Nothing
R/rugarch-multi.R
In rugarch: Univariate GARCH models
#################################################################################
##
## R package rugarch by Alexios Ghalanos Copyright (C) 2008-2013.
## This file is part of the R package rugarch.
##
## The R package rugarch is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package rugarch 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 General Public License for more details.
##
#################################################################################
.multispecall = function( speclist ){
model = unlist( strsplit(class(speclist[[1]]), "spec") )
if( model == "ARFIMA" ){
ans = .multispecarfima( speclist )
} else{
ans = .multispecgarch( speclist )
}
return( ans )
}
.multispecgarch = function( speclist )
{
# first create a spec which goes through validation process
tp = 1
if( !all(unlist(lapply(speclist, FUN = function(x) is(x, "uGARCHspec"))) ) ){
stop("\nNot a valid list of univariate GARCH specs.")
}
# then check type
n = length(speclist)
for(i in 2:n){
modelnames1 = rownames( speclist[[i]]@model$pars[speclist[[i]]@model$pars[,3]==1, ] )
modelnames2 = rownames( speclist[[i-1]]@model$pars[speclist[[i-1]]@model$pars[,3]==1, ] )
if(length(modelnames1) != length(modelnames2))
{
tp = 0
break()
} else{
if(!all(modelnames1 == modelnames2))
{
tp = 0
break()
}
}
}
if(tp) type = "equal" else type = "unequal"
if(type=="unequal"){
# mcsGARCH and realGARCH cannot be in unequal specification. Either all the same or none.
mod = sapply(speclist, function(x) x@model$modeldesc$vmodel)
if(any(mod=="mcsGARCH")) stop("\nmultispec-->error: cannot have unequal spec containing mcsGARCH model.\n")
if(any(mod=="realGARCH")) stop("\nmultispec-->error: cannot have unequal spec containing realGARCH model.\n")
}
ans = new("uGARCHmultispec",
spec = speclist,
type = type)
return(ans)
}
# a multifit function possible utilizing parallel execution returning a fitlist
# object
.multifitgarch = function(multispec, data, out.sample = 0, solver = "solnp",
solver.control = list(), fit.control = list(stationarity = 1, fixed.se = 0, scale = 0,
rec.init = "all"), cluster = NULL, ...)
{
n = length(multispec@spec)
if(is.null(data)) stop("\nmultifit GARCH-->error: multifit requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data)) stop("\nmultifit GARCH-->error: multifit only supports xts, matrix or data.frame objects for the data", call. = FALSE)
asset.names = colnames(data)
if(dim(data)[2] != n)
stop("\nmultifit GARCH-->error: speclist length not equal to data length", call. = FALSE)
fitlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(multispec@type=="equal"){
mod = multispec@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifit-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifit-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifit-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifit-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifit-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifit-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifit-->error: DailyVar must have the same number of columns as the data.")
}
} else{
mod = "X"
}
##################
# Parallel Execution Prelim Checks
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multispec", "data", "out.sample", "solver",
"solver.control", "fit.control"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
return(ans)
})
} else if(mod=="mcsGARCH"){
clusterExport(cluster, "DailyVar", envir = environment())
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
return(ans)
})
} else{
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
return(ans)
})
}
} else{
if(mod=="realGARCH"){
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
return(ans)
})
} else if(mod=="mcsGARCH"){
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
return(ans)
})
} else{
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
return(ans)
})
}
}
# converged: print
desc = list()
desc$type = multispec@type
desc$asset.names = asset.names
ans = new("uGARCHmultifit",
fit = fitlist,
desc = desc)
return(ans)
}
.multifiltergarch1 = function(multifitORspec, data = NULL, out.sample = 0,
n.old = NULL, rec.init = 'all', cluster = NULL, ...)
{
fitlist = multifitORspec
n = length(fitlist@fit)
if(is.null(data))
stop("\nmultifilter GARCH-->error: multifilter requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultifilter GARCH-->error: multifilter only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultifilter GARCH-->error: fitlist length not equal to data length", call. = FALSE)
asset.names = colnames(data)
filterlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(length(rec.init) == 1 | length(rec.init) < n) rec.init = rep(rec.init, n)
mod = fitlist@fit[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifilter-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifilter-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifilter-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifilter-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifilter-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifilter-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifilter-->error: DailyVar must have the same number of columns as the data.")
