Nothing
R/copula-main.R
In rmgarch: Multivariate GARCH Models
Defines functions
.fullinc2
.cgarchsim2
.cgarchsim1
.cgarchsim
.cgarchfilter.dynamic
.cgarchfilter.static
.cgarchfilter
.cgarchfit.static
.cgarchfit.dynamic
.cgarchfit
.cgarchspec
#################################################################################
##
## R package rmgarch by Alexios Galanos Copyright (C) 2008-2022.
## This file is part of the R package rmgarch.
##
## The R package rmgarch 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 rmgarch 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.
##
#################################################################################
#################################################################################
# Fit, Filter, Forecast and Simulation
#-------------------------------------
.cgarchspec = function(uspec, VAR = FALSE, robust = FALSE, lag = 1, lag.max = NULL,
lag.criterion = c("AIC", "HQ", "SC", "FPE"),
external.regressors = NULL, robust.control = list("gamma" = 0.25,
"delta" = 0.01, "nc" = 10, "ns" = 500), dccOrder = c(1,1),
asymmetric = FALSE,
distribution.model = list(copula = c("mvnorm", "mvt"),
method = c("Kendall", "ML"), time.varying = FALSE,
transformation = c("parametric", "empirical", "spd")),
start.pars = list(), fixed.pars = list())
{
.eps = .Machine$double.eps
VAR.opt = list()
if(is.null(VAR)){
VAR.opt$VAR = FALSE
} else{
VAR.opt$VAR = as.logical(VAR)
}
if(is.null(robust)){
VAR.opt$robust = FALSE
} else{
VAR.opt$robust = as.logical(robust)
}
if(is.null(lag)){
VAR.opt$lag = 1
} else{
VAR.opt$lag = as.integer(lag)
}
if(is.null(lag.max)){
VAR.opt$lag.max = NULL
} else{
VAR.opt$lag.max = as.integer(min(1, lag.max))
}
if(is.null(lag.criterion)){
VAR.opt$lag.criterion = "AIC"
} else{
VAR.opt$lag.criterion = lag.criterion[1]
}
if(is.null(external.regressors)){
VAR.opt$external.regressors = NULL
} else{
VAR.opt$external.regressors = external.regressors
}
rc = list("gamma" = 0.25, "delta" = 0.01, "nc" = 10, "ns" = 500)
rcmatch = match(names(robust.control), c("gamma", "delta", "nc", "ns"))
if(length(rcmatch[!is.na(rcmatch)]) > 0){
rx = which(!is.na(rcmatch))
rc[rcmatch[!is.na(rcmatch)]] = robust.control[rx]
}
VAR.opt$robust.control = rc
modeldata = list()
modeldesc = list()
m = length(uspec@spec)
modelinc = rep(0, 11)
names(modelinc) = c("var", "mvmxreg", "C", "dcca", "dccb", "dccg", "mshape",
"mskew", "aux", "aux", "aux")
if(is.null(distribution.model$copula)){
distribution = "mvnorm"
} else{
distribution = tolower(distribution.model$copula)
}
distribution = distribution[1]
valid.distributions = c("mvnorm", "mvt")
if(!any(distribution == valid.distributions))
stop("\nInvalid Copula Distribution Choice\n", call. = FALSE)
modeldesc$distribution = distribution
if(distribution == "mvt") modelinc[7] = 1
if(is.null(distribution.model$method)){
method = "kendall"
} else{
method = tolower(distribution.model$method)
}
method = method[1]
valid.methods = c("kendall", "ml")
if(!any(method == valid.methods)){
if(distribution == "mvt")
warning("\nInvalid Rho Method Estimation Choice\n", call. = FALSE)
method = "kendall"
}
modeldesc$cor.method = toupper(method)
if(is.null(distribution.model$time.varying)){
timecopula = FALSE
} else{
timecopula = as.logical(distribution.model$time.varying)
}
timecopula = timecopula[1]
modeldesc$timecopula = timecopula
if(!timecopula && tolower(method[1]) == "ml") modelinc[3] = ((m*m - m)/2)
if(is.null(distribution.model$transformation)){
transformation = "parametric"
} else{
transformation = tolower(distribution.model$transformation)
}
transformation = transformation[1]
valid.transformations = c("parametric", "empirical", "spd")
if(!any(transformation == valid.transformations))
stop("\nInvalid Copula Transformation Choice\n", call. = FALSE)
modeldesc$transformation = transformation
if(timecopula){
if(is.null(dccOrder)){
modelinc[4:5] = 1
} else{
modelinc[4] = as.integer(dccOrder[1])
modelinc[5] = as.integer(dccOrder[2])
}
if(is.null(asymmetric)){
modelinc[6] = 0
} else{
if(as.logical(asymmetric)) modelinc[6] = modelinc[4]
}
} else{
modelinc[4:6] = 0
}
if( VAR ){
if(is.null(VAR.opt$lag)){
modelinc[1] = 1
} else{
modelinc[1] = as.integer( VAR.opt$lag )
}
if(!is.null(VAR.opt$external.regressors)){
if(!is.matrix(VAR.opt$external.regressors))
stop("\nexternal.regressors must be a matrix.")
modelinc[2] = dim(VAR.opt$external.regressors)[2]
modeldata$mexdata = VAR.opt$external.regressors
} else{
modeldata$mexdata = NULL
}
}
varmodel = list()
umodel = vector(mode ="list")
if( modelinc[1]>0 ){
varmodel$robust = VAR.opt$robust
varmodel$lag.max = VAR.opt$lag.max
varmodel$lag.criterion = VAR.opt$lag.criterion
varmodel$robust.control = VAR.opt$robust.control
umodel$modelinc = matrix(0, ncol = m, nrow = 21)
rownames(umodel$modelinc) = names(uspec@spec[[1]]@model$modelinc[1:21])
umodel$modeldesc = list()
umodel$vt = sapply(uspec@spec, FUN = function(x) x@model$modelinc[22])
umodel$modeldesc$vmodel = vector(mode = "character", length = m)
umodel$modeldesc$vsubmodel = vector(mode = "character", length = m)
umodel$start.pars = umodel$fixed.pars = vector(mode = "list", length = m)
umodel$modeldesc$distribution = vector(mode = "character", length = m)
umodel$modeldata = list()
umodel$modeldata$vexdata = vector(mode = "list", length = m)
for(i in 1:m){
# zero the mean equation since we are using VAR
umodel$modeldesc$vmodel[i] = uspec@spec[[i]]@model$modeldesc$vmodel
umodel$modeldesc$vsubmodel[i] = ifelse(is.null(uspec@spec[[i]]@model$modeldesc$vsubmodel),
"GARCH", uspec@spec[[i]]@model$modeldesc$vsubmodel)
umodel$modeldesc$distribution[i] = uspec@spec[[i]]@model$modeldesc$distribution
umodel$modelinc[,i] = uspec@spec[[i]]@model$modelinc[1:21]
umodel$modelinc[1:6,i] = 0
umodel$modeldata$vexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$vexdata)) NA else uspec@spec[[i]]@model$modeldata$vexdata
umodel$start.pars[[i]] = if(is.null(uspec@spec[[i]]@model$start.pars)) NA else uspec@spec[[i]]@model$start.pars
umodel$fixed.pars[[i]] = if(is.null(uspec@spec[[i]]@model$fixed.pars)) NA else uspec@spec[[i]]@model$fixed.pars
umodel$modeldata$mexdata[[i]] = NA
}
} else{
varmodel$lag.max = 1
varmodel$lag.criterion = "HQ"
umodel$modelinc = matrix(0, ncol = m, nrow = 21)
rownames(umodel$modelinc) = names(uspec@spec[[1]]@model$modelinc[1:21])
umodel$modeldesc = list()
# variance targeting custom
umodel$vt = sapply(uspec@spec, FUN = function(x) x@model$modelinc[22])
umodel$modeldesc$vmodel = vector(mode = "character", length = m)
umodel$modeldesc$vsubmodel = vector(mode = "character", length = m)
umodel$start.pars = umodel$fixed.pars = vector(mode = "list", length = m)
umodel$modeldesc$distribution = vector(mode = "character", length = m)
umodel$modeldata = list()
umodel$modeldata$mexdata = vector(mode = "list", length = m)
umodel$modeldata$vexdata = vector(mode = "list", length = m)
for(i in 1:m){
umodel$modeldesc$vmodel[i] = uspec@spec[[i]]@model$modeldesc$vmodel
umodel$modeldesc$vsubmodel[i] = ifelse(is.null(uspec@spec[[i]]@model$modeldesc$vsubmodel),"GARCH",uspec@spec[[i]]@model$modeldesc$vsubmodel)
umodel$modeldesc$distribution[i] = uspec@spec[[i]]@model$modeldesc$distribution
umodel$modelinc[,i] = uspec@spec[[i]]@model$modelinc[1:21]
umodel$modeldata$mexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$mexdata)) NA else uspec@spec[[i]]@model$modeldata$mexdata
umodel$modeldata$vexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$vexdata)) NA else uspec@spec[[i]]@model$modeldata$vexdata
umodel$start.pars[[i]] = if(is.null(uspec@spec[[i]]@model$start.pars)) NA else uspec@spec[[i]]@model$start.pars
umodel$fixed.pars[[i]] = if(is.null(uspec@spec[[i]]@model$fixed.pars)) NA else uspec@spec[[i]]@model$fixed.pars
}
}
modelinc[11] = which(c("mvnorm", "mvt") == distribution)
maxdccOrder = max(dccOrder)
maxgarchOrder = max( sapply(uspec@spec, FUN = function(x) max(max(x@model$modelinc[2:3]), max(x@model$modelinc[8:9]) ) ) )
maxOrder = max( maxdccOrder, maxgarchOrder )
if(modelinc[1]>0){
maxgarchOrder = max(c(maxgarchOrder, modelinc[1]))
}
modeldesc$dccmodel = ifelse(modelinc[6]>0, "ADCC", "DCC")
pars = matrix(0, ncol = 6, nrow = 6)
colnames(pars) = c("Level", "Fixed", "Include", "Estimate", "LB", "UB")
pidx = matrix(NA, nrow = 6, ncol = 2)
colnames(pidx) = c("begin", "end")
rownames(pidx) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
pos = 1
pos.matrix = matrix(0, ncol = 3, nrow = 6)
colnames(pos.matrix) = c("start", "stop", "include")
rownames(pos.matrix) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
for(i in 1:6){
if( modelinc[2+i] > 0 ){
pos.matrix[i,1:3] = c(pos, pos+modelinc[2+i]-1, 1)
pos = max(pos.matrix[1:i,2]+1)
}
}
mm = sum(modelinc[3:8])
mm = mm - length( which(modelinc[c(3:8)]>0) )
pars = matrix(0, ncol = 6, nrow = 6 + mm)
colnames(pars) = c("Level", "Fixed", "Include", "Estimate", "LB", "UB")
pidx = matrix(NA, nrow = 6, ncol = 2)
colnames(pidx) = c("begin", "end")
rownames(pidx) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
fixed.names = names(fixed.pars)
start.names = names(start.pars)
fixed.pars = unlist(fixed.pars)
start.pars = unlist(start.pars)
pnames = NULL
nx = 0
pn = 1
pidx[1,1] = 1
if(pos.matrix[1,3] == 1){
cvm = 1:((m*m - m)/2)
pn = length( seq(pos.matrix[1,1], pos.matrix[1,2], by = 1) )
for(i in 1:pn){
nnx = paste("C", cvm[i], sep="")
pars[(nx+i), 1] = 0
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = -0.99
pars[(nx+i), 6] = 0.99
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "C")
}
pidx[1,2] = pn
nx = pn
pn = 1
pidx[2,1] = nx+1
if(pos.matrix[2,3] == 1){
pn = length( seq(pos.matrix[2,1], pos.matrix[2,2], by = 1) )
for(i in 1:pn){
nnx = paste("dcca", i, sep="")
pars[(nx+i), 1] = 0.05/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dcca")
}
pidx[2,2] = nx+pn
nx = nx + pn
pn = 1
pidx[3,1] = nx+1
if(pos.matrix[3,3] == 1){
pn = length( seq(pos.matrix[3,1], pos.matrix[3,2], by = 1) )
for(i in 1:pn){
nnx = paste("dccb", i, sep="")
pars[(nx+i), 1] = 0.9/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dccb")
}
pidx[3,2] = nx+pn
nx = nx + pn
pn = 1
pidx[4,1] = nx+1
if(pos.matrix[4,3] == 1){
pn = length( seq(pos.matrix[4,1], pos.matrix[4,2], by = 1) )
for(i in 1:pn){
nnx = paste("dccg", i, sep="")
pars[(nx+i), 1] = 0.05/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dccg")
}
pidx[4,2] = nx+pn
nx = nx + pn
pn = 1
pidx[5,1] = nx+1
if(modelinc[7]<=1){
if(pos.matrix[5,3]==1){
pars[nx+pn, 3] = 1
pars[nx+pn, 1] = 5
pars[(nx+pn), 5] = 4
pars[(nx+pn), 6] = 50
if(any(substr(start.names, 1, 6) == "mshape")) pars[nx+pn, 1] = start.pars["mshape"]
if(any(substr(fixed.names, 1, 6) == "mshape")){
pars[nx+pn,2] = 1
pars[nx+pn, 1] = fixed.pars["mshape"]
} else{
pars[nx+pn,4] = 1
}
}
pnames = c(pnames, "mshape")
} else{
if(pos.matrix[5,3] == 1){
pn = length( seq(pos.matrix[5,1], pos.matrix[5,2], by = 1) )
for(i in 1:pn){
nnx = paste("mshape", i, sep="")
pars[(nx+i), 1] = 5
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = 4
pars[(nx+i), 6] = 50
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "mshape")
}
}
pidx[5,2] = nx+pn
nx = nx + pn
pn = 1
pidx[6,1] = nx+1
if(modelinc[8]<=1){
if(pos.matrix[6,3]==1){
pars[nx+pn, 3] = 1
pars[nx+pn, 1] = 0.5
pars[(nx+pn), 5] = -1
pars[(nx+pn), 6] = 1
if(any(substr(start.names, 1, 5) == "mskew")) pars[nx+pn, 1] = start.pars["mskew"]
if(any(substr(fixed.names, 1, 5) == "mskew")){
pars[nx+pn,2] = 1
pars[nx+pn, 1] = fixed.pars["mskew"]
} else{
pars[nx+pn,4] = 1
}
}
pnames = c(pnames, "mskew")
} else{
if(pos.matrix[6,3] == 1){
pn = length( seq(pos.matrix[6,1], pos.matrix[6,2], by = 1) )
for(i in 1:pn){
nnx = paste("mskew", i, sep="")
pars[(nx+i), 1] = 3
pars[(nx+i), 5] = -1
pars[(nx+i), 6] = 1
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "mskew")
}
}
pidx[6,2] = nx+pn
rownames(pars) = pnames
model = list(modelinc = modelinc, modeldesc = modeldesc, modeldata = modeldata,
varmodel = varmodel, pars = pars, start.pars = start.pars,
fixed.pars = fixed.pars, maxgarchOrder = maxgarchOrder,
maxdccOrder = maxdccOrder, pos.matrix = pos.matrix, pidx = pidx)
ans = new("cGARCHspec",
model = model,
umodel = umodel)
return(ans)
}
.cgarchfit = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL, fit = NULL,
VAR.fit = NULL, realizedVol = NULL, ...)
