# 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 # 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.nlminb <- function() { # RVs: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") # Load Data: x = garchSim(n = 100, returnClass = "numeric") # Algorithms: # "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm" # nlminb: fit = garchFit( ~ garch(1,1), data = x, algorithm = "nlminb", trace = FALSE) print(coef(fit)) # Return Value: return() } # ------------------------------------------------------------------------------ test.garchFit.sqp <- function() { # RVs: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") # Load Data: x = garchSim(n = 100, returnClass = "numeric") # Algorithms: # "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm" # sqp: fit = garchFit( ~ garch(1,1), data = x, algorithm = "lbfgsb", trace = FALSE) print(coef(fit)) # Return Value: return() } # ------------------------------------------------------------------------------ test.garchFit.lbfgsb <- function() { # RVs: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") # Load Data: x = garchSim(n = 100, returnClass = "numeric") # Algorithms: # "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm" # lbfgsb: fit = garchFit( ~ garch(1,1), data = x, algorithm = "lbfgsb", trace = FALSE) coef(fit) # nlminb+nm: fit = garchFit( ~ garch(1,1), data = x, algorithm = "nlminb+nm", trace = FALSE) coef(fit) # lbfgsb+nm: fit = garchFit( ~ garch(1,1), data = x, algorithm = "lbfgsb+nm", trace = FALSE) coef(fit) # Return Value: return() } # ------------------------------------------------------------------------------ test.garchFit.nlmin.nm <- function() { # RVs: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") # Load Data: x = garchSim(n = 100, returnClass = "numeric") # Algorithms: # "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm" # nlminb+nm: fit = garchFit( ~ garch(1,1), data = x, algorithm = "nlminb+nm", trace = FALSE) coef(fit) # Return Value: return() } # ------------------------------------------------------------------------------ test.garchFit.lbfgsb.nm <- function() { # RVs: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") # Load Data: x = garchSim(n = 100, returnClass = "numeric") # Algorithms: # "nlminb", "sqp", "lbfgsb", "nlminb+nm", "lbfgsb+nm" # lbfgsb+nm: fit = garchFit( ~ garch(1,1), data = x, algorithm = "lbfgsb+nm", trace = FALSE) coef(fit) # Return Value: return() } ################################################################################