问As.vector中的错误(数据)：没有将此S4类强制向向量的方法

EN

graphics.off()        # clean up graphic window

#install.packages("fGarch")

library(rmgarch)
library(tseries)
library(stats)
library(fGarch)
library(rugarch)
library(quantmod)

getSymbols(Symbols = c('PG','CVX','CSCO'),from="2005-01-01",  to="2020-04-17",
           env=parent.frame(),
           reload.Symbols = FALSE,
           verbose = FALSE,
           warnings = TRUE,
           src="yahoo",
           symbol.lookup = TRUE,
           auto.assign = getOption('getSymbols.auto.assign', TRUE))


Pt=cbind(PG$PG.Adjusted,CVX$CVX.Adjusted,CSCO$CSCO.Adjusted) 
rt = 100 * diff(log(Pt))
rt=na.omit(rt)
rm(CSCO,CVX,PG)
rt_ts=ts(rt)
n=nrow(rt_ts)
N=ncol(rt_ts)
#O-GARCH:
Sigma = cov(rt_ts);  # Covariance matrix
P = cor(rt_ts)       # correlation matrix

# spectral decomposition
SpectralDec = eigen(Sigma, symmetric=TRUE)  
V = SpectralDec$vectors                            # eigenvector matrix
V
lambda = SpectralDec$values                        # eigenvalues
lambda
Lambda = diag(lambda)                 # Eigenvalues on the diagonal
print(Sigma - V %*% Lambda %*% t(V), digits = 3)   # Sigma - V Lambda V' = 0  
print(V  %*%   t(V), digits = 3) # V'V = I  
print(t(V)  %*% V, digits = 3)   # VV' = I  


f = ts(as.matrix(rt_ts) %*% V);  
cov(f) # diagonal matrix with lambda on the diagonal

ht.f = matrix(0, n, N)
for (i in 1:N)
{
  fit = garchFit(~ garch(1,1), data =f[, i], trace = FALSE);
  summary(fit);
  ht = volatility(fit, type = "h");
  ht.f[, i] = ht;
} 
ht.f=ts(ht.f) ```