{
type = ifelse(spec@model$modeldesc$timecopula, "dynamic", "static")
ans = switch(type,
dynamic = .cgarchfit.dynamic(spec = spec, data = data, spd.control = spd.control,
fit.control = fit.control, solver = solver, solver.control = solver.control,
out.sample = out.sample, cluster = cluster, fit = fit, VAR.fit = VAR.fit,
realizedVol = realizedVol, ...),
static = .cgarchfit.static(spec = spec, data = data, spd.control = spd.control,
fit.control = fit.control, solver = solver, solver.control = solver.control,
out.sample = out.sample, cluster = cluster, fit = fit, VAR.fit = VAR.fit,
realizedVol = realizedVol, ...))
return( ans )
}
.cgarchfit.dynamic = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL,
fit = NULL, VAR.fit = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
.eps = .Machine$double.eps
model = spec@model
umodel = spec@umodel
ufit.control = list()
if(is.null(fit.control$stationarity)){
ufit.control$stationarity = TRUE
} else {
ufit.control$stationarity = fit.control$stationarity
fit.control$stationarity = NULL
}
if(is.null(fit.control$scale)){
ufit.control$scale = TRUE
} else{
ufit.control$scale = fit.control$scale
fit.control$scale = NULL
}
if(is.null(fit.control$eval.se)) fit.control$eval.se = TRUE
if(length(solver)==2){
garch.solver = solver[1]
solver = solver[2]
} else{
garch.solver = solver[1]
}
solver = match.arg(tolower(solver)[1], c("solnp", "nlminb", "lbfgs","gosolnp"))
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfit-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfit-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = NROW(xdata$data)
if( (n-n.start) < 100)
stop("\ndccfit-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( model$modelinc[1]>0 ){
tmp = mvmean.varfit(model = model, data = data, VAR.fit = VAR.fit, T = T,
out.sample = out.sample, cluster = cluster)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
varcoef = tmp$varcoef
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
# Univariate GARCH fit
# create a multispec list, check for variance targeting (vt)
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
# Check if a pre-fitted uGARCHmultifit object provided, else estimate
if( !is.null(fit) && is(fit, "uGARCHmultifit") ){
# check VAR and fit:
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(fit@fit[[i]]@model$modelinc[1:6])>0)
stop("\nThe user supplied fit object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
fitlist = fit
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
} else{
fitlist = multifit(multispec = mspec, data = xts(zdata, index), out.sample = n.start,
solver = garch.solver, solver.control = solver.control,
fit.control = ufit.control, cluster = cluster, realizedVol = realizedVol, ...)
converge = sapply(fitlist@fit, FUN = function(x) x@fit$convergence)
if( any( converge == 1 ) ){
pr = which(converge != 1)
cat("\nNon-Converged:\n")
print(pr)
cat("\ndccfit-->error: convergence problem in univariate fit...")
cat("\n...returning uGARCHmultifit object instead...check and resubmit...")
return( fitlist )
}
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
}
stdresid = res/sig
#-----------------------------------------------------------------------------------
# Create the Full Model Pars and Indices
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
# This is the estimated parameter index
eidx = .estindfn(midx, mspec, model$pars)
unipars = lapply(fitlist@fit, FUN = function(x) x@fit$ipars[x@fit$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
LB = ipars[,5]
UB = ipars[,6]
estidx = as.logical( ipars[,4] )
npars = sum(estidx)
# Transformation of z to [0,1] domain
ans = switch(transformation,
parametric = .pparametric(fitlist, stdresid),
empirical = .pempirical(stdresid),
spd = .pspd(stdresid, spd.control))
if(transformation == "spd"){
ures = ans$ures
sfit = ans$sfit
} else{
ures = ans
sfit = NULL
}
# make small tail adjustments in order to avoid problem with the quantile
# functions in optimization
if(any(ures > 0.99999)){
xn = which(ures > 0.99999)
ures[xn] = 0.99999
}
if(any(ures < .eps)){
xn = which(ures < (1.5*.eps))
ures[xn] = .eps
}
model$spd.control = spd.control
mgarchenv = new.env(hash = TRUE)
arglist = list()
arglist$mgarchenv = mgarchenv
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$eval.se = fit.control$eval.se
arglist$solver = solver
arglist$fit.control = fit.control
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$fitlist = fitlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$dccN = npars
arglist$stdresid = stdresid
arglist$ures = ures
if(any(ipars[,2]==1)){
if(npars == 0){
xspex = spec
for(i in 1:m) xspex@umodel$fixed.pars[[i]] = as.list(fitlist@fit[[i]]@model$pars[fitlist@fit[[i]]@model$pars[,3]==1,1])
return(cgarchfilter(spec = xspex, data = xts(data, index), out.sample = out.sample,
filter.control = list(), spd.control = spd.control,
cluster = cluster, VAR.fit = VAR.fit, realizedVol = realizedVol))
} else{
# with some parameters fixed we extract them (to be rejoined at end)
# so that they do not enter the solver
use.solver = 1
}
} else{
use.solver = 1
}
# start counter
assign("rmgarch_llh", 1, envir = mgarchenv)
ILB = 0
IUB = 1
# Asymmetric Spec has different constraints
if(model$modelinc[6]> 0) Ifn = copula.adcccon else Ifn = copula.dcccon
if( solver == "solnp" | solver == "gosolnp" ) fit.control$stationarity = FALSE else fit.control$stationarity = TRUE
arglist$fit.control = fit.control
# get
if( use.solver )
{
arglist$returnType = "llh"
solution = switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.tvnormalLLH1, Ifn, ILB, IUB, gr = NULL,
hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.tvstudentLLH1, Ifn, ILB, IUB, gr = NULL,
hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist))
sol = solution$sol
hess = solution$hess
timer = Sys.time()-tic
convergence = sol$convergence
mpars[which(eidx[,(m+1)]==1, arr.ind = TRUE),m+1] = sol$pars
ipars[estidx, 1] = sol$pars
arglist$mpars = mpars
arglist$ipars = ipars
} else{
hess = NULL
timer = Sys.time()-tic
convergence = 1
sol = list()
sol$message = "all parameters fixed"
}
# add some tail data of the univariate fits to be used with simulation
# (we assume that noone is going to add more than 50 lags in a univariate fit!
mfit = list()
if( convergence == 0 ){
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.tvnormalLLH2(mpars[which(eidx==1, arr.ind = TRUE)],
arglist = arglist),
mvt = copula.tvstudentLLH2(mpars[which(eidx==1, arr.ind = TRUE)],
arglist = arglist))
mfit$tailusigma = tail(sigma(fitlist), 50)
mfit$tailuresids = tail(residuals(fitlist), 50)
mfit$tailuret = tail(data, 50)
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel1(f = copula.tvnormalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.tvnormalLLH2"),
mvt = .cgarchmakefitmodel1(f = copula.tvstudentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.tvstudentLLH2"))
mfit = c(mfit, nderiv)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
} else{
mfit$convergence = 1
}
mfit$realizedVol = realizedVol
mfit$timer = Sys.time() - tic
# Add the spd fit as this is needed for simulation
model$sfit = sfit
rm(mgarchenv)
ans = new("cGARCHfit",
mfit = mfit,
model = model)
return(ans)
}
.cgarchfit.static = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL,
fit = NULL, VAR.fit = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
.eps = .Machine$double.eps
model = spec@model
umodel = spec@umodel
ufit.control = list()
if(is.null(fit.control$stationarity)){
ufit.control$stationarity = TRUE
} else {
ufit.control$stationarity = fit.control$stationarity
fit.control$stationarity = NULL
}
if(is.null(fit.control$scale)){
ufit.control$scale = TRUE
} else{
ufit.control$scale = fit.control$scale
fit.control$scale = NULL
}
if(is.null(fit.control$eval.se)) fit.control$eval.se = TRUE
if(length(solver)==2){
garch.solver = solver[1]
solver = solver[2]
} else{
garch.solver = solver[1]
}
solver = match.arg(tolower(solver)[1], c("solnp", "nlminb", "lbfgs","gosolnp"))
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfit-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfit-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfit-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( model$modelinc[1]>0 ){
tmp = mvmean.varfit(model = model, data = data, VAR.fit = VAR.fit, T = T,
out.sample = out.sample, cluster = cluster)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
varcoef = tmp$varcoef
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
# Univariate GARCH fit (only subtract the ARMA order)
# create a multispec list
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
if( !is.null(fit) && is(fit, "uGARCHmultifit") ){
# check VAR and fit:
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(fit@fit[[i]]@model$modelinc[1:6])>0)
stop("\nThe user supplied fit object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
fitlist = fit
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
} else{
fitlist = multifit(multispec = mspec, data = xts(zdata, index), out.sample = n.start,
solver = garch.solver, solver.control = solver.control,
fit.control = ufit.control, cluster = cluster, realizedVol = realizedVol, ...)
converge = sapply(fitlist@fit, FUN = function(x) x@fit$convergence)
if( any( converge == 1 ) ){
pr = which(converge != 1)
cat("\nNon-Converged:\n")
print(pr)
cat("\ndccfit-->error: convergence problem in univariate fit...")
cat("\n...returning uGARCHmultifit object instead...check and resubmit...")
return( fitlist )
}
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
}
stdresid = res/sig
#-----------------------------------------------------------------------------------
# Create the Full Model Pars and Indices
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
# This is the estimated parameter index
eidx = .estindfn(midx, mspec, model$pars)
unipars = lapply(fitlist@fit, FUN = function(x) x@fit$ipars[x@fit$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
ipars = model$pars
LB = ipars[,5]
UB = ipars[,6]
estidx = as.logical( ipars[,4] )
npars = sum(estidx)
# Transformation of z to [0,1] domain
ans = switch(transformation,
parametric = .pparametric(fitlist, stdresid),
empirical = .pempirical(stdresid),
spd = .pspd(stdresid, spd.control))
if(transformation == "spd"){
ures = ans$ures
sfit = ans$sfit
} else{
ures = ans
sfit = NULL
}
# make small tail adjustments in order to avoid problem with the quantile
# functions in optimization
if(any(ures > 0.99999)){
xn = which(ures > 0.99999)
ures[xn] = 0.99999
}
if(any(ures < .eps)){
xn = which(ures < (1.5*.eps))
ures[xn] = .eps
}
model$spd.control = spd.control
mgarchenv = new.env(hash = TRUE)
arglist = list()
arglist$mgarchenv = mgarchenv
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$eval.se = fit.control$eval.se
arglist$solver = solver
arglist$fit.control = fit.control
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$fitlist = fitlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$ures = ures
arglist$npars = npars
if(any(ipars[,2]==1)){
if(npars == 0){
use.solver = 0
} else{
# with some parameters fixed we extract them (to be rejoined at end)
# so that they do not enter the solver
use.solver = 1
}
} else{
use.solver = 1
}
# start counter
# get
if( use.solver )
{
arglist$returnType = "llh"
solution = try(
switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.normalLLH1, Ifn = NULL, ILB = NULL, IUB = NULL,
gr = NULL, hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.studentLLH1, Ifn = NULL, ILB = NULL, IUB = NULL,
gr = NULL, hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist) ), silent = TRUE)
if(inherits(solution, "try-error") | solution$sol$convergence == 1){
solver = "lbfgs"
solver.control = list(trace = 1)
solution = switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.normalLLH1, Ifn = NULL, ILB = NULL,
IUB = NULL, gr = NULL, hessian = NULL,
control = solver.control, LB = ipars[estidx, 5],
UB = ipars[estidx, 6], arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.studentLLH1, Ifn = NULL, ILB = NULL,
IUB = NULL, gr = NULL, hessian = NULL,
control = solver.control, LB = ipars[estidx, 5],
UB = ipars[estidx, 6], arglist = arglist))
}
sol = solution$sol
hess = solution$hess
timer = Sys.time()-tic
convergence = sol$convergence
mpars[which(eidx[,(m+1)]==1, arr.ind = TRUE),m+1] = sol$pars
ipars[estidx, 1] = sol$pars
arglist$mpars = mpars
arglist$ipars = ipars
} else{
hess = NULL
timer = Sys.time()-tic
convergence = 1
sol = list()
sol$message = "all parameters fixed"
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist),
mvt = copula.studentLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist))
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel2(f = copula.normalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.normalLLH2"),
mvt = .cgarchmakefitmodel2(f = copula.studentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.studentLLH2"))
mfit = c(mfit, nderiv)
mfit$tailusigma = tail(sigma(fitlist), 25)
mfit$tailuresids = tail(residuals(fitlist), 25)
mfit$tailuret = tail(data, 25)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
}
# add some tail data of the univariate fits to be used with simulation
# (we assume that noone is going to add more than 50 lags in a univariate fit!
if( convergence == 0 ){
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist),
mvt = copula.studentLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist))
mfit$tailusigma = tail(sigma(fitlist), 50)
mfit$tailuresids = tail(residuals(fitlist), 50)
mfit$tailuret = tail(data, 50)
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel2(f = copula.normalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.normalLLH2"),
mvt = .cgarchmakefitmodel2(f = copula.studentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.studentLLH2"))
mfit = c(mfit, nderiv)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfit$timer = Sys.time() - tic
} else{
mfit$convergence = 1
mfit$timer = Sys.time() - tic
}
model$sfit = sfit
mfit$realizedVol = realizedVol
ans = new("cGARCHfit",
mfit = mfit,
model = model)
return(ans)
}
.cgarchfilter = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
type = ifelse(spec@model$modeldesc$timecopula, "dynamic", "static")
ans = switch(type,
dynamic = .cgarchfilter.dynamic(spec = spec, data = data, out.sample = out.sample,
filter.control = filter.control, spd.control = spd.control,
cluster = cluster, varcoef = varcoef, realizedVol = realizedVol, ...),
static = .cgarchfilter.static(spec = spec, data = data, out.sample = out.sample,
filter.control = filter.control, spd.control = spd.control,
cluster = cluster, varcoef = varcoef, realizedVol = realizedVol, ...))
return( ans )
}
.cgarchfilter.static = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
model = spec@model
umodel = spec@umodel
n.old = filter.control$n.old
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfilter-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfilter-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfilter-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# VAR model
if( spec@model$modelinc[1]>0 ){
tmp = mvmean.varfilter(model = model, data = data, varcoef = varcoef,
T = T, out.sample = out.sample)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# Univariate GARCH filter
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(umodel$modelinc[1:6,i])>0)
stop("\nThe user supplied univariate spec object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
filterlist = multifilter(multifitORspec = mspec, data = xts(zdata, index), out.sample = out.sample,
cluster = cluster, n.old = n.old, realizedVol = realizedVol, ...)
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(filterlist)
model$residuals = res = residuals(filterlist)
model$sigma = sig = sigma(filterlist)
stdresid = res/sig
N = dim(stdresid)[1]
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
eidx = midx
unipars = sapply(filterlist@filter, FUN = function(x) x@filter$ipars[x@filter$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1, arr.ind = TRUE), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
estidx = as.logical( ipars[,3] )
npars = sum(estidx)
model$spd.control = spd.control
arglist = list()
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$filterlist = filterlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$npars = npars
arglist$n.old = n.old
arglist$filter.control = filter.control
mfilter = list()
arglist$returnType = "ALL"
mfilter = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH3(arglist = arglist),
mvt = copula.studentLLH3(arglist = arglist))
mfilter$tailusigma = tail(sigma(filterlist), 50)
mfilter$tailuresids = tail(residuals(filterlist), 50)
mfilter$tailuret = tail(data, 50)
Rt = mfilter$R
Ht = array( 0, dim = c(m, m, N) )
stdresid = matrix(0, nrow = N, ncol = m)
if( !is.null(cluster) ){
clusterExport(cluster, c("sig", "Rt", "res"), envir = environment())
tmp = parLapply(cluster, as.list(1:N), fun = function(i){
tmph = diag( sig[i, ] ) %*% Rt %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
} else{
tmp = lapply(as.list(1:N), FUN = function(i){
tmph = diag( sig[i, ] ) %*% Rt %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
}
mfilter$H = Ht
allnames = NULL
for(i in 1:m){
allnames = c(allnames, paste("[",cnames[i],"].", rownames(midx[midx[,i]==1,i, drop = FALSE]), sep = ""))
}
garchnames = allnames
dccnames = rownames(midx[midx[,m+1]==1,m+1, drop = FALSE])
if(!is.null(dccnames)){
allnames = c(garchnames, paste("[Joint]", rownames(midx[midx[,m+1]==1,m+1, drop = FALSE]), sep = ""))
} else{
allnames = garchnames
}
mfilter$coef = mpars[which(midx==1, arr.ind = TRUE)]
names(mfilter$coef) = allnames
mfilter$garchnames = garchnames
mfilter$dccnames = dccnames
mfilter$stdresid = stdresid
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfilter$realizedVol = realizedVol
mfilter$timer = Sys.time() - tic
#model$sfit = sfit
ans = new("cGARCHfilter",
mfilter = mfilter,
model = model)
return(ans)
}
# ToDo : change the likelihood to take into account the n.old argument for the cov calculation.
.cgarchfilter.dynamic = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
model = spec@model
umodel = spec@umodel
n.old = filter.control$n.old
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfilter-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfilter-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfilter-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( spec@model$modelinc[1]>0 ){
tmp = mvmean.varfilter(model = model, data = data, varcoef = varcoef,
T = T, out.sample = out.sample)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# Univariate GARCH filter
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(umodel$modelinc[1:6,i])>0)
stop("\nThe user supplied univariate spec object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
filterlist = multifilter(multifitORspec = mspec, data = xts(zdata, index), out.sample = out.sample,
cluster = cluster, n.old = n.old, realizedVol = realizedVol, ...)
