第一次在这里提出问题，我会尽力明确 - 但如果我应该提供更多信息，请告诉我！其次，这是一个很长的问题......希望为某人解决简单;）！所以使用“R”，我根据一些论文（Manera et al. 2012）对多元 GARCH 模型进行建模。
我在均值方程中使用外部回归器对恒定条件相关 (CCC) 和动态条件相关 (DCC) 模型进行建模；使用“R”版本 3.0.1 和包“rugarch”版本 1.2-2 用于带有外部回归器的单变量 GARCH，以及用于 CCC/DCC 模型的“ccgarch”包（版本 0.2.0-2）。（我目前正在研究“rmgarch”包——但它似乎只适用于 DCC，我也需要 CCC 模型。）
我的模型的平均方程有问题。在我上面提到的论文中，CCC 和 DCC 模型之间的平均方程的参数估计发生了变化！而且我不知道我将如何在 R 中做到这一点......（目前，查看谷歌并查看 Tsay 的书“金融时间序列分析”和 Engle 的书“预测相关性”以找到我的错误）
我所说的“我的平均方程不会在 CCC 和 DCC 模型之间改变”的意思是：我使用包 rugarch 为我的 n=5 时间序列指定单变量 GARCH。然后，我使用 GARCH 的估计参数（ARCH + GARCH 项）并将它们用于 CCC 和 DCC 函数“eccc.sim()”和“dcc.sim()”。然后，从 eccc.estimation() 和 dcc.estimation() 函数，我可以检索方差方程的估计值以及相关矩阵。但不适用于平均方程。
我只发布了单变量模型和 CCC 模型的 R 代码（可重现的和我的原始代码）。已经谢谢你阅读我的帖子了！！！！！！
注意：在下面的代码中，“data.repl”是dim 843x22 的“zoo”对象（9 个每日商品返回系列和解释变量系列）。多元 GARCH 仅适用于 5 个系列。
可重现的代码：
# libraries:
library(rugarch)
library(ccgarch)
library(quantmod)
# Creating fake data:
dataRegr <- matrix(rep(rnorm(3149, 11, 1),1), ncol=1, nrow=3149)
dataFuelsLag1 <- matrix(rep(rnorm(3149, 24, 8),2), ncol=2, nrow=3149)
#S&P 500 via quantmod and Yahoo Finance
T0 <- "2000-06-23"
T1 <- "2012-12-31"
getSymbols("^GSPC", src="yahoo", from=T0, to=T1)
sp500.close <- GSPC[,"GSPC.Close"],
getSymbols("UBS", src="yahoo", from=T0, to=T1)
ubs.close <- UBS[,"UBS.Close"]
dataReplic <- merge(sp500.close, ubs.close, all=TRUE)
dataReplic[which(is.na(dataReplic[,2])),2] <- 0 #replace NA
### (G)ARCH modelling ###
#########################
# External regressors: macrovariables and all fuels+biofuel Working's T index
ext.regr.ext <- dataRegr
regre.fuels <- cbind(dataFuelsLag1, dataRegr)
### spec of GARCH(1,1) spec with AR(1) ###
garch11.fuels <- as.list(1:2)
for(i in 1:2){
garch11.fuels[[i]] <- ugarchspec(mean.model = list(armaOrder=c(1,0),
external.regressors = as.matrix(regre.fuels[,-i])))
}
### fit of GARCH(1,1) AR(1) ###
garch11.fuels.fit <- as.list(1:2)
for(i in 1:2){
garch11.fuels.fit[[i]] <- ugarchfit(garch11.fuels[[i]], dataReplic[,i])
}
##################################################################
#### CCC fuels: with external regression in the mean eqaution ####
##################################################################
nObs <- length(data.repl[-1,1])
coef.unlist <- sapply(garch11.fuels.fit, coef)
cccFuels.a <- rep(0.1, 2)
cccFuels.A <- diag(coef.unlist[6,])
cccFuels.B <- diag(coef.unlist[7, ])
cccFuels.R <- corr.test(data.repl[,fuels.ind], data.repl[,fuels.ind])$r
# model=extended (Jeantheau (1998))
ccc.fuels.sim <- eccc.sim(nobs = nObs, a=cccFuels.a, A=cccFuels.A,
B=cccFuels.B, R=cccFuels.R, model="extended")
ccc.fuels.eps <- ccc.fuels.sim$eps
ccc.fuels.est <- eccc.estimation(a=cccFuels.a, A=cccFuels.A,
B=cccFuels.B, R=cccFuels.R,