}
specx = vector(mode = "list", length = n)
for(i in 1:n){
specx[[i]] = getspec(fitlist@fit[[i]])
specx[[i]]@model$fixed.pars = as.list(coef(fitlist@fit[[i]]))
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("specx", "data", "out.sample", "n.old", "rec.init"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
} else{
if(mod=="realGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
}
desc = list()
desc$type = "equal"
desc$asset.names = asset.names
ans = new("uGARCHmultifilter",
filter = filterlist,
desc = desc)
return(ans)
}
.multifiltergarch2 = function(multifitORspec, data = NULL, out.sample = 0,
n.old = NULL, rec.init = 'all', cluster = NULL, ...)
{
speclist = multifitORspec
n = length(speclist@spec)
if(is.null(data))
stop("\nmultifilter GARCH-->error: multifilter requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultifilter GARCH-->error: multifilter only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultifilter GARCH-->error: multispec length not equal to data length", call. = FALSE)
asset.names = colnames(data)
filterlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(length(rec.init) == 1 | length(rec.init) < n) rec.init = rep(rec.init, n)
if(speclist@type=="equal"){
mod = speclist@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifilter-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifilter-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifilter-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifilter-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifilter-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifilter-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifilter-->error: DailyVar must have the same number of columns as the data.")
}
} else{
mod = "X"
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("speclist", "data", "out.sample", "n.old", "rec.init"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
clusterExport(cluster, "DailyVar", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
} else{
if(mod=="realGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
}
# converged: print
desc = list()
desc$type = speclist@type
desc$asset.names = asset.names
ans = new("uGARCHmultifilter",
filter = filterlist,
desc = desc)
return(ans)
}
.multiforecastgarch1 = function(multifitORspec, data = NULL, n.ahead = 1,
n.roll = 0, out.sample = 0, external.forecasts = list(mregfor = NULL, vregfor = NULL),
cluster = NULL, ...)
{
# only need to account for mcsGARCH and only partially
multifit = multifitORspec
n = length(multifit@fit)
asset.names = multifit@desc$asset.names
forecastlist = vector(mode = "list", length = n)
mod = multifit@fit[[1]]@model$modeldesc$vmodel
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) includeD = FALSE else includeD = TRUE
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multifit", "n.ahead", "n.roll", "external.forecasts"), envir = environment())
if(mod=="mcsGARCH"){
if(includeD) clusterExport(cluster, c("includeD","DailyVar"), envir = environment())
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts,
if(includeD) DailyVar = DailyVar[,i] else DailyVar = NULL)
})
} else{
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts)
})
}
} else{
if(mod=="mcsGARCH"){
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts,
if(includeD) DailyVar = DailyVar[,i] else DailyVar = NULL)
})
} else{
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts)
})
}
}
desc = list()
desc$type = "equal"
desc$asset.names = asset.names
ans = new("uGARCHmultiforecast",
forecast = forecastlist,
desc = desc)
return(ans)
}
.multiforecastgarch2 = function(multifitORspec, data = NULL, n.ahead = 1, n.roll = 0, out.sample = 0,
external.forecasts = list(mregfor = NULL, vregfor = NULL), cluster =NULL, ...)
{
multispec = multifitORspec
n = length(multispec@spec)
if(is.null(data))
stop("\nmultiforecast GARCH-->error: multiforecast with multiple spec requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultiforecast GARCH-->error: multiforecast only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultiforecast GARCH-->error: multispec length not equal to data length", call. = FALSE)
asset.names = colnames(data)
forecastlist = vector(mode = "list", length = n)
if(is.null(out.sample)) out.sample = 0
if(length(out.sample) == 1) out.sample = rep(out.sample, n)
if(length(out.sample) !=n )
stop("\nmultiforecast GARCH-->error: out.sample length not equal to data length", call. = FALSE)
if(multispec@type=="equal"){
mod = multispec@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultiforecast GARCH-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultiforecast GARCH-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultiforecast GARCH-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultiforecast GARCH-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
stop("\nugarchforecast (and multiforecast) with specification object not available for mcsGARCH model")
}
} else{
mod = "X"
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multispec", "data", "n.ahead", "n.roll",
"out.sample", "external.forecasts"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts,
realizedVol = realVol[,i])
})
} else{
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts)
})
}
} else{
if(mod=="realGARCH"){
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts,
realizedVol = realVol[,i])
})
} else{
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts, ...)