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(filterlist)
model$residuals = res = residuals(filterlist)
model$sigma = sig = sigma(filterlist)
stdresid = res/sig
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
eidx = midx
unipars = sapply(filterlist@filter, FUN = function(x) x@filter$ipars[x@filter$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1, arr.ind = TRUE), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
estidx = as.logical( ipars[,3] )
npars = sum(estidx)
model$spd.control = spd.control
arglist = list()
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$zdata = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$filterlist = filterlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$npars = npars
arglist$filter.control = filter.control
arglist$n.old = n.old
mfilter = list()
arglist$returnType = "ALL"
mfilter = switch(model$modeldesc$distribution,
mvnorm = copula.tvnormalLLH3(arglist = arglist),
mvt = copula.tvstudentLLH3(arglist = arglist))
mfilter$tailusigma = tail(sigma(filterlist), 50)
mfilter$tailuresids = tail(residuals(filterlist), 50)
mfilter$tailuret = tail(data, 50)
N = dim(stdresid)[1]
Rt = mfilter$R
Ht = array( 0, dim = c(m, m, N) )
stdresid = matrix(0, nrow = N, ncol = m)
if( !is.null(cluster) ){
clusterExport(cluster, c("sig", "Rt", "res"), envir = environment())
tmp = parLapply(cluster, as.list(1:N), fun = function(i){
tmph = diag( sig[i, ] ) %*% Rt[[i]] %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
} else{
tmp = lapply(as.list(1:N), FUN = function(i){
tmph = diag( sig[i, ] ) %*% Rt[[i]] %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
}
mfilter$H = Ht
allnames = NULL
for(i in 1:m){
allnames = c(allnames, paste("[",cnames[i],"].", rownames(midx[midx[,i]==1,i, drop = FALSE]), sep = ""))
}
garchnames = allnames
dccnames = rownames(midx[midx[,m+1]==1,m+1, drop = FALSE])
allnames = c(garchnames, paste("[Joint]",dccnames, sep = ""))
mfilter$coef = mpars[which(midx==1, arr.ind = TRUE)]
names(mfilter$coef) = allnames
mfilter$garchnames = garchnames
mfilter$dccnames = dccnames
mfilter$stdresid = stdresid
# make model list to return some useful information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfilter$realizedVol = realizedVol
mfilter$timer = Sys.time() - tic
#model$sfit = sfit
ans = new("cGARCHfilter",
mfilter = mfilter,
model = model)
return(ans)
}
# allow returning only the scenario (return) matrix
.cgarchsim = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL,
preresiduals = NULL, prereturns = NULL, preR = NULL, preQ = NULL,
preZ = NULL, rseed = NULL, mexsimdata = NULL, vexsimdata = NULL,
cluster = NULL, only.density = FALSE, prerealized = NULL, ...)
{
timecopula = fit@model$modeldesc$timecopula
if(timecopula){
ans = .cgarchsim2(fit = fit, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
startMethod = startMethod[1], presigma = presigma, preresiduals = preresiduals,
prereturns = prereturns, preR = preR, preQ = preQ, preZ = preZ,
rseed = rseed, mexsimdata = mexsimdata, vexsimdata = vexsimdata,
cluster = cluster, only.density = only.density, prerealized = prerealized)
} else{
ans = .cgarchsim1(fit = fit, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
startMethod = startMethod[1], presigma = presigma, preresiduals = preresiduals,
prereturns = prereturns, preR = preR, rseed = rseed,
mexsimdata = mexsimdata, vexsimdata = vexsimdata, cluster = cluster,
only.density = only.density, prerealized = prerealized)
}
return(ans)
}
.cgarchsim1 = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), preresiduals = NULL,
presigma = NULL, prereturns = NULL, preR = NULL, rseed = NULL,
mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, only.density = FALSE,
prerealized = NULL, ...)
{
# first generate the copula random uniform numbers (static copula)
# ures --> transform --> zres
# pass zres to GARCH simulation
# pass if needed to VAR model
if( is.null(rseed) ){
rseed = as.integer(runif(m.sim, 1, Sys.time()))
} else {
if(length(rseed) == 1) rseed = c(rseed[1], rseed[1]+seq_len(m.sim))
rseed = as.integer( rseed[1:m.sim] )
}
model = fit@model
umodel = model$umodel
T = model$modeldata$T
Data = model$modeldata$data[1:T, ]
m = dim(Data)[2]
nsim = n.sim + n.start
mg = model$maxgarchOrder
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel,
umodel$modeldesc$vsubmodel, umodel$modeldata$mexdata,
umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, NULL)
for(i in 1:m){
# variance targeting case
if(model$midx["omega",i]==0){
setfixed(mspec@spec[[i]]) = as.list(c("omega"=model$mpars["omega",i], model$mpars[which(model$midx[,i]==1), i]))
} else{
setfixed(mspec@spec[[i]]) = as.list(model$mpars[which(model$midx[,i]==1), i])
}
}
#setfixed(specx)<-as.list(model$mpars[model$midx[,m+1]==1,m+1])
uncv = sapply(mspec@spec, FUN = function(x) uncvariance(x))
if( !is.null(presigma) ){
if( !is.matrix(presigma) )
stop("\ncgarchsim-->error: presigma must be a matrix.")
if( dim(presigma)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for presigma.")
if( dim(presigma)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for presigma (need ", mg, " rows.", sep = ""))
} else{
if(startMethod == "sample"){
presigma = fit@mfit$tailusigma
} else{
presigma = matrix(uncv, ncol = m, nrow = 25)
}
}
if(is.null(preR)){
Rbar = rcor(fit)
} else{
if(dim(preR)[1] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(dim(preR)[2] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(any(diag(preR) != 1))
stop("\ncgarchsim-->error: preR diagonals must be 1.\n")
Rbar = preR
}
if( !is.null(prereturns) ){
if( !is.matrix(prereturns) )
stop("\ncgarchsim-->error: prereturns must be a matrix.")
if( dim(prereturns)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prereturns.")
if( dim(prereturns)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prereturns (need ", mg, " rows.", sep = ""))
} else{
prereturns = tail(model$modeldata$data[1:T, ], 25)
}
if(fit@model$umodel$modeldesc$vmodel[1]=="realGARCH"){
if( !is.null(prerealized) ){
if( !is.matrix(prerealized) )
stop("\ncgarchsim-->error: prerealized must be a matrix.")
if( dim(prerealized)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prerealized.")
if( dim(prerealized)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prerealized (need ", mg, " rows.", sep = ""))
} else{
# might want to include the option for unconditional
# value of realized (unexported in rugarch)
prerealized = tail(fit@mfit$realizedVol[1:T,], 25)
}
} else{
prerealized = matrix(NA, ncol = m, nrow = 25)
}
ures = .sample.copula(model, Qbar = NULL, preQ = NULL, Rbar = Rbar,
Nbar = NULL, preZ = NULL, n.sim = n.sim, n.start = n.start,
m.sim = m.sim, rseed = rseed, cluster = cluster)
# ures = [0,1] copula random numbers
# now transform back into margins for use in garch
transformation = model$modeldesc$transformation
distu = umodel$modeldesc$distribution
zres = array(NA, dim = c(nsim, m, m.sim))
# parallel:
if( !is.null(cluster) ){
ssfit = model$sfit
minc = umodel$modelinc
mpars = model$mpars
sxres = fit@mfit$stdresid
clusterEvalQ(cluster, loadNamespace('rmgarch'))
clusterEvalQ(cluster, loadNamespace('rugarch'))
if(transformation == "spd"){
clusterExport(cluster, c("ures", "mpars", "minc", "m",
"sxres", "ssfit", "transformation"), envir = environment())
} else{
clusterExport(cluster, c("ures", "mpars", "minc", "m", "sxres",
"transformation"), envir = environment())
}
clusterExport(cluster, c(".qparametric", ".qempirical",".qspd"), envir = environment())
mtmp = parLapply(cluster, as.list(1:m.sim), fun = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures[,,i], ncol = m), pars = mpars, modelinc = minc),
empirical = .qempirical(matrix(ures[,,i], ncol = m), sxres),
spd = .qspd(matrix(ures[,,i], ncol = m), sfit = ssfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
} else{
mtmp = lapply(as.list(1:m.sim), FUN = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures[,,i], ncol = m),
pars = model$mpars, modelinc = umodel$modelinc),
empirical = .qempirical(matrix(ures[,,i], ncol = m), fit@mfit$stdresid),
spd = .qspd(matrix(ures[,,i], ncol = m), sfit = model$sfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
}
# Now we have the innovations which we feed back into the garch routine to simulate
simRes = simX = simR = simQ = simH = simSeries = vector(mode = "list", length = m.sim)
if( !is.null(cluster) ){
tailres = fit@mfit$tailuresids
clusterEvalQ(cluster, loadNamespace('rugarch'))
clusterExport(cluster, c("mspec", "n.sim", "n.start", "m.sim",
"startMethod", "zres", "presigma", "tailres",
"preresiduals", "prereturns", "model",
"mexsimdata", "vexsimdata","prerealized"), envir = environment())
simlist = parLapply(cluster, as.list(1:m), fun = function(i){
maxx = mspec@spec[[i]]@model$maxOrder;
htmp = rugarch::ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(tailres[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
return(list(h = h, x = x))
})
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% Rbar %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
} else{
simlist = vector(mode="list", length=m)
for(i in 1:m){
maxx = mspec@spec[[i]]@model$maxOrder
htmp = ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(fit@mfit$tailuresids[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
simlist[[i]] = list(h = h, x = x)
}
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% Rbar %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
}
if( model$modelinc[1]>0 ){
simRes = simX
simX = mvmean.varsim(model = model, Data = Data, res = simX,
mexsimdata = mexsimdata, prereturns = prereturns, m.sim = m.sim,
n.sim = n.sim, n.start = n.start, startMethod = startMethod,
cluster = cluster)
} else{
# reshape
for(j in 1:m.sim) simX[[j]] = tail(simX[[j]], n.sim)
}
msim = list()
if(only.density){
msim$simX = simX
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
} else{
msim$simH = simH
msim$simR = simR
msim$simQ = simQ
msim$simX = simX
msim$simRes = simRes
msim$simZ = zres
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
}
model$only.density = only.density
# need to run gc and rm since 'memory accumulates here'
if(exists("simX")) rm(simX)
if(exists("simRes")) rm(simRes)
if(exists("simH")) rm(simH)
if(exists("H")) rm(H)
if(exists("simlist")) rm(simlist)
if(exists("tmpH")) rm(tmpH)
if(exists("mtmp")) rm(mtmp)
if(exists("simxX")) rm(simxX)
if(exists("zres")) rm(zres)
if(exists("ures")) rm(ures)
gc(verbose = FALSE)
ans = new("cGARCHsim",
msim = msim,
model = model)
return( ans )
}
# time varying copula simulation
.cgarchsim2 = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL, preresiduals = NULL,
prereturns = NULL, preR = NULL, preQ = NULL, preZ = NULL, rseed = NULL,
mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, only.density = FALSE,
prerealized = NULL, ...)
{
# first generate the copula random uniform numbers (static copula)
# ures --> transform --> zres
# pass zres to GARCH simulation
# pass if needed to VAR model
if( is.null(rseed) ){
rseed = as.integer(runif(m.sim, 1, Sys.time()))
} else {
if(length(rseed) == 1) rseed = c(rseed[1], rseed[1]+seq_len(m.sim))
rseed = as.integer( rseed[1:m.sim] )
}
model = fit@model
umodel = model$umodel
T = model$modeldata$T
Data = model$modeldata$data[1:T, ]
m = dim(Data)[2]
nsim = n.sim + n.start
mg = fit@model$maxgarchOrder
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, NULL)
for(i in 1:m){
# variance targeting case
if(model$midx["omega",i]==0){
setfixed(mspec@spec[[i]]) = as.list(c("omega"=model$mpars["omega",i], model$mpars[which(model$midx[,i]==1), i]))
} else{
setfixed(mspec@spec[[i]]) = as.list(model$mpars[which(model$midx[,i]==1), i])
}
}
uncv = sapply(mspec@spec, FUN = function(x) uncvariance(x))
if( !is.null(presigma) ){
if( !is.matrix(presigma) )
stop("\ncgarchsim-->error: presigma must be a matrix.")
if( dim(presigma)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for presigma.")
if( dim(presigma)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for presigma (need ", mg, " rows.", sep = ""))
} else{
if(startMethod == "sample"){
presigma = fit@mfit$tailusigma
} else{
presigma = matrix(uncv, ncol = m, nrow = 25)
}
}
if(is.null(preR)){
Rbar = last(rcor(fit), 1)[,,1]
} else{
if(dim(preR)[1] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(dim(preR)[2] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(any(diag(preR) != 1))
stop("\ncgarchsim-->error: preR diagonals must be 1.\n")
Rbar = preR
}
Qbar = fit@mfit$Qbar
if(model$modelinc[6]>0){
Nbar = fit@mfit$Nbar
} else{
Nbar = matrix(0, m, m)
}
if( !is.null(prereturns) ){
if( !is.matrix(prereturns) )
stop("\ncgarchsim-->error: prereturns must be a matrix.")
if( dim(prereturns)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prereturns.")
if( dim(prereturns)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prereturns (need ", mg, " rows.", sep = ""))
} else{
prereturns = tail(model$modeldata$data[1:T, ], 25)
}
if(fit@model$umodel$modeldesc$vmodel[1]=="realGARCH"){
if( !is.null(prerealized) ){
if( !is.matrix(prerealized) )
stop("\ncgarchsim-->error: prerealized must be a matrix.")
if( dim(prerealized)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prerealized.")
