Description Usage Arguments Value Note Examples
This function estimates the parameters(alpha,beta) and time-varying correlation matrices(Rt) of DCC-GARCH model.
1 2 | dcc_estimation(ini.para = c(0.05, 0.93), ht, residuals, method = c("COV",
"LS", "NLS"), ts = 1)
|
ini.para |
initial DCC-GARCH parameters(alpha,beta) of optimization |
ht |
matrix of conditional variance vectors |
residuals |
matrix of residual(de-mean) returns |
method |
shrinkage method of unconditional correlation matrix(Cov:sample,LS:Linear Shrinkage,NLS:NonLinear Shrinkage) |
ts |
ts how many time series are you taking(dufalut:1 latest value) |
time-varying correlations(Rt) and the result of estimation
Rt are vectorized values of the conditional correlation matrix(Rt) until time t(ts) for each row.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | library(rugarch)
library(xdcclarge)
#load data
data(us_stocks)
n<-3
Rtn<-log(us_stocks[-1,1:n]/us_stocks[-nrow(us_stocks),1:n])
# Step 1:GARCH Parameter Estimation with rugarch
spec = ugarchspec()
mspec = multispec( replicate(spec, n = n) )
fitlist = multifit(multispec = mspec, data = Rtn)
ht<-sigma(fitlist)^2
residuals<-residuals(fitlist)
# Step 2:DCC-GARCH Parameter Estimation with xdcclarge
DCC<-dcc_estimation(ini.para=c(0.05,0.93) ,ht ,residuals)
#Time varying correlation matrix Rt at time t
(Rt<-matrix(DCC$dcc_Rt,n,n))
## Not run:
#If you want Rt at time t-s,then
s<-10
DCC<-dcc_estimation(ini.para=c(0.05,0.93) ,ht ,residuals,ts = s)
matrix(DCC$cdcc_Rt[s,],n,n)
## End(Not run)
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