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R/rdcc-likelihoods.R
In rgarch: Flexible GARCH modelling in R
#################################################################################
##
## R package rgarch by Alexios Ghalanos Copyright (C) 2008, 2009, 2010, 2011
## This file is part of the R package rgarch.
##
## The R package rgarch 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 rgarch 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.
##
#################################################################################
normal.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
normal.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
normal.adccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
Nbarx = get("Nbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[2,3] == 1) dccg = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
student.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 2.001 ) shape = 2.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccstudentC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), nu = as.double(shape),
dccorder = as.numeric(dccorderx), dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
student.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 2.001 ) shape = 2.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccstudentC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.numeric(dccax), dccb = as.numeric(dccbx), nu = as.numeric(shape),
dccorder = as.numeric(dccorderx), dccsum = as.numeric(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
laplace.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 0 ) shape = 0.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dcclaplaceC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx),
dccorder = as.numeric(dccorderx), dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
laplace.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 0.001 ) shape = 0.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dcclaplaceC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.numeric(dccax), dccb = as.numeric(dccbx),
dccorder = as.numeric(dccorderx), dccsum = as.numeric(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
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#################################################################################
##
## R package rgarch by Alexios Ghalanos Copyright (C) 2008, 2009, 2010, 2011
## This file is part of the R package rgarch.
##
## The R package rgarch 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 rgarch 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.
##
#################################################################################
normal.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
normal.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
normal.adccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
Nbarx = get("Nbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[2,3] == 1) dccg = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccnormC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), dccorder = as.numeric(dccorderx),
dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
student.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 2.001 ) shape = 2.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccstudentC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx), nu = as.double(shape),
dccorder = as.numeric(dccorderx), dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
student.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 2.001 ) shape = 2.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dccstudentC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.numeric(dccax), dccb = as.numeric(dccbx), nu = as.numeric(shape),
dccorder = as.numeric(dccorderx), dccsum = as.numeric(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
laplace.dccLLH1 = function(pars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
Qbarx = get("Qbar", garchenv)
stdresidsx = get("stdresid", garchenv)
Hx = get("H", garchenv)
m = dim(data)[2]
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
T = dim(data)[1]
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(0, nrow = mo, ncol = m), stdresidsx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 0 ) shape = 0.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dcclaplaceC1", Qbar = Qbarx, Z = stdresidsx,
dcca = as.double(dccax), dccb = as.double(dccbx),
dccorder = as.numeric(dccorderx), dccsum = as.double(dccsumx))
llh = res[[3]]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
return(llh)
}
laplace.dccLLH2 = function(pars, garchpars, data, returnType = "llh", garchenv)
{
# prepare inputs
# rejoin fixed and pars
fixed = get("fixed", garchenv)
fixid = get("fixid", garchenv)
mspec = get("mspec", garchenv)
npar = get("npar", garchenv)
paridx = get("paridx", garchenv)
m = dim(data)[2]
T = dim(data)[1]
speclist = get("speclist", garchenv)
Hx = matrix(0, nrow = T, ncol = m)
paridxa = get("paridxa", garchenv)
resids = matrix(0, nrow = T, ncol = m)
# simulate new H with which to standardize the dataset
for(i in 1:m){
specx = ugarchspec(mean.model = list(
armaOrder = speclist@spec[[i]]@mean.model$armaOrder,
include.mean = speclist@spec[[i]]@mean.model$include.mean,
garchInMean = speclist@spec[[i]]@mean.model$im,
inMeanType = speclist@spec[[i]]@mean.model$inMeanType,
arfima = speclist@spec[[i]]@mean.model$arfima,
external.regressors = speclist@spec[[i]]@mean.model$mexdata),
variance.model = list(
model = speclist@spec[[i]]@variance.model$model,
garchOrder = speclist@spec[[i]]@variance.model$garchOrder,
submodel = speclist@spec[[i]]@variance.model$submodel,
external.regressors = speclist@spec[[i]]@variance.model$vexdata),
distribution.model = speclist@spec[[i]]@distribution.model$distribution)
fpars = garchpars[paridxa[i,1]:paridxa[i,2]]
names(fpars) = specx@optimization.model$modelnames
specx@optimization.model$fixed.pars = as.list(fpars)
flt = ugarchfilter(spec = specx, data = data[,i])
Hx[, i] = sigma(flt)
resids[,i] = residuals(flt)
}
stdresidsx = resids/Hx
Qbarx = cov(stdresidsx)
if(!is.null(fixed)){
pall = vector(mode = "numeric", length = npar)
pall[fixid] = fixed
pall[-fixid] = pars
pars = pall
}
omodel = get("omodel", garchenv)
fit.control = get("fit.control", garchenv)
mo = omodel$maxOrder
N = c(mo, T)
dccOrder = mspec$dccOrder
pos = omodel$pos.matrix
stdresidsx = rbind( matrix(1, nrow = mo, ncol = m), stdresidsx )
Hx = rbind( matrix(0, nrow = mo, ncol = m), Hx )
dcca = dccb = skew = shape = 0
if(pos[1,3] == 1) dcca = pars[pos[1,1]:pos[1,2]]
if(pos[2,3] == 1) dccb = pars[pos[2,1]:pos[2,2]]
if(pos[3,3] == 1) skew = pars[pos[3,1]:pos[3,2]]
if(pos[4,3] == 1) shape = pars[pos[4,1]:pos[4,2]]
if( shape < 0.001 ) shape = 0.001
dccsum = sum(dcca) + sum(dccb)
dccax = dcca
dccbx = dccb
dccorderx = c(dccOrder, mo)
dccsumx = sum(dcca) + sum(dccb)
if( fit.control$stationarity ){
if( !is.na(dccsumx) && dccsumx >= 1 ) return(llh = get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
res = .Call(name = "dcclaplaceC2", Qbar = Qbarx, H = Hx, Z = stdresidsx,
dcca = as.numeric(dccax), dccb = as.numeric(dccbx),
dccorder = as.numeric(dccorderx), dccsum = as.numeric(dccsumx))
Qtout = res[[1]]
likelihoods = res[[2]]
llh = res[[3]]
Rtout = res[[4]]
Qtout = Qtout[(mo+1):(mo+T)]
Rtout = Rtout[(mo+1):(mo+T)]
likelihoods = likelihoods[(mo+1):(mo+T)]
if(is.finite(llh) | !is.na(llh) | !is.nan(llh)){
assign(".llh", llh, envir = garchenv)
} else {
llh = (get(".llh", garchenv) + 0.1*(abs(get(".llh", garchenv))))
}
ans = switch(returnType,
lik = likelihoods,
llh = llh,
all = list(lik = likelihoods, llh = llh, Rt = Rtout, Qt = Qtout))
return(ans)
}
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