if( dim(prerealized)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prerealized (need ", mg, " rows.", sep = ""))
} else{
# might want to include the option for unconditional
# value of realized (unexported in rugarch)
prerealized = tail(fit@mfit$realizedVol[1:T,], 25)
}
} else{
prerealized = matrix(NA, ncol = m, nrow = 25)
}
# This step is KEY if we are to replicate the 1-ahead filter
if(startMethod == "sample"){
if(is.null(preZ)){
preZ = matrix(tail(fit@mfit$Z, fit@model$maxdccOrder), ncol = m)
} else{
preZ = matrix(preZ[1,], ncol = m, nrow = fit@model$maxdccOrder)
}
if(is.null(preQ)){
preQ = fit@mfit$Qt[[length(fit@mfit$Qt)]]
} else{
preQ = preQ
}
} else{
if(is.null(preZ)){
preZ = matrix(0, ncol = m, nrow = fit@model$maxdccOrder)
} else{
preZ = matrix(preZ[1,], ncol = m, nrow = fit@model$maxdccOrder)
}
if(is.null(preQ)){
preQ = Rbar
} else{
preQ = preQ
}
}
ures = .sample.copula(model, Qbar = Qbar, preQ = preQ, Rbar = Rbar, Nbar = Nbar,
preZ = preZ, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
rseed = rseed, cluster = cluster)
# ures = [0,1] copula random numbers
# now transform back into margins for use in garch
transformation = model$modeldesc$transformation
distu = umodel$modeldesc$distribution
zres = array(NA, dim = c(nsim, m, m.sim))
# parallel:
if( !is.null(cluster) ){
ssfit = model$sfit
minc = umodel$modelinc
mpars = model$mpars
sxres = fit@mfit$stdresid
clusterEvalQ(cluster, require('rmgarch'))
if(transformation == "spd"){
clusterExport(cluster, c("ures", "mpars", "minc",
"m", "sxres", "ssfit", "transformation"),
envir = environment())
} else{
clusterExport(cluster, c("ures", "mpars", "minc",
"m", "sxres", "ssfit", "transformation"),
envir = environment())
}
clusterExport(cluster, c(".qparametric", ".qempirical",".qspd"), envir = environment())
mtmp = parLapply(cluster, as.list(1:m.sim), fun = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures$Usim[,,i], ncol = m), pars = mpars, modelinc = minc),
empirical = .qempirical(matrix(ures$Usim[,,i], ncol = m), sxres),
spd = .qspd(matrix(ures$Usim[,,i], ncol = m), sfit = ssfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
} else{
mtmp = lapply(as.list(1:m.sim), FUN = function(i) switch(transformation,
parametric = .qparametric(matrix(ures$Usim[,,i], ncol = m), pars = model$mpars, modelinc = umodel$modelinc),
empirical = .qempirical(matrix(ures$Usim[,,i], ncol = m), fit@mfit$stdresid),
spd = .qspd(matrix(ures$Usim[,,i], ncol = m), sfit = model$sfit)))
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
}
# Now we have the innovations which we feed back into the garch routine to simulate
simRes = simX = simR = simQ = simH = simSeries = vector(mode = "list", length = m.sim)
#if( is.null(fit@mfit$vrmodel) ){
# if(is.null(prereturns)) prereturns = fit@mfit$tailuret
#}
if( !is.null(cluster) ){
tailres = fit@mfit$tailuresids
clusterEvalQ(cluster, loadNamespace('rugarch'))
clusterExport(cluster, c("mspec", "n.sim", "n.start", "m.sim",
"startMethod", "zres", "presigma", "tailres",
"preresiduals", "prereturns", "model", "mexsimdata",
"vexsimdata", "rseed","prerealized"), envir = environment())
simR = ures$simR
simlist = parLapply(cluster, as.list(1:m), fun = function(i){
maxx = mspec@spec[[i]]@model$maxOrder;
htmp = rugarch::ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(tailres[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
return(list(h = h, x = x))
})
if(!only.density){
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% simR[[i]][,,j] %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
} else{
simR = ures$simR
#simQ = lapply(mtmp, FUN = function(x) if(is.matrix(x$Q)) array(x$Q, dim = c(m, m, n.sim)) else last(x$Q, n.sim))
simlist = vector(mode="list", length=m)
for(i in 1:m){
maxx = mspec@spec[[i]]@model$maxOrder
htmp = ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(fit@mfit$tailuresids[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
simlist[[i]] = list(h = h, x = x)
}
if(!only.density){
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% simR[[i]][,,j] %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
}
if( model$modelinc[1]>0 ){
simRes = simX
simX = mvmean.varsim(model = model, Data = Data, res = simX,
mexsimdata = mexsimdata, prereturns = prereturns, m.sim = m.sim,
n.sim = n.sim, n.start = n.start, startMethod = startMethod,
cluster = cluster)
} else{
# reshape
for(j in 1:m.sim) simX[[j]] = tail(simX[[j]], n.sim)
}
msim = list()
if(only.density){
msim$simX = simX
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
} else{
msim$simH = simH
msim$simR = simR
msim$simX = simX
msim$simRes = simRes
msim$simZ = zres
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
}
model$only.density = only.density
# need to run gc and rm since 'memory accumulates here'
if(exists("simX")) rm(simX)
if(exists("simRes")) rm(simRes)
if(exists("simH")) rm(simH)
if(exists("H")) rm(H)
if(exists("simlist")) rm(simlist)
if(exists("tmpH")) rm(tmpH)
if(exists("mtmp")) rm(mtmp)
if(exists("simxX")) rm(simxX)
if(exists("zres")) rm(zres)
if(exists("ures")) rm(ures)
if(exists("simR")) rm(simR)
gc(verbose = FALSE)
ans = new("cGARCHsim",
msim = msim,
model = model)
return( ans )
}
.fullinc2 = function(modelinc, umodel){
m = dim(umodel$modelinc)[2]
# the par matrix will have the max parameter vis Asset (if an asset does not have
# the parameter it will be zero...for matrix estimation.
# the index places the solver estimates into their place in the matrix
###################################################################################
# Asset1 .... Asset2 .... AssetN ... ........ Joint
# mu
# ar
# ma
# arfima
# archm
# mxreg
# omega
# alpha
# beta
# gamma
# delta
# lambda
# eta1
# eta2
# vxreg
# skew
# shape
# ghlambda
# xi
# C
# dcca
# dccb
# dccg
# mshape
# mskew
vecmax = rep(0, 19)
names(vecmax) = rownames(umodel$modelinc[1:19,])
vecmax = apply(umodel$modelinc, 1, FUN = function(x) max(x) )
maxOrder = apply(umodel$modelinc, 2, FUN = function(x) max(c(x[2], x[3], x[8], x[9])))
sumv = 19 + sum(pmax(1, vecmax[c(2,3,6,8,9,10,11,12,13,15,16)])) - 11
tmpmat = matrix(0, ncol = m+1, nrow = sumv)
nx = 0
pnames = NULL
# mu
if(vecmax[1]>0){
tmpmat[1, 1:m] = umodel$modelinc[1,]
}
nx = nx + max(1, vecmax[1])
pnames = c(pnames, "mu")
# ar
if(vecmax[2]>0){
for(i in 1:vecmax[2]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[2,] >= i)
pnames = c(pnames, paste("ar", i, sep = ""))
}
} else{
pnames = c(pnames, "ar")
}
nx = nx + max(1, vecmax[2])
# ma
if(vecmax[3]>0){
for(i in 1:vecmax[3]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[3,] >= i)
pnames = c(pnames, paste("ma", i, sep = ""))
}
} else{
pnames = c(pnames, "ma")
}
nx = nx + max(1, vecmax[3])
# arfima
if(vecmax[4]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[4, ]
}
nx = nx + max(1, vecmax[4])
pnames = c(pnames, "arfima")
# archm no allowed
nx = nx + max(1, vecmax[5])
pnames = c(pnames, "archm")
# mxreg
if(vecmax[6]>0){
for(i in 1:vecmax[6]){
tmpmat[nx+i, 1:m] = as.integer(umodel$modelinc[6, ] >= i)
pnames = c(pnames, paste("mxreg", i, sep = ""))
}
} else{
pnames = c(pnames, "mxreg")
}
nx = nx + max(1, vecmax[6])
# omega
if(vecmax[7]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[7, ]
}
nx = nx + max(1, vecmax[7])
pnames = c(pnames, "omega")
# alpha
if(vecmax[8]>0){
for(i in 1:vecmax[8]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[8, ] >= i)
pnames = c(pnames, paste("alpha", i, sep = ""))
}
} else{
pnames = c(pnames, "alpha")
}
nx = nx + max(1, vecmax[8])
# beta
if(vecmax[9]>0){
for(i in 1:vecmax[9]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[9, ] >= i)
pnames = c(pnames, paste("beta", i, sep = ""))
}
} else{
pnames = c(pnames, "beta")
}
nx = nx + max(1, vecmax[9])
# gamma
if(vecmax[10]>0){
for(i in 1:vecmax[10]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[10, ] >= i)
pnames = c(pnames, paste("gamma", i, sep = ""))
}
} else{
pnames = c(pnames, "gamma")
}
nx = nx + max(1, vecmax[10])
# eta1
if(vecmax[11]>0){
for(i in 1:vecmax[11]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[11, ] >= i)
pnames = c(pnames, paste("eta1", i, sep = ""))
}
} else{
pnames = c(pnames, "eta1")
}
nx = nx + max(1, vecmax[11])
# eta2
if(vecmax[12]>0){
for(i in 1:vecmax[12]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[12, ] >= i)
pnames = c(pnames, paste("eta2", i, sep = ""))
}
} else{
pnames = c(pnames, "eta2")
}
nx = nx + max(1, vecmax[12])
# delta
if(vecmax[13]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[13, ]
}
nx = nx + max(1, vecmax[13])
pnames = c(pnames, "delta")
# lambda
if(vecmax[14]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[14, ]
}
nx = nx + max(1, vecmax[14])
pnames = c(pnames, "lambda")
# vxreg
if(vecmax[15]>0){
for(i in 1:vecmax[15]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[15, ] >= i)
pnames = c(pnames, paste("vxreg", i, sep = ""))
}
} else{
pnames = c(pnames, "vxreg")
}
nx = nx + max(1, vecmax[15])
# skew
if(vecmax[16]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[16, ]
}
nx = nx + max(1, vecmax[16])
pnames = c(pnames, "skew")
# shape
if(vecmax[17]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[17, ]
}
nx = nx + max(1, vecmax[17])
pnames = c(pnames, "shape")
# skew
if(vecmax[18]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[18, ]
}
nx = nx + max(1, vecmax[18])
pnames = c(pnames, "ghlambda")
if(vecmax[19]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[19, ]
}
nx = nx + max(1, vecmax[19])
pnames = c(pnames, "xi")
sumdcc = 6 + sum(pmax(1, modelinc[c(3,4,5,6,7,8)])) - 6
tmpmat = rbind(tmpmat, matrix(0, ncol = m+1, nrow = sumdcc))
if(modelinc[3]>0){
for(i in 1:modelinc[3]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("C", i, sep = ""))
}
} else{
pnames = c(pnames, "C")
}
nx = nx + max(1, modelinc[3])
if(modelinc[4]>0){
for(i in 1:modelinc[4]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dcca", i, sep = ""))
}
} else{
pnames = c(pnames, "dcca")
}
nx = nx + max(1, modelinc[4])
if(modelinc[5]>0){
for(i in 1:modelinc[5]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dccb", i, sep = ""))
}
} else{
pnames = c(pnames, "dccb")
}
nx = nx + max(1, modelinc[5])
if(modelinc[6]>0){
for(i in 1:modelinc[6]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dccg", i, sep = ""))
}
} else{
pnames = c(pnames, "dccg")
}
nx = nx + max(1, modelinc[6])
# we allow for possibility of vector valued shape and skew parameters for future expansion
if(modelinc[7]>0){
for(i in 1:modelinc[7]){
tmpmat[nx+i, m+1] = 1
if(modelinc[7]>1) pnames = c(pnames, paste("mshape", i, sep = "")) else pnames = c(pnames, "mshape")
}
} else{
pnames = c(pnames, "mshape")
}
nx = nx + max(1, modelinc[7])
if(modelinc[8]>0){
for(i in 1:modelinc[8]){
tmpmat[nx+i, m+1] = 1
if(modelinc[8]>1) pnames = c(pnames, paste("mskew", i, sep = "")) else pnames = c(pnames, "mskew")
}
} else{
pnames = c(pnames, "mskew")
}
# NOW we have and indicator matrix
colnames(tmpmat) = c(paste("Asset", 1:m, sep = ""), "Joint")
rownames(tmpmat) = pnames
return(tmpmat)
}
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Defines functions .fullinc2 .cgarchsim2 .cgarchsim1 .cgarchsim .cgarchfilter.dynamic .cgarchfilter.static .cgarchfilter .cgarchfit.static .cgarchfit.dynamic .cgarchfit .cgarchspec
#################################################################################
##
## R package rmgarch by Alexios Galanos Copyright (C) 2008-2022.
## This file is part of the R package rmgarch.
##
## The R package rmgarch 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 rmgarch 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.
##
#################################################################################
#################################################################################
# Fit, Filter, Forecast and Simulation
#-------------------------------------
.cgarchspec = function(uspec, VAR = FALSE, robust = FALSE, lag = 1, lag.max = NULL,
lag.criterion = c("AIC", "HQ", "SC", "FPE"),
external.regressors = NULL, robust.control = list("gamma" = 0.25,
"delta" = 0.01, "nc" = 10, "ns" = 500), dccOrder = c(1,1),
asymmetric = FALSE,
distribution.model = list(copula = c("mvnorm", "mvt"),
method = c("Kendall", "ML"), time.varying = FALSE,
transformation = c("parametric", "empirical", "spd")),
start.pars = list(), fixed.pars = list())
{
.eps = .Machine$double.eps
VAR.opt = list()
if(is.null(VAR)){
VAR.opt$VAR = FALSE
} else{
VAR.opt$VAR = as.logical(VAR)
}
if(is.null(robust)){
VAR.opt$robust = FALSE
} else{
VAR.opt$robust = as.logical(robust)
}
if(is.null(lag)){
VAR.opt$lag = 1
} else{
VAR.opt$lag = as.integer(lag)
}
if(is.null(lag.max)){
VAR.opt$lag.max = NULL
} else{
VAR.opt$lag.max = as.integer(min(1, lag.max))
}
if(is.null(lag.criterion)){
VAR.opt$lag.criterion = "AIC"
} else{
VAR.opt$lag.criterion = lag.criterion[1]
}
if(is.null(external.regressors)){
VAR.opt$external.regressors = NULL
} else{
VAR.opt$external.regressors = external.regressors
}
rc = list("gamma" = 0.25, "delta" = 0.01, "nc" = 10, "ns" = 500)
rcmatch = match(names(robust.control), c("gamma", "delta", "nc", "ns"))
if(length(rcmatch[!is.na(rcmatch)]) > 0){
rx = which(!is.na(rcmatch))
rc[rcmatch[!is.na(rcmatch)]] = robust.control[rx]
}
VAR.opt$robust.control = rc
modeldata = list()
modeldesc = list()
m = length(uspec@spec)
modelinc = rep(0, 11)
names(modelinc) = c("var", "mvmxreg", "C", "dcca", "dccb", "dccg", "mshape",
"mskew", "aux", "aux", "aux")
if(is.null(distribution.model$copula)){
distribution = "mvnorm"
} else{
distribution = tolower(distribution.model$copula)
}
distribution = distribution[1]
valid.distributions = c("mvnorm", "mvt")
if(!any(distribution == valid.distributions))
stop("\nInvalid Copula Distribution Choice\n", call. = FALSE)
modeldesc$distribution = distribution
if(distribution == "mvt") modelinc[7] = 1
if(is.null(distribution.model$method)){
method = "kendall"
} else{
method = tolower(distribution.model$method)
}
method = method[1]
valid.methods = c("kendall", "ml")
if(!any(method == valid.methods)){
if(distribution == "mvt")
warning("\nInvalid Rho Method Estimation Choice\n", call. = FALSE)
method = "kendall"
}
modeldesc$cor.method = toupper(method)
if(is.null(distribution.model$time.varying)){
timecopula = FALSE
} else{
timecopula = as.logical(distribution.model$time.varying)
}
timecopula = timecopula[1]
modeldesc$timecopula = timecopula
if(!timecopula && tolower(method[1]) == "ml") modelinc[3] = ((m*m - m)/2)
if(is.null(distribution.model$transformation)){
transformation = "parametric"
} else{
transformation = tolower(distribution.model$transformation)
}
transformation = transformation[1]
valid.transformations = c("parametric", "empirical", "spd")
if(!any(transformation == valid.transformations))
stop("\nInvalid Copula Transformation Choice\n", call. = FALSE)
modeldesc$transformation = transformation
if(timecopula){
if(is.null(dccOrder)){
modelinc[4:5] = 1
} else{
modelinc[4] = as.integer(dccOrder[1])
modelinc[5] = as.integer(dccOrder[2])
}
if(is.null(asymmetric)){
modelinc[6] = 0
} else{
if(as.logical(asymmetric)) modelinc[6] = modelinc[4]
}
} else{
modelinc[4:6] = 0
}
if( VAR ){
if(is.null(VAR.opt$lag)){
modelinc[1] = 1
} else{
modelinc[1] = as.integer( VAR.opt$lag )
}
if(!is.null(VAR.opt$external.regressors)){
if(!is.matrix(VAR.opt$external.regressors))
stop("\nexternal.regressors must be a matrix.")