})
}
}
desc = list()
desc$type = multispec@type
desc$asset.names = asset.names
ans = new("uGARCHmultiforecast",
forecast = forecastlist,
desc = desc)
return(ans)
}
Try the rugarch package in your browser
Any scripts or data that you put into this service are public.
rugarch documentation built on May 2, 2019, 4:43 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#################################################################################
##
## R package rugarch by Alexios Ghalanos Copyright (C) 2008-2013.
## This file is part of the R package rugarch.
##
## The R package rugarch is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## The R package rugarch 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 General Public License for more details.
##
#################################################################################
.multispecall = function( speclist ){
model = unlist( strsplit(class(speclist[[1]]), "spec") )
if( model == "ARFIMA" ){
ans = .multispecarfima( speclist )
} else{
ans = .multispecgarch( speclist )
}
return( ans )
}
.multispecgarch = function( speclist )
{
# first create a spec which goes through validation process
tp = 1
if( !all(unlist(lapply(speclist, FUN = function(x) is(x, "uGARCHspec"))) ) ){
stop("\nNot a valid list of univariate GARCH specs.")
}
# then check type
n = length(speclist)
for(i in 2:n){
modelnames1 = rownames( speclist[[i]]@model$pars[speclist[[i]]@model$pars[,3]==1, ] )
modelnames2 = rownames( speclist[[i-1]]@model$pars[speclist[[i-1]]@model$pars[,3]==1, ] )
if(length(modelnames1) != length(modelnames2))
{
tp = 0
break()
} else{
if(!all(modelnames1 == modelnames2))
{
tp = 0
break()
}
}
}
if(tp) type = "equal" else type = "unequal"
if(type=="unequal"){
# mcsGARCH and realGARCH cannot be in unequal specification. Either all the same or none.
mod = sapply(speclist, function(x) x@model$modeldesc$vmodel)
if(any(mod=="mcsGARCH")) stop("\nmultispec-->error: cannot have unequal spec containing mcsGARCH model.\n")
if(any(mod=="realGARCH")) stop("\nmultispec-->error: cannot have unequal spec containing realGARCH model.\n")
}
ans = new("uGARCHmultispec",
spec = speclist,
type = type)
return(ans)
}
# a multifit function possible utilizing parallel execution returning a fitlist
# object
.multifitgarch = function(multispec, data, out.sample = 0, solver = "solnp",
solver.control = list(), fit.control = list(stationarity = 1, fixed.se = 0, scale = 0,
rec.init = "all"), cluster = NULL, ...)
{
n = length(multispec@spec)
if(is.null(data)) stop("\nmultifit GARCH-->error: multifit requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data)) stop("\nmultifit GARCH-->error: multifit only supports xts, matrix or data.frame objects for the data", call. = FALSE)
asset.names = colnames(data)
if(dim(data)[2] != n)
stop("\nmultifit GARCH-->error: speclist length not equal to data length", call. = FALSE)
fitlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(multispec@type=="equal"){
mod = multispec@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifit-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifit-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifit-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifit-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifit-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifit-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifit-->error: DailyVar must have the same number of columns as the data.")
}
} else{
mod = "X"
}
##################
# Parallel Execution Prelim Checks
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multispec", "data", "out.sample", "solver",
"solver.control", "fit.control"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
return(ans)
})
} else if(mod=="mcsGARCH"){
clusterExport(cluster, "DailyVar", envir = environment())
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
return(ans)
})
} else{
fitlist = parLapply(cluster, as.list(1:n), fun = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
return(ans)
})
}
} else{
if(mod=="realGARCH"){
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
realizedVol = realVol[,i]), silent=TRUE)
}
return(ans)
})
} else if(mod=="mcsGARCH"){
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control,
DailyVar = DailyVar[,i]), silent=TRUE)
}
return(ans)
})
} else{
fitlist = lapply(as.list(1:n), FUN = function(i){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = solver,
solver.control = solver.control, fit.control = fit.control), silent=TRUE)
if(inherits(ans, 'try-error')){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
if(convergence(ans)==1){
ans<-try(ugarchfit(spec = multispec@spec[[i]], data = data[, i, drop = FALSE],
out.sample = out.sample[i], solver = "gosolnp",
fit.control = fit.control), silent=TRUE)
}
return(ans)
})
}
}
# converged: print
desc = list()
desc$type = multispec@type
desc$asset.names = asset.names
ans = new("uGARCHmultifit",
fit = fitlist,
desc = desc)
return(ans)
}
.multifiltergarch1 = function(multifitORspec, data = NULL, out.sample = 0,
n.old = NULL, rec.init = 'all', cluster = NULL, ...)
