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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
# Copyrights (C)
# for this R-port:
# 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# FUNCTION: PARAMETER ESTIMATION:
# 'fGARCH' S4: fGARCH Class representation
# garchFit Fits GARCH and APARCH processes
################################################################################
test.garchFit.nlminb <-
function()
{
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Load Data:
x = garchSim(n = 100)
# Algorithms:
# "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm"
# nlminb:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "nlminb", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.sqp <-
function()
{
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Load Data:
x = garchSim(n = 100)
# Algorithms:
# "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm"
### # sqp:
### fit = garchFit( ~ garch(1,1), data = x,
### algorithm = "sqp", trace = FALSE)
### print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.lbfgsb <-
function()
{
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Load Data:
x = garchSim(n = 100)
# Algorithms:
# "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm"
# lbfgsb:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "lbfgsb", trace = FALSE)
coef(fit)
# nlminb+nm:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "nlminb+nm", trace = FALSE)
coef(fit)
# lbfgsb+nm:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "lbfgsb+nm", trace = FALSE)
coef(fit)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.nlmin.nm <-
function()
{
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Load Data:
x = garchSim(n = 100)
# Algorithms:
# "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm"
# nlminb+nm:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "nlminb+nm", trace = FALSE)
coef(fit)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.lbfgsb.nm <-
function()
{
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Load Data:
x = garchSim(n = 100)
# Algorithms:
# "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm"
# lbfgsb+nm:
fit = garchFit( ~ garch(1,1), data = x,
algorithm = "lbfgsb+nm", trace = FALSE)
coef(fit)
# Return Value:
return()
}
################################################################################
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