modelinc[2] = dim(VAR.opt$external.regressors)[2]
modeldata$mexdata = VAR.opt$external.regressors
} else{
modeldata$mexdata = NULL
}
}
varmodel = list()
umodel = vector(mode ="list")
if( modelinc[1]>0 ){
varmodel$robust = VAR.opt$robust
varmodel$lag.max = VAR.opt$lag.max
varmodel$lag.criterion = VAR.opt$lag.criterion
varmodel$robust.control = VAR.opt$robust.control
umodel$modelinc = matrix(0, ncol = m, nrow = 21)
rownames(umodel$modelinc) = names(uspec@spec[[1]]@model$modelinc[1:21])
umodel$modeldesc = list()
umodel$vt = sapply(uspec@spec, FUN = function(x) x@model$modelinc[22])
umodel$modeldesc$vmodel = vector(mode = "character", length = m)
umodel$modeldesc$vsubmodel = vector(mode = "character", length = m)
umodel$start.pars = umodel$fixed.pars = vector(mode = "list", length = m)
umodel$modeldesc$distribution = vector(mode = "character", length = m)
umodel$modeldata = list()
umodel$modeldata$vexdata = vector(mode = "list", length = m)
for(i in 1:m){
# zero the mean equation since we are using VAR
umodel$modeldesc$vmodel[i] = uspec@spec[[i]]@model$modeldesc$vmodel
umodel$modeldesc$vsubmodel[i] = ifelse(is.null(uspec@spec[[i]]@model$modeldesc$vsubmodel),
"GARCH", uspec@spec[[i]]@model$modeldesc$vsubmodel)
umodel$modeldesc$distribution[i] = uspec@spec[[i]]@model$modeldesc$distribution
umodel$modelinc[,i] = uspec@spec[[i]]@model$modelinc[1:21]
umodel$modelinc[1:6,i] = 0
umodel$modeldata$vexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$vexdata)) NA else uspec@spec[[i]]@model$modeldata$vexdata
umodel$start.pars[[i]] = if(is.null(uspec@spec[[i]]@model$start.pars)) NA else uspec@spec[[i]]@model$start.pars
umodel$fixed.pars[[i]] = if(is.null(uspec@spec[[i]]@model$fixed.pars)) NA else uspec@spec[[i]]@model$fixed.pars
umodel$modeldata$mexdata[[i]] = NA
}
} else{
varmodel$lag.max = 1
varmodel$lag.criterion = "HQ"
umodel$modelinc = matrix(0, ncol = m, nrow = 21)
rownames(umodel$modelinc) = names(uspec@spec[[1]]@model$modelinc[1:21])
umodel$modeldesc = list()
# variance targeting custom
umodel$vt = sapply(uspec@spec, FUN = function(x) x@model$modelinc[22])
umodel$modeldesc$vmodel = vector(mode = "character", length = m)
umodel$modeldesc$vsubmodel = vector(mode = "character", length = m)
umodel$start.pars = umodel$fixed.pars = vector(mode = "list", length = m)
umodel$modeldesc$distribution = vector(mode = "character", length = m)
umodel$modeldata = list()
umodel$modeldata$mexdata = vector(mode = "list", length = m)
umodel$modeldata$vexdata = vector(mode = "list", length = m)
for(i in 1:m){
umodel$modeldesc$vmodel[i] = uspec@spec[[i]]@model$modeldesc$vmodel
umodel$modeldesc$vsubmodel[i] = ifelse(is.null(uspec@spec[[i]]@model$modeldesc$vsubmodel),"GARCH",uspec@spec[[i]]@model$modeldesc$vsubmodel)
umodel$modeldesc$distribution[i] = uspec@spec[[i]]@model$modeldesc$distribution
umodel$modelinc[,i] = uspec@spec[[i]]@model$modelinc[1:21]
umodel$modeldata$mexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$mexdata)) NA else uspec@spec[[i]]@model$modeldata$mexdata
umodel$modeldata$vexdata[[i]] = if(is.null(uspec@spec[[i]]@model$modeldata$vexdata)) NA else uspec@spec[[i]]@model$modeldata$vexdata
umodel$start.pars[[i]] = if(is.null(uspec@spec[[i]]@model$start.pars)) NA else uspec@spec[[i]]@model$start.pars
umodel$fixed.pars[[i]] = if(is.null(uspec@spec[[i]]@model$fixed.pars)) NA else uspec@spec[[i]]@model$fixed.pars
}
}
modelinc[11] = which(c("mvnorm", "mvt") == distribution)
maxdccOrder = max(dccOrder)
maxgarchOrder = max( sapply(uspec@spec, FUN = function(x) max(max(x@model$modelinc[2:3]), max(x@model$modelinc[8:9]) ) ) )
maxOrder = max( maxdccOrder, maxgarchOrder )
if(modelinc[1]>0){
maxgarchOrder = max(c(maxgarchOrder, modelinc[1]))
}
modeldesc$dccmodel = ifelse(modelinc[6]>0, "ADCC", "DCC")
pars = matrix(0, ncol = 6, nrow = 6)
colnames(pars) = c("Level", "Fixed", "Include", "Estimate", "LB", "UB")
pidx = matrix(NA, nrow = 6, ncol = 2)
colnames(pidx) = c("begin", "end")
rownames(pidx) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
pos = 1
pos.matrix = matrix(0, ncol = 3, nrow = 6)
colnames(pos.matrix) = c("start", "stop", "include")
rownames(pos.matrix) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
for(i in 1:6){
if( modelinc[2+i] > 0 ){
pos.matrix[i,1:3] = c(pos, pos+modelinc[2+i]-1, 1)
pos = max(pos.matrix[1:i,2]+1)
}
}
mm = sum(modelinc[3:8])
mm = mm - length( which(modelinc[c(3:8)]>0) )
pars = matrix(0, ncol = 6, nrow = 6 + mm)
colnames(pars) = c("Level", "Fixed", "Include", "Estimate", "LB", "UB")
pidx = matrix(NA, nrow = 6, ncol = 2)
colnames(pidx) = c("begin", "end")
rownames(pidx) = c("C", "dcca", "dccb", "dccg", "mshape", "mskew")
fixed.names = names(fixed.pars)
start.names = names(start.pars)
fixed.pars = unlist(fixed.pars)
start.pars = unlist(start.pars)
pnames = NULL
nx = 0
pn = 1
pidx[1,1] = 1
if(pos.matrix[1,3] == 1){
cvm = 1:((m*m - m)/2)
pn = length( seq(pos.matrix[1,1], pos.matrix[1,2], by = 1) )
for(i in 1:pn){
nnx = paste("C", cvm[i], sep="")
pars[(nx+i), 1] = 0
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = -0.99
pars[(nx+i), 6] = 0.99
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "C")
}
pidx[1,2] = pn
nx = pn
pn = 1
pidx[2,1] = nx+1
if(pos.matrix[2,3] == 1){
pn = length( seq(pos.matrix[2,1], pos.matrix[2,2], by = 1) )
for(i in 1:pn){
nnx = paste("dcca", i, sep="")
pars[(nx+i), 1] = 0.05/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dcca")
}
pidx[2,2] = nx+pn
nx = nx + pn
pn = 1
pidx[3,1] = nx+1
if(pos.matrix[3,3] == 1){
pn = length( seq(pos.matrix[3,1], pos.matrix[3,2], by = 1) )
for(i in 1:pn){
nnx = paste("dccb", i, sep="")
pars[(nx+i), 1] = 0.9/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dccb")
}
pidx[3,2] = nx+pn
nx = nx + pn
pn = 1
pidx[4,1] = nx+1
if(pos.matrix[4,3] == 1){
pn = length( seq(pos.matrix[4,1], pos.matrix[4,2], by = 1) )
for(i in 1:pn){
nnx = paste("dccg", i, sep="")
pars[(nx+i), 1] = 0.05/pn
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = .eps
pars[(nx+i), 6] = 1
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "dccg")
}
pidx[4,2] = nx+pn
nx = nx + pn
pn = 1
pidx[5,1] = nx+1
if(modelinc[7]<=1){
if(pos.matrix[5,3]==1){
pars[nx+pn, 3] = 1
pars[nx+pn, 1] = 5
pars[(nx+pn), 5] = 4
pars[(nx+pn), 6] = 50
if(any(substr(start.names, 1, 6) == "mshape")) pars[nx+pn, 1] = start.pars["mshape"]
if(any(substr(fixed.names, 1, 6) == "mshape")){
pars[nx+pn,2] = 1
pars[nx+pn, 1] = fixed.pars["mshape"]
} else{
pars[nx+pn,4] = 1
}
}
pnames = c(pnames, "mshape")
} else{
if(pos.matrix[5,3] == 1){
pn = length( seq(pos.matrix[5,1], pos.matrix[5,2], by = 1) )
for(i in 1:pn){
nnx = paste("mshape", i, sep="")
pars[(nx+i), 1] = 5
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
pars[(nx+i), 3] = 1
pars[(nx+i), 5] = 4
pars[(nx+i), 6] = 50
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "mshape")
}
}
pidx[5,2] = nx+pn
nx = nx + pn
pn = 1
pidx[6,1] = nx+1
if(modelinc[8]<=1){
if(pos.matrix[6,3]==1){
pars[nx+pn, 3] = 1
pars[nx+pn, 1] = 0.5
pars[(nx+pn), 5] = -1
pars[(nx+pn), 6] = 1
if(any(substr(start.names, 1, 5) == "mskew")) pars[nx+pn, 1] = start.pars["mskew"]
if(any(substr(fixed.names, 1, 5) == "mskew")){
pars[nx+pn,2] = 1
pars[nx+pn, 1] = fixed.pars["mskew"]
} else{
pars[nx+pn,4] = 1
}
}
pnames = c(pnames, "mskew")
} else{
if(pos.matrix[6,3] == 1){
pn = length( seq(pos.matrix[6,1], pos.matrix[6,2], by = 1) )
for(i in 1:pn){
nnx = paste("mskew", i, sep="")
pars[(nx+i), 1] = 3
pars[(nx+i), 5] = -1
pars[(nx+i), 6] = 1
if(any(substr(start.names, 1, nchar(nnx))==nnx)){
nix = which(start.names == nnx)
pars[(nx+i), 1] = start.pars[nix]
}
if(any(substr(fixed.names, 1, nchar(nnx))==nnx)){
nix = which(fixed.names == nnx)
pars[(nx+i), 1] = fixed.pars[nix]
pars[(nx+i), 2] = 1
} else{
pars[(nx+i), 4] = 1
}
pnames = c(pnames, nnx)
}
} else{
pnames = c(pnames, "mskew")
}
}
pidx[6,2] = nx+pn
rownames(pars) = pnames
model = list(modelinc = modelinc, modeldesc = modeldesc, modeldata = modeldata,
varmodel = varmodel, pars = pars, start.pars = start.pars,
fixed.pars = fixed.pars, maxgarchOrder = maxgarchOrder,
maxdccOrder = maxdccOrder, pos.matrix = pos.matrix, pidx = pidx)
ans = new("cGARCHspec",
model = model,
umodel = umodel)
return(ans)
}
.cgarchfit = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL, fit = NULL,
VAR.fit = NULL, realizedVol = NULL, ...)
{
type = ifelse(spec@model$modeldesc$timecopula, "dynamic", "static")
ans = switch(type,
dynamic = .cgarchfit.dynamic(spec = spec, data = data, spd.control = spd.control,
fit.control = fit.control, solver = solver, solver.control = solver.control,
out.sample = out.sample, cluster = cluster, fit = fit, VAR.fit = VAR.fit,
realizedVol = realizedVol, ...),
static = .cgarchfit.static(spec = spec, data = data, spd.control = spd.control,
fit.control = fit.control, solver = solver, solver.control = solver.control,
out.sample = out.sample, cluster = cluster, fit = fit, VAR.fit = VAR.fit,
realizedVol = realizedVol, ...))
return( ans )
}
.cgarchfit.dynamic = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL,
fit = NULL, VAR.fit = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
.eps = .Machine$double.eps
model = spec@model
umodel = spec@umodel
ufit.control = list()
if(is.null(fit.control$stationarity)){
ufit.control$stationarity = TRUE
} else {
ufit.control$stationarity = fit.control$stationarity
fit.control$stationarity = NULL
}
if(is.null(fit.control$scale)){
ufit.control$scale = TRUE
} else{
ufit.control$scale = fit.control$scale
fit.control$scale = NULL
}
if(is.null(fit.control$eval.se)) fit.control$eval.se = TRUE
if(length(solver)==2){
garch.solver = solver[1]
solver = solver[2]
} else{
garch.solver = solver[1]
}
solver = match.arg(tolower(solver)[1], c("solnp", "nlminb", "lbfgs","gosolnp"))
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfit-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfit-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = NROW(xdata$data)
if( (n-n.start) < 100)
stop("\ndccfit-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( model$modelinc[1]>0 ){
tmp = mvmean.varfit(model = model, data = data, VAR.fit = VAR.fit, T = T,
out.sample = out.sample, cluster = cluster)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
varcoef = tmp$varcoef
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
# Univariate GARCH fit
# create a multispec list, check for variance targeting (vt)
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
# Check if a pre-fitted uGARCHmultifit object provided, else estimate
if( !is.null(fit) && is(fit, "uGARCHmultifit") ){
# check VAR and fit:
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(fit@fit[[i]]@model$modelinc[1:6])>0)
stop("\nThe user supplied fit object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
fitlist = fit
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
} else{
fitlist = multifit(multispec = mspec, data = xts(zdata, index), out.sample = n.start,
solver = garch.solver, solver.control = solver.control,
fit.control = ufit.control, cluster = cluster, realizedVol = realizedVol, ...)
converge = sapply(fitlist@fit, FUN = function(x) x@fit$convergence)
if( any( converge == 1 ) ){
pr = which(converge != 1)
cat("\nNon-Converged:\n")
print(pr)
cat("\ndccfit-->error: convergence problem in univariate fit...")
cat("\n...returning uGARCHmultifit object instead...check and resubmit...")
