the GO-GARCH Roll.

The class is returned by calling the function gogarchroll.

forecast:: Object of class "vector" which contains the rolling forecasts of the distributional parameters for each factor.
model:: Object of class "vector" containing details of the GOGARCH model specification.

Class "mGARCHroll", directly. Class "GARCHroll", by class "mGARCHroll", distance 2. Class "rGARCH", by class "mGARCHroll", distance 3.

coef: signature(object = "goGARCHroll"): Extraction of independent factor GARCH model coefficients saved from the goGARCHfit objects(returns a list).
fitted: signature(object = "goGARCHroll"): Extracts the conditional fitted forecast values (returns an xts object with index the actual forecast T+1 times).
sigma: signature(object = "goGARCHroll"): Extracts the conditional sigma forecast values (returns an xts object with index the actual forecast T+1 times). Takes optional argument “factors” (default TRUE) denoting whether to return the factor conditional sigma or the transformed sigma for the assets.
rcov: signature(object = "goGARCHroll"): Returns the time-varying n.asset x n.asset x (n.roll+1) covariance matrix in array format, where the third dimension labels are now the actual rolling n.ahead=1 forecast time indices (T+1).
rcor: signature(object = "goGARCHroll"): Returns the time-varying n.asset x n.asset x (n.roll+1) correlation matrix in array format, where the third dimension labels are now the actual rolling n.ahead=1 forecast time indices (T+1).
rcoskew: signature(object = "goGARCHroll"): Returns the time-varying n.asset x n.asset^2 x (n.roll+1) coskewness matrix in array format, where the third dimension labels are now the actual rolling n.ahead=1 forecast time indices (T+1). There is a “standardize” option which indicates whether the coskewness should be standardized by the conditional sigma (see equations in vignette).
rcokurt: signature(object = "goGARCHroll"): Returns the time-varying n.asset x n.asset^3 x (n.roll+1) cokurtosis matrix in array format, where the third dimension labels are now the actual rolling n.ahead=1 forecast time indices (T+1). There is a “standardize” option which indicates whether the cokurtosis should be standardized by the conditional sigma (see equations in vignette).
gportmoments: signature(object = "goGARCHroll"):
function:
gportmoments(object, weights)
Calculates the first 4 standardized portfolio moments using the geometric properties of the model, given a matrix of asset weights with row dimension equal to the total rolling forecast horizon. Returns an xts object of dimensions (total rolling forecast) x 4 (moments), with the index denoting the T+1 actual forecast time. If the number of assets > 100, then the kurtosis is not returned (see cokurtosis restrictions below).
convolution: signature(object = "goGARCHroll"):
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, cluster = NULL, trace = 0,...)
The convolution method takes a goGARCHroll 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 total forecast horizon. 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. The option for parallel computation is available via the use of a cluster object as elsewhere in this package. Passing this object to the distribution methods (e.g. qfft) follows the same rules as the goGARCHforecast object, namely that the index is zero based.
show: signature(object = "goGARCHroll"): Summary.