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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
################################################################################
test.formula.methods.univariate <-
function()
{
# Numeric Vector RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Simulate normal GARCH(1, 1) numeric Vector:
spec = garchSpec()
N = 250
# Univariate Data Simulation:
x.vec = 100*garchSim(spec, N)
print(head(x.vec))
x.tS = dummyDailySeries(matrix(x.vec), units = "GARCH11")
print(head(x.tS))
# x.zoo = zoo(as.vector(x.vec), order.by = as.Date(rownames(x.tS)))
# print(head(x.zoo))
x.ts = as.ts(x.vec)
print(head(x.ts))
# Univariate Modeling:
# A numeric Vector:
fit = garchFit(~ garch(1,1), data = x.vec, trace = FALSE)
print(formula(fit))
fit = garchFit(x.vec ~ garch(1,1), data = x.vec, trace = FALSE)
print(formula(fit))
# An univariate timeSeries object with dummy dates:
fit = garchFit(~ garch(1,1), data = x.tS, trace = FALSE)
print(formula(fit))
fit = garchFit(x.tS ~ garch(1,1), data = x.tS, trace = FALSE)
print(formula(fit))
### # An univariate zoo object with dummy dates:
### fit = garchFit(~ garch(1,1), data = x.zoo, trace = FALSE)
### print(formula(fit))
### fit = garchFit(x.zoo ~ garch(1,1), data = x.zoo, trace = FALSE)
### print(formula(fit))
# An univariate "ts" object:
fit = garchFit(~ garch(1,1), data = x.ts, trace = FALSE)
print(formula(fit))
fit = garchFit(x.ts ~ garch(1,1), data = x.ts, trace = FALSE)
print(formula(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.formula.methods.multivariate <-
function()
{
# Numeric Vector RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Simulate normal GARCH(1, 1) numeric Vector:
spec = garchSpec()
N = 250
# Univariate Data Simulation:
x.vec = 100*garchSim(spec, N)
print(head(x.vec))
x.tS = dummyDailySeries(matrix(x.vec), units = "GARCH11")
print(head(x.tS))
# Multivariate Data Simulation:
X.mat = cbind(GARCH11 = x.vec, R = rnorm(N))
colnames(X.mat) <- c("GARCH11", "R")
print(head(X.mat))
X.tS = dummyDailySeries(X.mat, units = c("GARCH11", "R"))
print(head(X.tS))
# X.zoo = zoo(X.mat, order.by = as.Date(rownames(x.tS)))
# print(head(X.zoo))
X.mts = as.ts(X.mat)
print(head(X.mts)) # head doesn't wor for mts !!!
# Multivariate Modeling:
# A numeric matrix:
fit = garchFit(GARCH11 ~ garch(1,1), data = X.mat, trace = FALSE)
print(formula(fit))
fit = garchFit(100*GARCH11 ~ garch(1,1), data = X.mat, trace = FALSE)
print(formula(fit))
# A multivariate timeSeries object with dummy dates:
fit = garchFit(GARCH11 ~ garch(1,1), data = X.tS, trace = FALSE)
print(formula(fit))
fit = garchFit(100*GARCH11 ~ garch(1,1), data = X.tS, trace = FALSE)
print(formula(fit))
### # A multivariate zoo object without column names:
### fit = garchFit(GARCH11 ~ garch(1,1), data = X.zoo, trace = FALSE)
### print(formula(fit))
### fit = garchFit(100*GARCH11 + R/100 ~ garch(1,1), data = X.zoo, trace = FALSE)
### print(formula(fit))
# A multivariate "mts" object without column names:
fit = garchFit(GARCH11 ~ garch(1,1), data = X.mts, trace = FALSE)
print(formula(fit))
fit = garchFit(100*GARCH11 + R/100 ~ garch(1,1), data = X.mts, trace = FALSE)
print(formula(fit))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.formula.methods.spread <-
function()
{
# MODELING THE PERCENTUAL SPI/SBI SPREAD FROM LPP BENCHMARK:
# Series:
X.tS = as.timeSeries(data(LPP2005REC))
print(head(X.tS))
X.mat = as.matrix(X.tS)
print(head(X.mat))
# X.zoo = zoo(X.mat, order.by = as.Date(rownames(X.tS)))
# print(head(X.zoo))
X.mts = ts(X.mat)
print(head(X.mts)) # head does not work for ts objects!
# Fit:
fit = garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.tS, trace = FALSE)
print(formula(fit))
## fit = garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.mat, trace = FALSE)
## print(formula(fit))
## fit = garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.zoo, trace = FALSE)
## print(formula(fit))
## fit = garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.mts, trace = FALSE)
## print(formula(fit))
# MODELING HIGH/LOW SPREADS FROM MSFT PRICE SERIES:
# Series:
X.tS = MSFT
# Fit:
fit = garchFit(Open ~ garch(1,1), data = returns(X.tS), trace = FALSE)
print(formula(fit))
fit = garchFit(100*(High-Low) ~ garch(1,1), data = returns(X.tS),
trace = FALSE)
print(formula(fit))
# Return Value:
return()
}
################################################################################
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