return( fitlist )
}
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
}
stdresid = res/sig
#-----------------------------------------------------------------------------------
# Create the Full Model Pars and Indices
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
# This is the estimated parameter index
eidx = .estindfn(midx, mspec, model$pars)
unipars = lapply(fitlist@fit, FUN = function(x) x@fit$ipars[x@fit$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
LB = ipars[,5]
UB = ipars[,6]
estidx = as.logical( ipars[,4] )
npars = sum(estidx)
# Transformation of z to [0,1] domain
ans = switch(transformation,
parametric = .pparametric(fitlist, stdresid),
empirical = .pempirical(stdresid),
spd = .pspd(stdresid, spd.control))
if(transformation == "spd"){
ures = ans$ures
sfit = ans$sfit
} else{
ures = ans
sfit = NULL
}
# make small tail adjustments in order to avoid problem with the quantile
# functions in optimization
if(any(ures > 0.99999)){
xn = which(ures > 0.99999)
ures[xn] = 0.99999
}
if(any(ures < .eps)){
xn = which(ures < (1.5*.eps))
ures[xn] = .eps
}
model$spd.control = spd.control
mgarchenv = new.env(hash = TRUE)
arglist = list()
arglist$mgarchenv = mgarchenv
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$eval.se = fit.control$eval.se
arglist$solver = solver
arglist$fit.control = fit.control
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$fitlist = fitlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$dccN = npars
arglist$stdresid = stdresid
arglist$ures = ures
if(any(ipars[,2]==1)){
if(npars == 0){
xspex = spec
for(i in 1:m) xspex@umodel$fixed.pars[[i]] = as.list(fitlist@fit[[i]]@model$pars[fitlist@fit[[i]]@model$pars[,3]==1,1])
return(cgarchfilter(spec = xspex, data = xts(data, index), out.sample = out.sample,
filter.control = list(), spd.control = spd.control,
cluster = cluster, VAR.fit = VAR.fit, realizedVol = realizedVol))
} else{
# with some parameters fixed we extract them (to be rejoined at end)
# so that they do not enter the solver
use.solver = 1
}
} else{
use.solver = 1
}
# start counter
assign("rmgarch_llh", 1, envir = mgarchenv)
ILB = 0
IUB = 1
# Asymmetric Spec has different constraints
if(model$modelinc[6]> 0) Ifn = copula.adcccon else Ifn = copula.dcccon
if( solver == "solnp" | solver == "gosolnp" ) fit.control$stationarity = FALSE else fit.control$stationarity = TRUE
arglist$fit.control = fit.control
# get
if( use.solver )
{
arglist$returnType = "llh"
solution = switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.tvnormalLLH1, Ifn, ILB, IUB, gr = NULL,
hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.tvstudentLLH1, Ifn, ILB, IUB, gr = NULL,
hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist))
sol = solution$sol
hess = solution$hess
timer = Sys.time()-tic
convergence = sol$convergence
mpars[which(eidx[,(m+1)]==1, arr.ind = TRUE),m+1] = sol$pars
ipars[estidx, 1] = sol$pars
arglist$mpars = mpars
arglist$ipars = ipars
} else{
hess = NULL
timer = Sys.time()-tic
convergence = 1
sol = list()
sol$message = "all parameters fixed"
}
# add some tail data of the univariate fits to be used with simulation
# (we assume that noone is going to add more than 50 lags in a univariate fit!
mfit = list()
if( convergence == 0 ){
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.tvnormalLLH2(mpars[which(eidx==1, arr.ind = TRUE)],
arglist = arglist),
mvt = copula.tvstudentLLH2(mpars[which(eidx==1, arr.ind = TRUE)],
arglist = arglist))
mfit$tailusigma = tail(sigma(fitlist), 50)
mfit$tailuresids = tail(residuals(fitlist), 50)
mfit$tailuret = tail(data, 50)
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel1(f = copula.tvnormalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.tvnormalLLH2"),
mvt = .cgarchmakefitmodel1(f = copula.tvstudentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.tvstudentLLH2"))
mfit = c(mfit, nderiv)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
} else{
mfit$convergence = 1
}
mfit$realizedVol = realizedVol
mfit$timer = Sys.time() - tic
# Add the spd fit as this is needed for simulation
model$sfit = sfit
rm(mgarchenv)
ans = new("cGARCHfit",
mfit = mfit,
model = model)
return(ans)
}
.cgarchfit.static = function(spec, data, spd.control = list(lower = 0.1, upper = 0.9,
type = "pwm", kernel = "epanech"),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
solver = "solnp", solver.control = list(), out.sample = 0, cluster = NULL,
fit = NULL, VAR.fit = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
.eps = .Machine$double.eps
model = spec@model
umodel = spec@umodel
ufit.control = list()
if(is.null(fit.control$stationarity)){
ufit.control$stationarity = TRUE
} else {
ufit.control$stationarity = fit.control$stationarity
fit.control$stationarity = NULL
}
if(is.null(fit.control$scale)){
ufit.control$scale = TRUE
} else{
ufit.control$scale = fit.control$scale
fit.control$scale = NULL
}
if(is.null(fit.control$eval.se)) fit.control$eval.se = TRUE
if(length(solver)==2){
garch.solver = solver[1]
solver = solver[2]
} else{
garch.solver = solver[1]
}
solver = match.arg(tolower(solver)[1], c("solnp", "nlminb", "lbfgs","gosolnp"))
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfit-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfit-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfit-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( model$modelinc[1]>0 ){
tmp = mvmean.varfit(model = model, data = data, VAR.fit = VAR.fit, T = T,
out.sample = out.sample, cluster = cluster)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
varcoef = tmp$varcoef
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
# Univariate GARCH fit (only subtract the ARMA order)
# create a multispec list
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
if( !is.null(fit) && is(fit, "uGARCHmultifit") ){
# check VAR and fit:
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(fit@fit[[i]]@model$modelinc[1:6])>0)
stop("\nThe user supplied fit object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
fitlist = fit
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
} else{
fitlist = multifit(multispec = mspec, data = xts(zdata, index), out.sample = n.start,
solver = garch.solver, solver.control = solver.control,
fit.control = ufit.control, cluster = cluster, realizedVol = realizedVol, ...)
converge = sapply(fitlist@fit, FUN = function(x) x@fit$convergence)
if( any( converge == 1 ) ){
pr = which(converge != 1)
cat("\nNon-Converged:\n")
print(pr)
cat("\ndccfit-->error: convergence problem in univariate fit...")
cat("\n...returning uGARCHmultifit object instead...check and resubmit...")
return( fitlist )
}
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(fitlist)
model$residuals = res = residuals(fitlist)
model$sigma = sig = sigma(fitlist)
}
stdresid = res/sig
#-----------------------------------------------------------------------------------
# Create the Full Model Pars and Indices
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
# This is the estimated parameter index
eidx = .estindfn(midx, mspec, model$pars)
unipars = lapply(fitlist@fit, FUN = function(x) x@fit$ipars[x@fit$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
ipars = model$pars
LB = ipars[,5]
UB = ipars[,6]
estidx = as.logical( ipars[,4] )
npars = sum(estidx)
# Transformation of z to [0,1] domain
ans = switch(transformation,
parametric = .pparametric(fitlist, stdresid),
empirical = .pempirical(stdresid),
spd = .pspd(stdresid, spd.control))
if(transformation == "spd"){
ures = ans$ures
sfit = ans$sfit
} else{
ures = ans
sfit = NULL
}
# make small tail adjustments in order to avoid problem with the quantile
# functions in optimization
if(any(ures > 0.99999)){
xn = which(ures > 0.99999)
ures[xn] = 0.99999
}
if(any(ures < .eps)){
xn = which(ures < (1.5*.eps))
ures[xn] = .eps
}
model$spd.control = spd.control
mgarchenv = new.env(hash = TRUE)
arglist = list()
arglist$mgarchenv = mgarchenv
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$eval.se = fit.control$eval.se
arglist$solver = solver
arglist$fit.control = fit.control
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$fitlist = fitlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$ures = ures
arglist$npars = npars
if(any(ipars[,2]==1)){
if(npars == 0){
use.solver = 0
} else{
# with some parameters fixed we extract them (to be rejoined at end)
# so that they do not enter the solver
use.solver = 1
}
} else{
use.solver = 1
}
# start counter
# get
if( use.solver )
{
arglist$returnType = "llh"
solution = try(
switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.normalLLH1, Ifn = NULL, ILB = NULL, IUB = NULL,
gr = NULL, hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.studentLLH1, Ifn = NULL, ILB = NULL, IUB = NULL,
gr = NULL, hessian = NULL, control = solver.control,
LB = ipars[estidx, 5], UB = ipars[estidx, 6],
arglist = arglist) ), silent = TRUE)
if(inherits(solution, "try-error") | solution$sol$convergence == 1){
solver = "lbfgs"
solver.control = list(trace = 1)
solution = switch(model$modeldesc$distribution,
mvnorm = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.normalLLH1, Ifn = NULL, ILB = NULL,
IUB = NULL, gr = NULL, hessian = NULL,
control = solver.control, LB = ipars[estidx, 5],
UB = ipars[estidx, 6], arglist = arglist),
mvt = .copulasolver(solver, pars = ipars[estidx, 1],
fun = copula.studentLLH1, Ifn = NULL, ILB = NULL,
IUB = NULL, gr = NULL, hessian = NULL,
control = solver.control, LB = ipars[estidx, 5],
UB = ipars[estidx, 6], arglist = arglist))
}
sol = solution$sol
hess = solution$hess
timer = Sys.time()-tic
convergence = sol$convergence
mpars[which(eidx[,(m+1)]==1, arr.ind = TRUE),m+1] = sol$pars
ipars[estidx, 1] = sol$pars
arglist$mpars = mpars
arglist$ipars = ipars
} else{
hess = NULL
timer = Sys.time()-tic
convergence = 1
sol = list()
sol$message = "all parameters fixed"
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist),
mvt = copula.studentLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist))
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel2(f = copula.normalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.normalLLH2"),
mvt = .cgarchmakefitmodel2(f = copula.studentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.studentLLH2"))
mfit = c(mfit, nderiv)
mfit$tailusigma = tail(sigma(fitlist), 25)
mfit$tailuresids = tail(residuals(fitlist), 25)
mfit$tailuret = tail(data, 25)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
}
# add some tail data of the univariate fits to be used with simulation
# (we assume that noone is going to add more than 50 lags in a univariate fit!
if( convergence == 0 ){
arglist$returnType = "ALL"
mfit = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist),
mvt = copula.studentLLH2(mpars[which(eidx==1, arr.ind = TRUE)], arglist = arglist))
mfit$tailusigma = tail(sigma(fitlist), 50)
mfit$tailuresids = tail(residuals(fitlist), 50)
mfit$tailuret = tail(data, 50)
nderiv = switch(model$modeldesc$distribution,
mvnorm = .cgarchmakefitmodel2(f = copula.normalLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.normalLLH2"),
mvt = .cgarchmakefitmodel2(f = copula.studentLLH2, arglist = arglist,
timer = timer, message = sol$message, fname = "copula.studentLLH2"))
mfit = c(mfit, nderiv)
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfit$timer = Sys.time() - tic
} else{
mfit$convergence = 1
mfit$timer = Sys.time() - tic
}
model$sfit = sfit
mfit$realizedVol = realizedVol
ans = new("cGARCHfit",
mfit = mfit,
model = model)
return(ans)
}
.cgarchfilter = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
type = ifelse(spec@model$modeldesc$timecopula, "dynamic", "static")
ans = switch(type,
dynamic = .cgarchfilter.dynamic(spec = spec, data = data, out.sample = out.sample,
filter.control = filter.control, spd.control = spd.control,
cluster = cluster, varcoef = varcoef, realizedVol = realizedVol, ...),
static = .cgarchfilter.static(spec = spec, data = data, out.sample = out.sample,
filter.control = filter.control, spd.control = spd.control,
cluster = cluster, varcoef = varcoef, realizedVol = realizedVol, ...))
return( ans )
}
.cgarchfilter.static = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
model = spec@model
umodel = spec@umodel
n.old = filter.control$n.old
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfilter-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfilter-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfilter-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# VAR model
if( spec@model$modelinc[1]>0 ){
tmp = mvmean.varfilter(model = model, data = data, varcoef = varcoef,
T = T, out.sample = out.sample)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# Univariate GARCH filter
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(umodel$modelinc[1:6,i])>0)
stop("\nThe user supplied univariate spec object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
filterlist = multifilter(multifitORspec = mspec, data = xts(zdata, index), out.sample = out.sample,
cluster = cluster, n.old = n.old, realizedVol = realizedVol, ...)
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(filterlist)
model$residuals = res = residuals(filterlist)
model$sigma = sig = sigma(filterlist)
stdresid = res/sig
N = dim(stdresid)[1]
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
eidx = midx
unipars = sapply(filterlist@filter, FUN = function(x) x@filter$ipars[x@filter$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1, arr.ind = TRUE), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
estidx = as.logical( ipars[,3] )
npars = sum(estidx)
model$spd.control = spd.control
arglist = list()
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$data = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$filterlist = filterlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$npars = npars
arglist$n.old = n.old
arglist$filter.control = filter.control
mfilter = list()
arglist$returnType = "ALL"
mfilter = switch(model$modeldesc$distribution,
mvnorm = copula.normalLLH3(arglist = arglist),
mvt = copula.studentLLH3(arglist = arglist))
mfilter$tailusigma = tail(sigma(filterlist), 50)
mfilter$tailuresids = tail(residuals(filterlist), 50)
mfilter$tailuret = tail(data, 50)
Rt = mfilter$R
Ht = array( 0, dim = c(m, m, N) )
stdresid = matrix(0, nrow = N, ncol = m)
if( !is.null(cluster) ){
clusterExport(cluster, c("sig", "Rt", "res"), envir = environment())
tmp = parLapply(cluster, as.list(1:N), fun = function(i){
tmph = diag( sig[i, ] ) %*% Rt %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
} else{
tmp = lapply(as.list(1:N), FUN = function(i){
tmph = diag( sig[i, ] ) %*% Rt %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
}
mfilter$H = Ht
allnames = NULL
for(i in 1:m){
allnames = c(allnames, paste("[",cnames[i],"].", rownames(midx[midx[,i]==1,i, drop = FALSE]), sep = ""))
}
garchnames = allnames
dccnames = rownames(midx[midx[,m+1]==1,m+1, drop = FALSE])
if(!is.null(dccnames)){
allnames = c(garchnames, paste("[Joint]", rownames(midx[midx[,m+1]==1,m+1, drop = FALSE]), sep = ""))
} else{
allnames = garchnames
}
mfilter$coef = mpars[which(midx==1, arr.ind = TRUE)]
names(mfilter$coef) = allnames
mfilter$garchnames = garchnames
mfilter$dccnames = dccnames
mfilter$stdresid = stdresid
# make model list to return some usefule information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfilter$realizedVol = realizedVol
mfilter$timer = Sys.time() - tic
#model$sfit = sfit
ans = new("cGARCHfilter",
mfilter = mfilter,
model = model)
return(ans)
}
# ToDo : change the likelihood to take into account the n.old argument for the cov calculation.
.cgarchfilter.dynamic = function(spec, data, out.sample = 0, filter.control = list(n.old = NULL),
spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"),
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...)
{
tic = Sys.time()
model = spec@model
umodel = spec@umodel
n.old = filter.control$n.old
#-----------------------------------------------------------------------------------
# Data Extraction
m = dim(data)[2]
if( is.null( colnames(data) ) ) cnames = paste("Asset_", 1:m, sep = "") else cnames = colnames(data)
colnames(umodel$modelinc) = cnames
xdata = .extractmdata(data)
if(!is.numeric(out.sample))
stop("\ndccfilter-->error: out.sample must be numeric\n")
if(as.numeric(out.sample) < 0)
stop("\ndccfilter-->error: out.sample must be positive\n")
n.start = round(out.sample, 0)
n = dim(xdata$data)[1]
if( (n-n.start) < 100)
stop("\ndccfilter-->error: function requires at least 100 data\n points to run\n")
data = xdata$data
index = xdata$index
period = xdata$period
# save the data to the model spec
model$modeldata$data = data
model$modeldata$index = index
model$modeldata$period = period
T = model$modeldata$T = n - n.start
model$modeldata$n.start = n.start
model$modeldata$asset.names = cnames
#-----------------------------------------------------------------------------------
transformation = model$modeldesc$transformation
#-----------------------------------------------------------------------------------
# VAR model
if( spec@model$modelinc[1]>0 ){
tmp = mvmean.varfilter(model = model, data = data, varcoef = varcoef,
T = T, out.sample = out.sample)
model = tmp$model
zdata = tmp$zdata
mu = tmp$mu
p = tmp$p
N = tmp$N
} else{
zdata = data
ex = NULL
}
T = dim(zdata)[1] - out.sample
#-----------------------------------------------------------------------------------
if(is.null(filter.control$n.old)) n.old = T
#-----------------------------------------------------------------------------------
# Univariate GARCH filter
if(model$modelinc[1]>0){
for(i in 1:m){
if(sum(umodel$modelinc[1:6,i])>0)
stop("\nThe user supplied univariate spec object has a non-null mean specification but VAR already chosen for mean filtration!!!")
