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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: SOLVER:
# .garchRnlminb R coded solver nlmin
# .garchRlbgfsb R coded solver optim using method lbgfsb
# .garchRnm R coded solver nm as hybrid addon
# .garchFsqp Fortran coded solver sqp
# .garchRdonlp2 R coded solver donlp2
# .garchFmnfb Fortran coded solver mnfb
################################################################################
## test.garchSolver.dem2gbp <-
## function()
## {
## # Note, Default has changed: "cda" -> "fda"
## # Loda Data
## data(dem2gbp)
## garchFit(~ garch(1,1), dem2gbp, hessian = "fda", algorithm = "nlminb")
## # Time difference of 2.969 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006190 0.008464 -0.731 0.464545
## # omega 0.010761 0.003081 3.492 0.000479 ***
## # alpha1 0.153134 0.027720 5.524 3.31e-08 ***
## # beta1 0.805974 0.035761 22.537 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1106.608 normalized: 0.5605916
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "nlminb")
## garchFit(~ garch(1,1), dem2gbp, algorithm = "lbfgsb")
## # Time difference of 4.75 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006187 0.008462 -0.731 0.464720
## # omega 0.010785 0.002860 3.771 0.000163 ***
## # alpha1 0.153306 0.026556 5.773 7.79e-09 ***
## # beta1 0.805706 0.033612 23.971 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1106.608 normalized: 0.5605916
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "nlminb+nm")
## # Time difference of 6.094 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006190 0.008462 -0.732 0.464447
## # omega 0.010761 0.002853 3.772 0.000162 ***
## # alpha1 0.153134 0.026523 5.774 7.76e-09 ***
## # beta1 0.805974 0.033553 24.021 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1106.608 normalized: 0.5605916
## #
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "lbfgsb+nm")
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "donlp2")
## # Time difference of 7.094 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006190 0.008462 -0.732 0.464447
## # omega 0.010761 0.002853 3.772 0.000162 ***
## # alpha1 0.153134 0.026523 5.774 7.76e-09 ***
## # beta1 0.805973 0.033553 24.021 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1106.608 normalized: 0.5605916
## #
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "sqp")
## # Time difference of 1.906 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006190 0.008462 -0.732 0.464437
## # omega 0.010761 0.002853 3.772 0.000162 ***
## # alpha1 0.153134 0.026523 5.774 7.76e-09 ***
## # beta1 0.805974 0.033553 24.021 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 0.5605916 normalized: 0.0002839877
## #
## garchFit(~ garch(1,1), dem2gbp, hessian = "cda", algorithm = "mnfb")
## # Time difference of 1.344 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu -0.006190 0.008461 -0.732 0.464384
## # omega 0.010761 0.002853 3.772 0.000162 ***
## # alpha1 0.153134 0.026523 5.774 7.75e-09 ***
## # beta1 0.805974 0.033552 24.021 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 0.5605916 normalized: 0.0002839877
## # Return Value:
## return()
## }
# ------------------------------------------------------------------------------
test.garchSolver2.dem2gbp <-
function()
{
# Note, Default has changed: "cda" -> "fda"
# Loda Data
data(dem2gbp)
## garchFit(~ garch(1,1), dem2gbp, hessian = "fcd", algorithm = "nlminb")
garchFit(~ garch(1,1), dem2gbp, hessian = "rcd", algorithm = "nlminb")
garchFit(~ garch(1,1), dem2gbp, algorithm = "lbfgsb")
## garchFit(~ garch(1,1), dem2gbp, hessian = "fcd", algorithm = "nlminb+nm")
garchFit(~ garch(1,1), dem2gbp, hessian = "rcd", algorithm = "lbfgsb+nm")
# garchFit(~ garch(1,1), dem2gbp, hessian = "fcd", algorithm = "donlp2")
# garchFit(~ garch(1,1), dem2gbp, hessian = "rcd", algorithm = "sqp")
## garchFit(~ garch(1,1), dem2gbp, hessian = "fcd", algorithm = "mnfb")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
## test.garchSolver.sp500dge <-
## function()
## {
## # Loda Data:
## data(sp500dge)
## sp500dge = 100 * sp500dge
## garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "cda",
## algorithm = "lbfgsb")
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "cda",
# algorithm = "sqp")
## # Time difference of 48.954 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu 0.020646 0.006350 3.251 0.00115 **
## # ma1 0.144745 0.008358 17.319 < 2e-16 ***
## # omega 0.009988 0.001085 9.201 < 2e-16 ***
## # alpha1 0.083803 0.004471 18.742 < 2e-16 ***
## # gamma1 0.373092 0.027997 13.326 < 2e-16 ***
## # beta1 0.919401 0.004093 224.614 < 2e-16 ***
## # delta 1.435124 0.067203 21.355 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1.264345 normalized: 7.413338e-05
## #
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "fda",
# algorithm = "sqp")
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "cda",
# algorithm = "mnfb")
## # Time difference of 35.156 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu 0.020646 0.006351 3.251 0.00115 **
## # ma1 0.144745 0.008358 17.319 < 2e-16 ***
## # omega 0.009988 0.001086 9.201 < 2e-16 ***
## # alpha1 0.083803 0.004472 18.741 < 2e-16 ***
## # gamma1 0.373092 0.027997 13.326 < 2e-16 ***
## # beta1 0.919401 0.004093 224.606 < 2e-16 ***
## # delta 1.435124 0.067204 21.355 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1.264345 normalized: 7.413338e-05
## #
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "fda",
# algorithm = "mnfb")
## # Time difference of 31.516 secs
## # Error Analysis:
## # Estimate Std. Error t value Pr(>|t|)
## # mu 0.020646 0.006342 3.255 0.00113 **
## # ma1 0.144745 0.008363 17.307 < 2e-16 ***
## # omega 0.009988 0.001113 8.975 < 2e-16 ***
## # alpha1 0.083803 0.004534 18.485 < 2e-16 ***
## # gamma1 0.373092 0.027798 13.422 < 2e-16 ***
## # beta1 0.919401 0.004102 224.141 < 2e-16 ***
## # delta 1.435124 0.066283 21.652 < 2e-16 ***
## # ---
## # Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
## #
## # Log Likelihood:
## # 1.264345 normalized: 7.413338e-05
## #
## # Return Value:
## return()
## }
# ------------------------------------------------------------------------------
test.garchSolver.sp500dge <-
function()
{
# Loda Data:
data(sp500dge)
sp500dge = 100 * sp500dge
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "fcd",
# algorithm = "lbfgsb")
### garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "fcd",
### algorithm = "sqp")
### garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "rcd",
### algorithm = "sqp")
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "fcd",
# algorithm = "mnfb")
# garchFit(~ arma(0,1) + aparch(1,1), sp500dge, hessian = "rcd",
# algorithm = "mnfb")
garchFit(~ arma(0,1) + aparch(1,1), sp500dge)
# Return Value:
return()
}
################################################################################
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