问题描述
我正在尝试预测Copula Garch模型.我曾尝试将dccforecast函数与cGARCHfit一起使用，但事实证明是错误的，说没有适用于cgARCHfit类对象的'dccforecast'方法.那么，实际上我们如何预测dcc copula garch模型?
I'm trying to forecast the Copula Garch Model. I have tried to use the dccforecast function with the cGARCHfit but it turns out to be error saying that there is no applicable method for 'dccforecast' applied to an object of class cGARCHfit. So how do actually we forecast the dcc copula garch model?
我有以下可复制的代码.
I have the following reproducible code.
library(zoo)
library(rugarch)
library(rmgarch)
data("EuStockMarkets")
EuStockLevel <- as.zoo(EuStockMarkets)[,c("DAX","CAC","FTSE")]
EuStockRet <- diff(log(EuStockLevel))
# DCC timecopula MVN
uspec = ugarchspec(mean.model = list(armaOrder = c(0,0)), variance.model = list(garchOrder = c(1,1), model = "sGARCH", variance.targeting=FALSE), distribution.model = "norm")
spec1 = cgarchspec(uspec = multispec( replicate(3, uspec) ), asymmetric = TRUE, distribution.model = list(copula = "mvnorm", method = "Kendall", time.varying = TRUE, transformation = "parametric"))
fit1 = cgarchfit(spec1, data = EuStockRet, cluster = NULL, solver.control=list(trace=1))
print(fit1)
> fit.copula = cgarchfit(spec1, data = EuStockRet, out.sample = 120, solver = "solnp", solver.control =list(),fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),cluster = NULL, fit =NULL, VAR.fit = NULL)
> dcc.copula.focast=dccforecast(fit.copula, n.ahead = 1, n.roll = 0)
Error in UseMethod("dccforecast") : no applicable method for 'dccforecast' applied to an object of class "c('cGARCHfit', 'mGARCHfit', 'GARCHfit', 'rGARCH')"
感谢您的帮助.
谢谢
推荐答案
DCC预测仅适用于dccfits.您可以尝试使用cGARCHsim函数，或者放开Kendall方法并进行dccfit.尽管如果要长期预测，使用cGARCHsim进行预测可能会很麻烦.
DCC forecasts only work with dccfits. You can try the function cGARCHsim or let go of the Kendall method and go for a dccfit. Though forecasting using cGARCHsim can be a pain if you want to forecast for a longer period ahead.
请参阅:
??cGARCHsim
由于没有明确的预测例程，用户应使用此方法>通过模拟提前1来逐步建立提前n的预测，>获得收益，sigma，Rho等的均值并将其提供给下一个>以模拟回合为起始值. "rmgarch.tests"文件夹包含用于说明这一点的特定示例.
Since there is no explicit forecasting routine, the user should use this method >for incrementally building up n-ahead forecasts by simulating 1-ahead, >obtaining the means of the returns, sigma, Rho etc and feeding them to the next >round of simulation as starting values. The ‘rmgarch.tests’ folder contains >specific examples which illustrate this particular point.
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