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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
# FUNCTION: PARAMETER ESTIMATION:
# garchFit Fits the parameters of GARCH process
# .garchArgsParser Parses formula and data for garchFit
# .garchOptimizerControl Sets default values for Garch Optimizer
# .garchFit ... old Version, still in use by garchFit()
# .garchNames Slot names, @fit slot, parameters and controls
################################################################################
garchFit <-
function(formula = ~ garch(1,1), data,
init.rec = c("mci", "uev"),
delta = 2,
skew = 1,
shape = 4,
cond.dist = c("norm", "snorm", "ged", "sged", "std", "sstd", "snig", "QMLE"),
include.mean = TRUE,
include.delta = NULL,
include.skew = NULL,
include.shape = NULL,
leverage = NULL,
trace = TRUE,
algorithm = c("nlminb", "lbfgsb", "nlminb+nm", "lbfgsb+nm"),
hessian = c("ropt", "rcd"),
control = list(),
title = NULL,
description = NULL,
...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Fit parameters to a ARMA-GARCH model by Formula Specification
# Arguments:
# formula - ARMA(m,n) + GARCH/APARCH(p,q) mean and variance
# specification
# data - any univariate time series which can be converted
# into a timeSeries using the generic function as.timeSeries
# init.rec - names type of initialization of recurrence
# mci = mu-current-iteration, or
# uev = unconditional-expected-variances
# delta - numeric value of the exponent delta
# skew - optional skewness or skewness related parameter
# shape - optional shape parameter
# cond.dist - name of the conditional distribution, one of
# norm, snorm, ged, sged, std, sstd, snig, QMLE
# include.mean - a logical, should the mean value be estimated ?
# include.delta - should the exponent be estimated ?
# leverage - should the leverage factors be estimated ?
# trace - should the optimization be traced ?
# control - list of additional control parameters for the solver
# title - an optional title string
# description - an optional project description string
# Example:
# garchFit()
# FUNCTION:
# DEBUG:
DEBUG = FALSE
# Match arguments:
init.rec = match.arg(init.rec)
cond.dist = match.arg(cond.dist)
hessian = match.arg(hessian)
algorithm = match.arg(algorithm)
# Call:
CALL = match.call()
# Parse formula and data for garchFit ...
# Note in the new version we are working with timeSeries ...
Name = capture.output(substitute(data))
if(is.character(data)) {
eval(parse(text = paste("data(", data, ")")))
data = eval(parse(text = data))
}
# data <- if (inherits(data, "timeSeries") data else as.timeSeries(data)
data <- as.data.frame(data)
# Column Names:
if (isUnivariate(data)) {
colnames(data) <- "data"
} else {
# Check unique column Names:
uniqueNames = unique(sort(colnames(data)))
if (is.null(colnames(data))) {
stop("Column names of data are missing.")
}
if (length(colnames(data)) != length(uniqueNames)) {
stop("Column names of data are not unique.")
}
}
# Handle if we have no left-hand-side for the formula ...
# Note in this case the length of the formula is 2 (else 3):
if (length(formula) == 3 && isUnivariate(data) ) formula[2] <- NULL
if (length(formula) == 2) {
if (isUnivariate(data)) {
# Missing lhs -- we substitute the data file name as lhs ...
formula = as.formula(paste("data", paste(formula, collapse = " ")))
} else {
stop("Multivariate data inputs require lhs for the formula.")
}
}
# Robust Covariance ?
robust.cvar <- (cond.dist == "QMLE")
# Parse Arguments:
args = .garchArgsParser(formula = formula, data = data, trace = FALSE)
# DEBUG - Print Arguments:
if (DEBUG) print(list(
formula.mean = args$formula.mean,
formula.var = args$formula.var,
series = args$series,
init.rec = init.rec,
delta = delta,
skew = skew,
shape = shape,
cond.dist = cond.dist,
include.mean = include.mean,
include.delta = include.delta,
include.skew = include.skew,
include.shape = include.shape,
leverage = leverage,
trace = trace,
algorithm = algorithm,
hessian = hessian,
robust.cvar = robust.cvar,
control = control,
title = title,
description = description))
# Fit:
ans = .garchFit(
formula.mean = args$formula.mean,
formula.var = args$formula.var,
series = args$series,
init.rec,
delta,
skew,
shape,
cond.dist,
include.mean,
include.delta,
include.skew,
include.shape,
leverage,
trace,
algorithm,
hessian,
robust.cvar,
control,
title,
description,
...)
ans@call = CALL
attr(formula, "data") <- paste("data = ", Name, sep = "")
ans@formula = formula
# Return Value:
ans
}
################################################################################
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