}
}
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, umodel$vt)
filterlist = multifilter(multifitORspec = mspec, data = xts(zdata, index), out.sample = out.sample,
cluster = cluster, n.old = n.old, realizedVol = realizedVol, ...)
if(spec@model$modelinc[1]>0) model$mu = mu else model$mu = fitted(filterlist)
model$residuals = res = residuals(filterlist)
model$sigma = sig = sigma(filterlist)
stdresid = res/sig
modelinc = model$modelinc
# create full par matrix
midx = .fullinc2(modelinc, umodel)
mpars = midx*0
eidx = midx
unipars = sapply(filterlist@filter, FUN = function(x) x@filter$ipars[x@filter$ipars[,3]==1,1])
if(is.list(unipars)){
for(i in 1:length(unipars)){
uninames = names(unipars[[i]])
mpars[uninames, i] = unipars[[i]]
}
} else{
uninames = rownames(unipars)
mpars[uninames, 1:NCOL(unipars)] = unipars
}
# add include pars from DCC spec (includes the fixed pars)
mpars[which(midx[,m+1]==1, arr.ind = TRUE), m+1] = as.numeric( model$pars[model$pars[,3]==1,1] )
# DCC parameters
ipars = model$pars
estidx = as.logical( ipars[,3] )
npars = sum(estidx)
model$spd.control = spd.control
arglist = list()
arglist$verbose = FALSE
arglist$spd.control = spd.control
arglist$cluster = cluster
arglist$cnames = cnames
arglist$m = m
arglist$T = T
arglist$zdata = zdata
arglist$index = index
arglist$realizedVol = realizedVol
arglist$model = model
arglist$filterlist = filterlist
arglist$umodel = umodel
arglist$midx = midx
arglist$eidx = eidx
arglist$mpars = mpars
arglist$ipars = ipars
arglist$estidx = estidx
arglist$stdresid = stdresid
arglist$npars = npars
arglist$filter.control = filter.control
arglist$n.old = n.old
mfilter = list()
arglist$returnType = "ALL"
mfilter = switch(model$modeldesc$distribution,
mvnorm = copula.tvnormalLLH3(arglist = arglist),
mvt = copula.tvstudentLLH3(arglist = arglist))
mfilter$tailusigma = tail(sigma(filterlist), 50)
mfilter$tailuresids = tail(residuals(filterlist), 50)
mfilter$tailuret = tail(data, 50)
N = dim(stdresid)[1]
Rt = mfilter$R
Ht = array( 0, dim = c(m, m, N) )
stdresid = matrix(0, nrow = N, ncol = m)
if( !is.null(cluster) ){
clusterExport(cluster, c("sig", "Rt", "res"), envir = environment())
tmp = parLapply(cluster, as.list(1:N), fun = function(i){
tmph = diag( sig[i, ] ) %*% Rt[[i]] %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
} else{
tmp = lapply(as.list(1:N), FUN = function(i){
tmph = diag( sig[i, ] ) %*% Rt[[i]] %*% diag( sig[i, ] )
zz = eigen( tmph )
sqrtzz = ( zz$vectors %*% diag( sqrt( zz$values ) ) %*% solve( zz$vectors ) )
tmpz = as.numeric( res[i, ] %*% solve( sqrtzz ) )
return( list( H = tmph, Z = tmpz ) )
})
for(i in 1:N){
Ht[,,i] = tmp[[i]]$H
stdresid[i,] = tmp[[i]]$Z
}
}
mfilter$H = Ht
allnames = NULL
for(i in 1:m){
allnames = c(allnames, paste("[",cnames[i],"].", rownames(midx[midx[,i]==1,i, drop = FALSE]), sep = ""))
}
garchnames = allnames
dccnames = rownames(midx[midx[,m+1]==1,m+1, drop = FALSE])
allnames = c(garchnames, paste("[Joint]",dccnames, sep = ""))
mfilter$coef = mpars[which(midx==1, arr.ind = TRUE)]
names(mfilter$coef) = allnames
mfilter$garchnames = garchnames
mfilter$dccnames = dccnames
mfilter$stdresid = stdresid
# make model list to return some useful information which
model$mpars = mpars
model$ipars = ipars
model$pars[,1] = ipars[,1]
model$midx = midx
model$eidx = eidx
model$umodel = umodel
mfilter$realizedVol = realizedVol
mfilter$timer = Sys.time() - tic
#model$sfit = sfit
ans = new("cGARCHfilter",
mfilter = mfilter,
model = model)
return(ans)
}
# allow returning only the scenario (return) matrix
.cgarchsim = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL,
preresiduals = NULL, prereturns = NULL, preR = NULL, preQ = NULL,
preZ = NULL, rseed = NULL, mexsimdata = NULL, vexsimdata = NULL,
cluster = NULL, only.density = FALSE, prerealized = NULL, ...)
{
timecopula = fit@model$modeldesc$timecopula
if(timecopula){
ans = .cgarchsim2(fit = fit, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
startMethod = startMethod[1], presigma = presigma, preresiduals = preresiduals,
prereturns = prereturns, preR = preR, preQ = preQ, preZ = preZ,
rseed = rseed, mexsimdata = mexsimdata, vexsimdata = vexsimdata,
cluster = cluster, only.density = only.density, prerealized = prerealized)
} else{
ans = .cgarchsim1(fit = fit, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
startMethod = startMethod[1], presigma = presigma, preresiduals = preresiduals,
prereturns = prereturns, preR = preR, rseed = rseed,
mexsimdata = mexsimdata, vexsimdata = vexsimdata, cluster = cluster,
only.density = only.density, prerealized = prerealized)
}
return(ans)
}
.cgarchsim1 = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), preresiduals = NULL,
presigma = NULL, prereturns = NULL, preR = NULL, rseed = NULL,
mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, only.density = FALSE,
prerealized = NULL, ...)
{
# first generate the copula random uniform numbers (static copula)
# ures --> transform --> zres
# pass zres to GARCH simulation
# pass if needed to VAR model
if( is.null(rseed) ){
rseed = as.integer(runif(m.sim, 1, Sys.time()))
} else {
if(length(rseed) == 1) rseed = c(rseed[1], rseed[1]+seq_len(m.sim))
rseed = as.integer( rseed[1:m.sim] )
}
model = fit@model
umodel = model$umodel
T = model$modeldata$T
Data = model$modeldata$data[1:T, ]
m = dim(Data)[2]
nsim = n.sim + n.start
mg = model$maxgarchOrder
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel,
umodel$modeldesc$vsubmodel, umodel$modeldata$mexdata,
umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, NULL)
for(i in 1:m){
# variance targeting case
if(model$midx["omega",i]==0){
setfixed(mspec@spec[[i]]) = as.list(c("omega"=model$mpars["omega",i], model$mpars[which(model$midx[,i]==1), i]))
} else{
setfixed(mspec@spec[[i]]) = as.list(model$mpars[which(model$midx[,i]==1), i])
}
}
#setfixed(specx)<-as.list(model$mpars[model$midx[,m+1]==1,m+1])
uncv = sapply(mspec@spec, FUN = function(x) uncvariance(x))
if( !is.null(presigma) ){
if( !is.matrix(presigma) )
stop("\ncgarchsim-->error: presigma must be a matrix.")
if( dim(presigma)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for presigma.")
if( dim(presigma)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for presigma (need ", mg, " rows.", sep = ""))
} else{
if(startMethod == "sample"){
presigma = fit@mfit$tailusigma
} else{
presigma = matrix(uncv, ncol = m, nrow = 25)
}
}
if(is.null(preR)){
Rbar = rcor(fit)
} else{
if(dim(preR)[1] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(dim(preR)[2] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(any(diag(preR) != 1))
stop("\ncgarchsim-->error: preR diagonals must be 1.\n")
Rbar = preR
}
if( !is.null(prereturns) ){
if( !is.matrix(prereturns) )
stop("\ncgarchsim-->error: prereturns must be a matrix.")
if( dim(prereturns)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prereturns.")
if( dim(prereturns)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prereturns (need ", mg, " rows.", sep = ""))
} else{
prereturns = tail(model$modeldata$data[1:T, ], 25)
}
if(fit@model$umodel$modeldesc$vmodel[1]=="realGARCH"){
if( !is.null(prerealized) ){
if( !is.matrix(prerealized) )
stop("\ncgarchsim-->error: prerealized must be a matrix.")
if( dim(prerealized)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prerealized.")
if( dim(prerealized)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prerealized (need ", mg, " rows.", sep = ""))
} else{
# might want to include the option for unconditional
# value of realized (unexported in rugarch)
prerealized = tail(fit@mfit$realizedVol[1:T,], 25)
}
} else{
prerealized = matrix(NA, ncol = m, nrow = 25)
}
ures = .sample.copula(model, Qbar = NULL, preQ = NULL, Rbar = Rbar,
Nbar = NULL, preZ = NULL, n.sim = n.sim, n.start = n.start,
m.sim = m.sim, rseed = rseed, cluster = cluster)
# ures = [0,1] copula random numbers
# now transform back into margins for use in garch
transformation = model$modeldesc$transformation
distu = umodel$modeldesc$distribution
zres = array(NA, dim = c(nsim, m, m.sim))
# parallel:
if( !is.null(cluster) ){
ssfit = model$sfit
minc = umodel$modelinc
mpars = model$mpars
sxres = fit@mfit$stdresid
clusterEvalQ(cluster, loadNamespace('rmgarch'))
clusterEvalQ(cluster, loadNamespace('rugarch'))
if(transformation == "spd"){
clusterExport(cluster, c("ures", "mpars", "minc", "m",
"sxres", "ssfit", "transformation"), envir = environment())
} else{
clusterExport(cluster, c("ures", "mpars", "minc", "m", "sxres",
"transformation"), envir = environment())
}
clusterExport(cluster, c(".qparametric", ".qempirical",".qspd"), envir = environment())
mtmp = parLapply(cluster, as.list(1:m.sim), fun = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures[,,i], ncol = m), pars = mpars, modelinc = minc),
empirical = .qempirical(matrix(ures[,,i], ncol = m), sxres),
spd = .qspd(matrix(ures[,,i], ncol = m), sfit = ssfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
} else{
mtmp = lapply(as.list(1:m.sim), FUN = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures[,,i], ncol = m),
pars = model$mpars, modelinc = umodel$modelinc),
empirical = .qempirical(matrix(ures[,,i], ncol = m), fit@mfit$stdresid),
spd = .qspd(matrix(ures[,,i], ncol = m), sfit = model$sfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
}
# Now we have the innovations which we feed back into the garch routine to simulate
simRes = simX = simR = simQ = simH = simSeries = vector(mode = "list", length = m.sim)
if( !is.null(cluster) ){
tailres = fit@mfit$tailuresids
clusterEvalQ(cluster, loadNamespace('rugarch'))
clusterExport(cluster, c("mspec", "n.sim", "n.start", "m.sim",
"startMethod", "zres", "presigma", "tailres",
"preresiduals", "prereturns", "model",
"mexsimdata", "vexsimdata","prerealized"), envir = environment())
simlist = parLapply(cluster, as.list(1:m), fun = function(i){
maxx = mspec@spec[[i]]@model$maxOrder;
htmp = rugarch::ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(tailres[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
return(list(h = h, x = x))
})
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% Rbar %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
} else{
simlist = vector(mode="list", length=m)
for(i in 1:m){
maxx = mspec@spec[[i]]@model$maxOrder
htmp = ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(fit@mfit$tailuresids[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
simlist[[i]] = list(h = h, x = x)
}
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% Rbar %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
}
if( model$modelinc[1]>0 ){
simRes = simX
simX = mvmean.varsim(model = model, Data = Data, res = simX,
mexsimdata = mexsimdata, prereturns = prereturns, m.sim = m.sim,
n.sim = n.sim, n.start = n.start, startMethod = startMethod,
cluster = cluster)
} else{
# reshape
for(j in 1:m.sim) simX[[j]] = tail(simX[[j]], n.sim)
}
msim = list()
if(only.density){
msim$simX = simX
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
} else{
msim$simH = simH
msim$simR = simR
msim$simQ = simQ
msim$simX = simX
msim$simRes = simRes
msim$simZ = zres
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
}
model$only.density = only.density
# need to run gc and rm since 'memory accumulates here'
if(exists("simX")) rm(simX)
if(exists("simRes")) rm(simRes)
if(exists("simH")) rm(simH)
if(exists("H")) rm(H)
if(exists("simlist")) rm(simlist)
if(exists("tmpH")) rm(tmpH)
if(exists("mtmp")) rm(mtmp)
if(exists("simxX")) rm(simxX)
if(exists("zres")) rm(zres)
if(exists("ures")) rm(ures)
gc(verbose = FALSE)
ans = new("cGARCHsim",
msim = msim,
model = model)
return( ans )
}
# time varying copula simulation
.cgarchsim2 = function(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL, preresiduals = NULL,
prereturns = NULL, preR = NULL, preQ = NULL, preZ = NULL, rseed = NULL,
mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, only.density = FALSE,
prerealized = NULL, ...)
