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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
################################################################################
# garchFit(
# formula,
# data,
# init.rec = c("mci", "uev"),
# delta = 2,
# skew = 1,
# shape = 4,
# cond.dist = c("dnorm", "dsnorm", "dged", "dsged", "dstd", "dsstd"),
# include.mean = TRUE,
# include.delta = NULL,
# include.skew = NULL,
# include.shape = NULL,
# leverage = NULL,
# trace = TRUE,
# algorithm = c("sqp", "nlminb", "lbfgsb", "nlminb+nm", "lbfgsb+nm"),
# control = list(),
# title = NULL,
# description = NULL,
# ...)
# ------------------------------------------------------------------------------
test.garchFit.garch11 <-
function()
{
# Use Simulated Series - an Object of class 'ts' ...
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Normal GARCH(1, 1)
x = garchSim(n = 250)
# Fit:
fit = garchFit( ~ garch(1,1), data = x, trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.garch21 <-
function()
{
# Use Simulated Series - an Object of class 'ts' ...
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Normal-GARCH(2, 1)
model = list(omega = 1e-06, alpha = c(0.1, 0.2), beta = 0.6)
spec = garchSpec(model)
x = garchSim(spec = spec, n = 250)
# Fit
fit = garchFit( ~ garch(2,1), data = x, trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.ar1garch11 <-
function()
{
# Use Simulated Series - an Object of class 'ts' ...
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Normal AR(1)-GARCH(1,1):
model = list(omega = 1e-06, ar = -0.1, alpha = c(0.1, 0.2), beta = 0.6)
spec = garchSpec(model)
x = garchSim(spec = spec, n = 250)
# Fit:
fit = garchFit(~ arma(1,0) + garch(1,1), data = x, trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
################################################################################
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