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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 - 2007, Diethelm Wuertz, 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: DESCRIPTION:
# hngarchSim Simulates an HN-GARCH(1,1) Time Series Process
# hngarchFit Fits a HN-GARCH model by Gaussian Maximum Likelihood
# print.hngarch Print method, reports results
# summary.hngarch Summary method, diagnostic analysis
# hngarchStats Computes Unconditional Moments of a HN-GARCH Process
################################################################################
test.hngarchSim =
function()
{
# Simulate a Heston-Nandi Garch(1,1) Process
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Symmetric Model - Parameters:
model = list(lambda = 4, omega = 8e-5, alpha = 6e-5,
beta = 0.7, gamma = 0, rf = 0)
# Series:
x = hngarchSim(model = model, n = 500, n.start = 100)
# Plot:
par(mfrow = c(2, 1), cex = 0.75)
plot(x, type = "l", col = "steelblue", main = "HN Garch Symmetric Model")
grid()
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.hngarchFit =
function()
{
# Simulate a Heston-Nandi Garch(1,1) Process:
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Symmetric Model - Parameters:
model = list(lambda = 4, omega = 8e-5, alpha = 6e-5,
beta = 0.7, gamma = 0, rf = 0)
x = hngarchSim(model = model, n = 500, n.start = 100)
# Estimate Parameters:
# HN-GARCH log likelihood Parameter Estimation:
# To speed up, we start with the simulated model ...
# Fit Symmetric Case:
mle = hngarchFit(x = x, model = model, trace = TRUE, symmetric = TRUE)
print(mle)
# Assymmetric Case:
mle = hngarchFit(x = x, model = model, trace = TRUE, symmetric = FALSE)
print(mle)
# HN GARCH Plot:
# ... there is no plot - plotting is done in summary
# HN-GARCH Diagnostic Analysis:
# Note, residuals are still missing ...
par(mfrow = c(3, 1))
summary(mle, col = "steelblue")
# HN-GARCH Moments:
hngarchStats(mle$model)
# Return Value:
return()
}
################################################################################
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