search for: dccrho

Displaying 4 results from an estimated 4 matches for "dccrho".

Did you mean: 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...