Nothing
### <======================================================================>
setClass("ghyp",
representation(call = "call",
lambda = "numeric",
alpha.bar = "numeric",
chi = "numeric",
psi = "numeric",
mu = "numeric",
sigma = "matrix",
gamma = "numeric",
model = "character",
dimension = "numeric",
expected.value = "numeric",
variance = "matrix",
parametrization = "character",
data = "matrix"),
prototype(call = call("ghyp"),
lambda = 1,
alpha.bar = 1,
chi = 1,
psi = 1,
mu = 0,
sigma = matrix(0),
gamma = 0,
model = "Symmetric Generalized Hyperbolic",
dimension = 1,
expected.value = numeric(0),
variance = matrix(0),
parametrization = "alpha.bar",
data = matrix(0))
)
### <---------------------------------------------------------------------->
### <======================================================================>
setClass("mle.ghyp",
representation(n.iter = "numeric",
llh = "numeric",
converged = "logical",
error.code = "numeric",
error.message = "character",
fitted.params = "logical",
aic = "numeric",
parameter.variance = "matrix",
trace.pars = "list"),
prototype(n.iter = numeric(0),
llh = numeric(0),
converged = FALSE,
error.code = 0,
error.message = character(0),
fitted.params = logical(0),
aic = numeric(0),
parameter.variance = matrix(0),
trace.pars = list()),
contains = "ghyp"
)
### <---------------------------------------------------------------------->
### <======================================================================>
#' Class ghyp.attribution
#'
#' The class \dQuote{ghyp.attribution} contains the Expected Shortfall of
#' the portfolio as well as the contribution of each asset to the total risk
#' and the sensitivity of each Asset. The sensitivity gives an information
#' about the overall risk modification of the portfolio if the weight in a
#' given asset is marginally increased or decreased (1 percent).
#'
#' @docType class
#' @section Objects from the Class:
#' Objects should only be created by calls to the constructors \code{\link{ESghyp.attribution}}.
#'
#' @slot ES Portfolio's expected shortfall (ES) for a given confidence level. Class \code{matrix}.
#' @slot contribution Contribution of each asset to the overall ES. Class \code{matrix}.
#' @slot sensitivity Sensitivity of each asset. Class \code{matrix}.
#' @slot weights Weight of each asset.
#'
#' @method \dQuote{plot} plot
#' @method \dQuote{weights} weights
#' @method \dQuote{contribution} contribution
#' @method \dQuote{sensitivity} sensitivity
#'
#' @author Marc Weibel
#'
#' @note When showing special cases of the generalized hyperbolic distribution
#'the corresponding fixed parameters are not printed.
#'
#' @examples
#' \dontrun{
#' data(smi.stocks)
#' multivariate.fit <- fit.ghypmv(data = smi.stocks,
#' opt.pars = c(lambda = FALSE, alpha.bar = FALSE),
#' lambda = 2)
#'
#' portfolio <- ESghyp.attribution(0.01, multivariate.fit)
#' summary(portfolio)
#' }
#' @keywords classes
#' @name ghyp.attribution-class
#' @rdname ghyp.attribution-class
#' @exportClass
setClass("ghyp.attribution",
representation(weights = 'matrix',
ES = 'matrix',
contribution = 'matrix',
sensitivity = 'matrix'),
prototype(weights = matrix(0),
ES = matrix(0),
contribution = matrix(0),
sensitivity = matrix(0))
)
### <---------------------------------------------------------------------->
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