{
fitlist = multifitORspec
n = length(fitlist@fit)
if(is.null(data))
stop("\nmultifilter GARCH-->error: multifilter requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultifilter GARCH-->error: multifilter only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultifilter GARCH-->error: fitlist length not equal to data length", call. = FALSE)
asset.names = colnames(data)
filterlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(length(rec.init) == 1 | length(rec.init) < n) rec.init = rep(rec.init, n)
mod = fitlist@fit[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifilter-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifilter-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifilter-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifilter-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifilter-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifilter-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifilter-->error: DailyVar must have the same number of columns as the data.")
}
specx = vector(mode = "list", length = n)
for(i in 1:n){
specx[[i]] = getspec(fitlist@fit[[i]])
specx[[i]]@model$fixed.pars = as.list(coef(fitlist@fit[[i]]))
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("specx", "data", "out.sample", "n.old", "rec.init"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
} else{
if(mod=="realGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = specx[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
}
desc = list()
desc$type = "equal"
desc$asset.names = asset.names
ans = new("uGARCHmultifilter",
filter = filterlist,
desc = desc)
return(ans)
}
.multifiltergarch2 = function(multifitORspec, data = NULL, out.sample = 0,
n.old = NULL, rec.init = 'all', cluster = NULL, ...)
{
speclist = multifitORspec
n = length(speclist@spec)
if(is.null(data))
stop("\nmultifilter GARCH-->error: multifilter requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultifilter GARCH-->error: multifilter only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultifilter GARCH-->error: multispec length not equal to data length", call. = FALSE)
asset.names = colnames(data)
filterlist = vector(mode = "list", length = n)
if(length(out.sample) == 1 | length(out.sample) < n) out.sample = rep(out.sample, n)
if(length(rec.init) == 1 | length(rec.init) < n) rec.init = rep(rec.init, n)
if(speclist@type=="equal"){
mod = speclist@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultifilter-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultifilter-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultifilter-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultifilter-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) stop("\nmultifilter-->error: mcsGARCH model requires DailyVar xts matrix.")
if(!is.xts(DailyVar)) stop("\nmultifilter-->error: DailyVar must be an xts matrix.")
if(ncol(DailyVar)!=n) stop("\nmultifilter-->error: DailyVar must have the same number of columns as the data.")
}
} else{
mod = "X"
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("speclist", "data", "out.sample", "n.old", "rec.init"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
clusterExport(cluster, "DailyVar", envir = environment())
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
} else{
if(mod=="realGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
realizedVol = realVol[,i])
})
} else if(mod=="mcsGARCH"){
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i],
DailyVar = DailyVar[,i])
})
} else{
filterlist = lapply(as.list(1:n), FUN = function(i){
ugarchfilter(data = data[, i, drop = FALSE], spec = speclist@spec[[i]],
out.sample = out.sample[i], n.old = n.old, rec.init = rec.init[i])
})
}
}
# converged: print
desc = list()
desc$type = speclist@type
desc$asset.names = asset.names
ans = new("uGARCHmultifilter",
filter = filterlist,
desc = desc)
return(ans)
}
.multiforecastgarch1 = function(multifitORspec, data = NULL, n.ahead = 1,
n.roll = 0, out.sample = 0, external.forecasts = list(mregfor = NULL, vregfor = NULL),
cluster = NULL, ...)