{
# first generate the copula random uniform numbers (static copula)
# ures --> transform --> zres
# pass zres to GARCH simulation
# pass if needed to VAR model
if( is.null(rseed) ){
rseed = as.integer(runif(m.sim, 1, Sys.time()))
} else {
if(length(rseed) == 1) rseed = c(rseed[1], rseed[1]+seq_len(m.sim))
rseed = as.integer( rseed[1:m.sim] )
}
model = fit@model
umodel = model$umodel
T = model$modeldata$T
Data = model$modeldata$data[1:T, ]
m = dim(Data)[2]
nsim = n.sim + n.start
mg = fit@model$maxgarchOrder
mspec = .makemultispec(umodel$modelinc, umodel$modeldesc$vmodel, umodel$modeldesc$vsubmodel,
umodel$modeldata$mexdata, umodel$modeldata$vexdata, umodel$start.pars,
umodel$fixed.pars, NULL)
for(i in 1:m){
# variance targeting case
if(model$midx["omega",i]==0){
setfixed(mspec@spec[[i]]) = as.list(c("omega"=model$mpars["omega",i], model$mpars[which(model$midx[,i]==1), i]))
} else{
setfixed(mspec@spec[[i]]) = as.list(model$mpars[which(model$midx[,i]==1), i])
}
}
uncv = sapply(mspec@spec, FUN = function(x) uncvariance(x))
if( !is.null(presigma) ){
if( !is.matrix(presigma) )
stop("\ncgarchsim-->error: presigma must be a matrix.")
if( dim(presigma)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for presigma.")
if( dim(presigma)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for presigma (need ", mg, " rows.", sep = ""))
} else{
if(startMethod == "sample"){
presigma = fit@mfit$tailusigma
} else{
presigma = matrix(uncv, ncol = m, nrow = 25)
}
}
if(is.null(preR)){
Rbar = last(rcor(fit), 1)[,,1]
} else{
if(dim(preR)[1] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(dim(preR)[2] != m)
stop("\ncgarchsim-->error: wrong dimension for preR\n")
if(any(diag(preR) != 1))
stop("\ncgarchsim-->error: preR diagonals must be 1.\n")
Rbar = preR
}
Qbar = fit@mfit$Qbar
if(model$modelinc[6]>0){
Nbar = fit@mfit$Nbar
} else{
Nbar = matrix(0, m, m)
}
if( !is.null(prereturns) ){
if( !is.matrix(prereturns) )
stop("\ncgarchsim-->error: prereturns must be a matrix.")
if( dim(prereturns)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prereturns.")
if( dim(prereturns)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prereturns (need ", mg, " rows.", sep = ""))
} else{
prereturns = tail(model$modeldata$data[1:T, ], 25)
}
if(fit@model$umodel$modeldesc$vmodel[1]=="realGARCH"){
if( !is.null(prerealized) ){
if( !is.matrix(prerealized) )
stop("\ncgarchsim-->error: prerealized must be a matrix.")
if( dim(prerealized)[2] != m )
stop("\ncgarchsim-->error: wrong column dimension for prerealized.")
if( dim(prerealized)[1] != mg )
stop(paste("\ncgarchsim-->error: wrong row dimension for prerealized (need ", mg, " rows.", sep = ""))
} else{
# might want to include the option for unconditional
# value of realized (unexported in rugarch)
prerealized = tail(fit@mfit$realizedVol[1:T,], 25)
}
} else{
prerealized = matrix(NA, ncol = m, nrow = 25)
}
# This step is KEY if we are to replicate the 1-ahead filter
if(startMethod == "sample"){
if(is.null(preZ)){
preZ = matrix(tail(fit@mfit$Z, fit@model$maxdccOrder), ncol = m)
} else{
preZ = matrix(preZ[1,], ncol = m, nrow = fit@model$maxdccOrder)
}
if(is.null(preQ)){
preQ = fit@mfit$Qt[[length(fit@mfit$Qt)]]
} else{
preQ = preQ
}
} else{
if(is.null(preZ)){
preZ = matrix(0, ncol = m, nrow = fit@model$maxdccOrder)
} else{
preZ = matrix(preZ[1,], ncol = m, nrow = fit@model$maxdccOrder)
}
if(is.null(preQ)){
preQ = Rbar
} else{
preQ = preQ
}
}
ures = .sample.copula(model, Qbar = Qbar, preQ = preQ, Rbar = Rbar, Nbar = Nbar,
preZ = preZ, n.sim = n.sim, n.start = n.start, m.sim = m.sim,
rseed = rseed, cluster = cluster)
# ures = [0,1] copula random numbers
# now transform back into margins for use in garch
transformation = model$modeldesc$transformation
distu = umodel$modeldesc$distribution
zres = array(NA, dim = c(nsim, m, m.sim))
# parallel:
if( !is.null(cluster) ){
ssfit = model$sfit
minc = umodel$modelinc
mpars = model$mpars
sxres = fit@mfit$stdresid
clusterEvalQ(cluster, require('rmgarch'))
if(transformation == "spd"){
clusterExport(cluster, c("ures", "mpars", "minc",
"m", "sxres", "ssfit", "transformation"),
envir = environment())
} else{
clusterExport(cluster, c("ures", "mpars", "minc",
"m", "sxres", "ssfit", "transformation"),
envir = environment())
}
clusterExport(cluster, c(".qparametric", ".qempirical",".qspd"), envir = environment())
mtmp = parLapply(cluster, as.list(1:m.sim), fun = function(i){
switch(transformation,
parametric = .qparametric(matrix(ures$Usim[,,i], ncol = m), pars = mpars, modelinc = minc),
empirical = .qempirical(matrix(ures$Usim[,,i], ncol = m), sxres),
spd = .qspd(matrix(ures$Usim[,,i], ncol = m), sfit = ssfit))
})
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
} else{
mtmp = lapply(as.list(1:m.sim), FUN = function(i) switch(transformation,
parametric = .qparametric(matrix(ures$Usim[,,i], ncol = m), pars = model$mpars, modelinc = umodel$modelinc),
empirical = .qempirical(matrix(ures$Usim[,,i], ncol = m), fit@mfit$stdresid),
spd = .qspd(matrix(ures$Usim[,,i], ncol = m), sfit = model$sfit)))
for(i in 1:m.sim) zres[,,i] = mtmp[[i]]
}
# Now we have the innovations which we feed back into the garch routine to simulate
simRes = simX = simR = simQ = simH = simSeries = vector(mode = "list", length = m.sim)
#if( is.null(fit@mfit$vrmodel) ){
# if(is.null(prereturns)) prereturns = fit@mfit$tailuret
#}
if( !is.null(cluster) ){
tailres = fit@mfit$tailuresids
clusterEvalQ(cluster, loadNamespace('rugarch'))
clusterExport(cluster, c("mspec", "n.sim", "n.start", "m.sim",
"startMethod", "zres", "presigma", "tailres",
"preresiduals", "prereturns", "model", "mexsimdata",
"vexsimdata", "rseed","prerealized"), envir = environment())
simR = ures$simR
simlist = parLapply(cluster, as.list(1:m), fun = function(i){
maxx = mspec@spec[[i]]@model$maxOrder;
htmp = rugarch::ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(tailres[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
return(list(h = h, x = x))
})
if(!only.density){
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% simR[[i]][,,j] %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
} else{
simR = ures$simR
#simQ = lapply(mtmp, FUN = function(x) if(is.matrix(x$Q)) array(x$Q, dim = c(m, m, n.sim)) else last(x$Q, n.sim))
simlist = vector(mode="list", length=m)
for(i in 1:m){
maxx = mspec@spec[[i]]@model$maxOrder
htmp = ugarchpath(mspec@spec[[i]], n.sim = n.sim + n.start, n.start = 0, m.sim = m.sim,
custom.dist = list(name = "sample", distfit = matrix(zres[,i,1:m.sim], ncol = m.sim)),
presigma = tail(presigma[,i], maxx),
preresiduals = if( is.null(preresiduals) ) tail(fit@mfit$tailuresids[,i], maxx) else tail(preresiduals[,i], maxx),
prereturns = if(model$modelinc[1]==0) tail(prereturns[,i], maxx) else NA,
mexsimdata = if(model$modelinc[1]==0) mexsimdata[[i]] else NULL, vexsimdata = vexsimdata[[i]],
prerealized = tail(prerealized[,i], maxx))
h = matrix(tail(htmp@path$sigmaSim^2, n.sim), nrow = n.sim)
x = matrix(htmp@path$seriesSim, nrow = n.sim + n.start)
simlist[[i]] = list(h = h, x = x)
}
if(!only.density){
H = array(NA, dim = c(n.sim, m, m.sim))
tmpH = array(NA, dim = c(m, m, n.sim))
for(i in 1:n.sim) H[i,,] = t(sapply(simlist, FUN = function(x) as.numeric(x$h[i,])))
for(i in 1:m.sim){
for(j in 1:n.sim){
tmpH[ , , j] = sqrt(diag( H[j, , i]) ) %*% simR[[i]][,,j] %*% sqrt(diag( H[j, , i] ) )
}
simH[[i]] = tmpH
}
}
if(model$modelinc[1]>0){
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(simxX[,,i], nrow = n.sim+n.start)
} else{
simxX = array(NA, dim = c(n.sim+n.start, m, m.sim))
for(i in 1:m.sim) simxX[,,i] = sapply(simlist, FUN = function(x) as.numeric(x$x[,i]))
simX = vector(mode = "list", length = m.sim)
for(i in 1:m.sim) simX[[i]] = matrix(tail(matrix(simxX[,,i], ncol = m), n.sim), nrow = n.sim)
}
}
if( model$modelinc[1]>0 ){
simRes = simX
simX = mvmean.varsim(model = model, Data = Data, res = simX,
mexsimdata = mexsimdata, prereturns = prereturns, m.sim = m.sim,
n.sim = n.sim, n.start = n.start, startMethod = startMethod,
cluster = cluster)
} else{
# reshape
for(j in 1:m.sim) simX[[j]] = tail(simX[[j]], n.sim)
}
msim = list()
if(only.density){
msim$simX = simX
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
} else{
msim$simH = simH
msim$simR = simR
msim$simX = simX
msim$simRes = simRes
msim$simZ = zres
msim$rseed = rseed
model$n.sim = n.sim
model$m.sim = m.sim
model$n.start = n.start
model$startMethod = startMethod[1]
}
model$only.density = only.density
# need to run gc and rm since 'memory accumulates here'
if(exists("simX")) rm(simX)
if(exists("simRes")) rm(simRes)
if(exists("simH")) rm(simH)
if(exists("H")) rm(H)
if(exists("simlist")) rm(simlist)
if(exists("tmpH")) rm(tmpH)
if(exists("mtmp")) rm(mtmp)
if(exists("simxX")) rm(simxX)
if(exists("zres")) rm(zres)
if(exists("ures")) rm(ures)
if(exists("simR")) rm(simR)
gc(verbose = FALSE)
ans = new("cGARCHsim",
msim = msim,
model = model)
return( ans )
}
.fullinc2 = function(modelinc, umodel){
m = dim(umodel$modelinc)[2]
# the par matrix will have the max parameter vis Asset (if an asset does not have
# the parameter it will be zero...for matrix estimation.
# the index places the solver estimates into their place in the matrix
###################################################################################
# Asset1 .... Asset2 .... AssetN ... ........ Joint
# mu
# ar
# ma
# arfima
# archm
# mxreg
# omega
# alpha
# beta
# gamma
# delta
# lambda
# eta1
# eta2
# vxreg
# skew
# shape
# ghlambda
# xi
# C
# dcca
# dccb
# dccg
# mshape
# mskew
vecmax = rep(0, 19)
names(vecmax) = rownames(umodel$modelinc[1:19,])
vecmax = apply(umodel$modelinc, 1, FUN = function(x) max(x) )
maxOrder = apply(umodel$modelinc, 2, FUN = function(x) max(c(x[2], x[3], x[8], x[9])))
sumv = 19 + sum(pmax(1, vecmax[c(2,3,6,8,9,10,11,12,13,15,16)])) - 11
tmpmat = matrix(0, ncol = m+1, nrow = sumv)
nx = 0
pnames = NULL
# mu
if(vecmax[1]>0){
tmpmat[1, 1:m] = umodel$modelinc[1,]
}
nx = nx + max(1, vecmax[1])
pnames = c(pnames, "mu")
# ar
if(vecmax[2]>0){
for(i in 1:vecmax[2]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[2,] >= i)
pnames = c(pnames, paste("ar", i, sep = ""))
}
} else{
pnames = c(pnames, "ar")
}
nx = nx + max(1, vecmax[2])
# ma
if(vecmax[3]>0){
for(i in 1:vecmax[3]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[3,] >= i)
pnames = c(pnames, paste("ma", i, sep = ""))
}
} else{
pnames = c(pnames, "ma")
}
nx = nx + max(1, vecmax[3])
# arfima
if(vecmax[4]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[4, ]
}
nx = nx + max(1, vecmax[4])
pnames = c(pnames, "arfima")
# archm no allowed
nx = nx + max(1, vecmax[5])
pnames = c(pnames, "archm")
# mxreg
if(vecmax[6]>0){
for(i in 1:vecmax[6]){
tmpmat[nx+i, 1:m] = as.integer(umodel$modelinc[6, ] >= i)
pnames = c(pnames, paste("mxreg", i, sep = ""))
}
} else{
pnames = c(pnames, "mxreg")
}
nx = nx + max(1, vecmax[6])
# omega
if(vecmax[7]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[7, ]
}
nx = nx + max(1, vecmax[7])
pnames = c(pnames, "omega")
# alpha
if(vecmax[8]>0){
for(i in 1:vecmax[8]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[8, ] >= i)
pnames = c(pnames, paste("alpha", i, sep = ""))
}
} else{
pnames = c(pnames, "alpha")
}
nx = nx + max(1, vecmax[8])
# beta
if(vecmax[9]>0){
for(i in 1:vecmax[9]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[9, ] >= i)
pnames = c(pnames, paste("beta", i, sep = ""))
}
} else{
pnames = c(pnames, "beta")
}
nx = nx + max(1, vecmax[9])
# gamma
if(vecmax[10]>0){
for(i in 1:vecmax[10]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[10, ] >= i)
pnames = c(pnames, paste("gamma", i, sep = ""))
}
} else{
pnames = c(pnames, "gamma")
}
nx = nx + max(1, vecmax[10])
# eta1
if(vecmax[11]>0){
for(i in 1:vecmax[11]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[11, ] >= i)
pnames = c(pnames, paste("eta1", i, sep = ""))
}
} else{
pnames = c(pnames, "eta1")
}
nx = nx + max(1, vecmax[11])
# eta2
if(vecmax[12]>0){
for(i in 1:vecmax[12]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[12, ] >= i)
pnames = c(pnames, paste("eta2", i, sep = ""))
}
} else{
pnames = c(pnames, "eta2")
}
nx = nx + max(1, vecmax[12])
# delta
if(vecmax[13]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[13, ]
}
nx = nx + max(1, vecmax[13])
pnames = c(pnames, "delta")
# lambda
if(vecmax[14]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[14, ]
}
nx = nx + max(1, vecmax[14])
pnames = c(pnames, "lambda")
# vxreg
if(vecmax[15]>0){
for(i in 1:vecmax[15]){
tmpmat[nx+i, 1:m] = as.integer( umodel$modelinc[15, ] >= i)
pnames = c(pnames, paste("vxreg", i, sep = ""))
}
} else{
pnames = c(pnames, "vxreg")
}
nx = nx + max(1, vecmax[15])
# skew
if(vecmax[16]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[16, ]
}
nx = nx + max(1, vecmax[16])
pnames = c(pnames, "skew")
# shape
if(vecmax[17]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[17, ]
}
nx = nx + max(1, vecmax[17])
pnames = c(pnames, "shape")
# skew
if(vecmax[18]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[18, ]
}
nx = nx + max(1, vecmax[18])
pnames = c(pnames, "ghlambda")
if(vecmax[19]>0){
tmpmat[nx+1, 1:m] = umodel$modelinc[19, ]
}
nx = nx + max(1, vecmax[19])
pnames = c(pnames, "xi")
sumdcc = 6 + sum(pmax(1, modelinc[c(3,4,5,6,7,8)])) - 6
tmpmat = rbind(tmpmat, matrix(0, ncol = m+1, nrow = sumdcc))
if(modelinc[3]>0){
for(i in 1:modelinc[3]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("C", i, sep = ""))
}
} else{
pnames = c(pnames, "C")
}
nx = nx + max(1, modelinc[3])
if(modelinc[4]>0){
for(i in 1:modelinc[4]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dcca", i, sep = ""))
}
} else{
pnames = c(pnames, "dcca")
}
nx = nx + max(1, modelinc[4])
if(modelinc[5]>0){
for(i in 1:modelinc[5]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dccb", i, sep = ""))
}
} else{
pnames = c(pnames, "dccb")
}
nx = nx + max(1, modelinc[5])
if(modelinc[6]>0){
for(i in 1:modelinc[6]){
tmpmat[nx+i, m+1] = 1
pnames = c(pnames, paste("dccg", i, sep = ""))
}
} else{
pnames = c(pnames, "dccg")
}
nx = nx + max(1, modelinc[6])
# we allow for possibility of vector valued shape and skew parameters for future expansion
if(modelinc[7]>0){
for(i in 1:modelinc[7]){
tmpmat[nx+i, m+1] = 1
if(modelinc[7]>1) pnames = c(pnames, paste("mshape", i, sep = "")) else pnames = c(pnames, "mshape")
}
} else{
pnames = c(pnames, "mshape")
}
nx = nx + max(1, modelinc[7])
if(modelinc[8]>0){
for(i in 1:modelinc[8]){
tmpmat[nx+i, m+1] = 1
if(modelinc[8]>1) pnames = c(pnames, paste("mskew", i, sep = "")) else pnames = c(pnames, "mskew")
}
} else{
pnames = c(pnames, "mskew")
}
# NOW we have and indicator matrix
colnames(tmpmat) = c(paste("Asset", 1:m, sep = ""), "Joint")
rownames(tmpmat) = pnames
return(tmpmat)
}
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