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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.snorm <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# skewed normal GARCH(1, 1)
model = list(omega = 1e-04, alpha = 0.1, beta = 0.8, skew = 0.9)
spec = garchSpec(model, cond.dist = "snorm")
x = garchSim(spec = spec, n = 250)
# Fit:
fit = garchFit( ~ garch(1,1), data = x, include.skew = TRUE,
cond.dist = "snorm", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.snorm.fixed <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# skewed normal GARCH(1, 1)
model = list(omega = 1e-04, alpha = 0.1, beta = 0.8, skew = 0.9)
spec = garchSpec(model, cond.dist = "snorm")
x = garchSim(spec = spec, n = 250)
# Fit: Skewed Normal GARCH(1, 1) with fixed skew ...
fit = garchFit(~garch(1,1), data = x, skew = 0.9,
include.skew = FALSE, cond.dist = "snorm", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.ged <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# GED-GARCH(1, 1)
model = list(omega = 1e-06, alpha = 0.1, beta = 0.8, shape = 2)
spec = garchSpec(model, cond.dist = "ged")
x = garchSim(spec = spec, n = 250)
# Fit:
fit = garchFit(~garch(1,1), data = x,
include.shape = TRUE, cond.dist = "ged", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.sged <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Skewed GED-GARCH(1, 1)
model = list(omega = 1e-05, alpha = 0.1, beta = 0.8, shape = 4, skew = 0.9)
spec = garchSpec(model, cond.dist = "sged")
x = garchSim(spec = spec, n = 250)
# Fit
fit = garchFit( ~ garch(1,1), data = x,
include.shape = TRUE, include.skew = TRUE, cond.dist = "sged",
trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.std <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Student t-GARCH(1, 1)
model = list(omega = 1e-06, alpha = 0.1, beta = 0.8, shape = 5)
spec = garchSpec(model, cond.dist = "std")
x = garchSim(spec = spec, n = 250)
# Fi
fit = garchFit( ~ garch(1,1), data = x,
include.shape = TRUE, cond.dist = "std", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.garchFit.sstd <-
function()
{
# Conditional Densities:
# "norm", "snorm", "ged", "sged", "std", "sstd"
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Skewed Student t-GARCH(1, 1)
model = list(omega = 1e-06, alpha = 0.1, beta = 0.8,
shape = 5, skew = 0.9)
spec = garchSpec(model, cond.dist = "std")
x = garchSim(spec = spec, n = 250)
# Fit:
fit = garchFit( ~ garch(1,1), data = x,
include.shape = TRUE, include.skew = TRUE,
cond.dist = "sstd", trace = FALSE)
print(coef(fit))
# Return Value:
return()
}
################################################################################
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