Displaying 4 results from an estimated 4 matches for "dccrho".
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dcaro
2011 May 15
4
DCC-GARCH model
...1@fit$coef
f2 = garchFit(~ garch(1,1), data=y[,2],include.mean=FALSE)
f2 = f2@fit$coef
a = c(f1[1], f2[1])
A = diag(c(f1[2],f2[2]))
B = diag(c(f1[3], f2[3]))
dccpara = c(0.2,0.6)
dccresults = dcc.estimation(inia=a, iniA=A, iniB=B, ini.dcc=dccpara,dvar=y,
model="diagonal")
dccresults$out
DCCrho = dccresults$DCC[,2]
matplot(DCCrho, type='l')
dccresults$out deliver me the estimated coefficients of the DCC-GARCH model.
And here is my first question:
How can I check if these coefficients are significant or not? How can I test
them for significance?
second question would be:
Is this...
2011 May 12
2
DCC-GARCH model and AR(1)-GARCH(1,1) regression model
...it$coef
f2 = garchFit(~ garch(1,1), data=y[,2],include.mean=FALSE)
f2 = f2 at fit$coef
a = c(f1[1], f2[1])
A = diag(c(f1[2],f2[2]))
B = diag(c(f1[3], f2[3]))
dccpara = c(0.2,0.6)
dccresults = dcc.estimation(inia=a, iniA=A, iniB=B, ini.dcc=dccpara,dvar=y,
model="diagonal")
dccresults$out
DCCrho = dccresults$DCC[,2]
matplot(DCCrho, type='l')
dccresults$out deliver me the estimated coefficients of the DCC-GARCH model.
And here is my first question:
How can I check if these coefficients are significant or not? How can I test
them for significance?
second question would be:
Is this...
2011 May 10
0
DCC-GARCH model and AR(1)-GARCH(1, 1) regression model - help needed..
...1@fit$coef
f2 = garchFit(~ garch(1,1), data=y[,2],include.mean=FALSE)
f2 = f2@fit$coef
a = c(f1[1], f2[1])
A = diag(c(f1[2],f2[2]))
B = diag(c(f1[3], f2[3]))
dccpara = c(0.2,0.6)
dccresults = dcc.estimation(inia=a, iniA=A, iniB=B, ini.dcc=dccpara,dvar=y,
model="diagonal")
dccresults$out
DCCrho = dccresults$DCC[,2]
matplot(DCCrho, type='l')
dccresults$out deliver me the estimated coefficients of the DCC-GARCH model.
And here is my first question:
How can I check if these coefficients are significant or not? How can I test
them for significance?
second question would be:
Is this...
2011 Aug 23
0
Dummy variable regression
...ons
significantly increased after the crisis began. For that reason I have to
use dummy variable regression. ‘1’ will stand for the turbulent period,
‘0’for the tranquility period.
This is how I programmed it in R:
d <- rep(0,991)
for (i in 814:922)
d[i]<-1;
step1 = arima(DCCrho, order = c(1,0,0), xreg=d, include.mean = TRUE)
step2 = garch (step1$res, order = c(1,1), include.intercept = TRUE)
the observations 814:922 are from the turbulent period that is why for them
the dummy variable takes the value ‘1’. Below, there are 2 steps that
apparently have to be made in ord...