{
# only need to account for mcsGARCH and only partially
multifit = multifitORspec
n = length(multifit@fit)
asset.names = multifit@desc$asset.names
forecastlist = vector(mode = "list", length = n)
mod = multifit@fit[[1]]@model$modeldesc$vmodel
if(mod=="mcsGARCH"){
DailyVar = list(...)$DailyVar
if(is.null(DailyVar)) includeD = FALSE else includeD = TRUE
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multifit", "n.ahead", "n.roll", "external.forecasts"), envir = environment())
if(mod=="mcsGARCH"){
if(includeD) clusterExport(cluster, c("includeD","DailyVar"), envir = environment())
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts,
if(includeD) DailyVar = DailyVar[,i] else DailyVar = NULL)
})
} else{
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts)
})
}
} else{
if(mod=="mcsGARCH"){
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts,
if(includeD) DailyVar = DailyVar[,i] else DailyVar = NULL)
})
} else{
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multifit@fit[[i]], data = NULL, n.ahead = n.ahead,
n.roll = n.roll, external.forecasts = external.forecasts)
})
}
}
desc = list()
desc$type = "equal"
desc$asset.names = asset.names
ans = new("uGARCHmultiforecast",
forecast = forecastlist,
desc = desc)
return(ans)
}
.multiforecastgarch2 = function(multifitORspec, data = NULL, n.ahead = 1, n.roll = 0, out.sample = 0,
external.forecasts = list(mregfor = NULL, vregfor = NULL), cluster =NULL, ...)
{
multispec = multifitORspec
n = length(multispec@spec)
if(is.null(data))
stop("\nmultiforecast GARCH-->error: multiforecast with multiple spec requires a data object", call. = FALSE)
if(!is.xts(data) & !is.matrix(data) & !is.data.frame(data))
stop("\nmultiforecast GARCH-->error: multiforecast only supports xts, matrix or data.frame objects for the data", call. = FALSE)
if(dim(data)[2] != n)
stop("\nmultiforecast GARCH-->error: multispec length not equal to data length", call. = FALSE)
asset.names = colnames(data)
forecastlist = vector(mode = "list", length = n)
if(is.null(out.sample)) out.sample = 0
if(length(out.sample) == 1) out.sample = rep(out.sample, n)
if(length(out.sample) !=n )
stop("\nmultiforecast GARCH-->error: out.sample length not equal to data length", call. = FALSE)
if(multispec@type=="equal"){
mod = multispec@spec[[1]]@model$modeldesc$vmodel
if(mod=="realGARCH"){
realVol = list(...)$realizedVol
if(is.null(realVol)) stop("\nmultiforecast GARCH-->error: realGARCH model requires realizedVol xts matrix.")
if(!is.xts(realVol)) stop("\nmultiforecast GARCH-->error: realizedVol must be an xts matrix.")
if(ncol(realVol)!=n) stop("\nmultiforecast GARCH-->error: realizedVol must have the same number of columns as the data.")
if(nrow(realVol)!=nrow(data)) stop("\nmultiforecast GARCH-->error: realizedVol must have the same number of rows as the data.")
}
if(mod=="mcsGARCH"){
stop("\nugarchforecast (and multiforecast) with specification object not available for mcsGARCH model")
}
} else{
mod = "X"
}
if( !is.null(cluster) ){
clusterEvalQ(cluster, library(rugarch))
clusterExport(cluster, c("multispec", "data", "n.ahead", "n.roll",
"out.sample", "external.forecasts"), envir = environment())
if(mod=="realGARCH"){
clusterExport(cluster, "realVol", envir = environment())
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts,
realizedVol = realVol[,i])
})
} else{
forecastlist = parLapply(cluster, as.list(1:n), fun = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts)
})
}
} else{
if(mod=="realGARCH"){
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts,
realizedVol = realVol[,i])
})
} else{
forecastlist = lapply(as.list(1:n), FUN = function(i){
ugarchforecast(fitORspec = multispec@spec[[i]],
data = data[, i, drop = FALSE], n.ahead = n.ahead, n.roll = n.roll,
out.sample = out.sample[i], external.forecasts = external.forecasts, ...)
})
}
}
desc = list()
desc$type = multispec@type
desc$asset.names = asset.names
ans = new("uGARCHmultiforecast",
forecast = forecastlist,
desc = desc)
return(ans)
}
Try the rugarch package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.