dvar=ccc.fuels.eps, model="extended")
ccc.fuels.condCorr <- round(corr.test(ccc.fuels.est$std.resid,
ccc.fuels.est$std.resid)$r,digits=3)
我的原始代码：
### (G)ARCH modelling ###
#########################
# External regressors: macrovariables and all fuels+biofuel Working's T index
ext.regr.ext <- as.matrix(data.repl[-1,c(10:13, 16, 19:22)])
regre.fuels <- cbind(fuel.lag1, ext.regr.ext) #fuel.lag1 is the pre-lagged series
### spec of GARCH(1,1) spec with AR(1) ###
garch11.fuels <- as.list(1:5)
for(i in 1:5){
garch11.fuels[[i]] <- ugarchspec(mean.model = list(armaOrder=c(1,0),
external.regressors = as.matrix(regre.fuels[,-i])))
}# regre.fuels[,-i] => "-i" because I model an AR(1) for each mean equation
### fit of GARCH(1,1) AR(1) ###
garch11.fuels.fit <- as.list(1:5)
for(i in 1:5){
j <- i
if(j==5){j <- 7} #because 5th "fuels" is actually column #7 in data.repl
garch11.fuels.fit[[i]] <- ugarchfit(garch11.fuels[[i]], as.matrix(data.repl[-1,j])))
}
#fuelsLag1.names <- paste(cmdty.names[fuels.ind], "(-1)")
fuelsLag1.names <- cmdty.names[fuels.ind]
rowNames.ext <- c("Constant", fuelsLag1.names, "Working's T Gasoline", "Working's T Heating Oil",
"Working's T Natural Gas", "Working's T Crude Oil",
"Working's T Soybean Oil", "Junk Bond", "T-bill",
"SP500", "Exch.Rate")
ic.n <- c("Akaike", "Bayes")
garch11.ext.univSpec <- univ.spec(garch11.fuels.fit, ols.fit.ext, rowNames.ext,
rowNum=c(1:15), colNames=cmdty.names[fuels.ind],
ccc=TRUE)
##################################################################
#### CCC fuels: with external regression in the mean eqaution ####
##################################################################
# From my GARCH(1,1)-AR(1) model, I extract ARCH and GARCH
# in order to model a CCC GARCH model:
nObs <- length(data.repl[-1,1])
coef.unlist <- sapply(garch11.fuels.fit, coef)
cccFuels.a <- rep(0.1, length(fuels.ind))
cccFuels.A <- diag(coef.unlist[17,])
cccFuels.B <- diag(coef.unlist[18, ])
#based on Engle(2009) book, page 31:
cccFuels.R <- corr.test(data.repl[,fuels.ind], data.repl[,fuels.ind])$r
# model=extended (Jeantheau (1998))
# "allow the squared errors and variances of the series to affect
# the dynamics of the individual conditional variances
ccc.fuels.sim <- eccc.sim(nobs = nObs, a=cccFuels.a, A=cccFuels.A,
B=cccFuels.B, R=cccFuels.R, model="extended")
ccc.fuels.eps <- ccc.fuels.sim$eps
ccc.fuels.est <- eccc.estimation(a=cccFuels.a, A=cccFuels.A,
B=cccFuels.B, R=cccFuels.R,
dvar=ccc.fuels.eps, model="extended")
ccc.fuels.condCorr <- round(corr.test(ccc.fuels.est$std.resid,
ccc.fuels.est$std.resid)$r,digits=3)
colnames(ccc.fuels.condCorr) <- cmdty.names[fuels.ind]
rownames(ccc.fuels.condCorr) <- cmdty.names[fuels.ind]
lowerTri(ccc.fuels.condCorr, rep=NA)