

Hello, I sent this message a couple of times to rhelp group but unfortunately did not get any response that would be helpful... I have a rather complex problem... I will have to explain
everything in detail because I cannot solve it by myself...i just ran
out of ideas. So here is what I want to do: I take quotes of two
indices  S&P500 and DJ. And my first aim is to estimate
coefficients of the DCCGARCH model for them. This is how I do it:
library(tseries)p1 = get.hist.quote(instrument = "^gspc",start = "20050107",end = "20090904",compression = "w", quote="AdjClose")
p2 = get.hist.quote(instrument = "^dji",start = "20050107",end = "20090904",compression = "w", quote="AdjClose")
p = cbind(p1,p2)y = diff(log(p))*100y[,1] = y[,1]mean(y[,1])
y[,2] = y[,2]mean(y[,2])T = length(y[,1])
library(ccgarch)library(fGarch)
f1 = garchFit(~ garch(1,1), data=y[,1],include.mean=FALSE)f1 = f1@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$outDCCrho = dccresults$DCC[,2]
matplot(DCCrho, type='l')dccresults$out deliver me the estimated coefficients of the DCCGARCH model. And here is my first question: How can I check if these coefficients are significant or not? How can I test them for significance?
and the second one:
What is actually
dccpara and why do I get totally different DCCalpha and DCCbeta
coefficients if I change dccpara from c(0.2,0.6) to, let's say, c(0.01,
0.98) ? What determines which values should be chosen?Ok. This would be it when it comes to DCCGARCH.
Now, using conditional correlation obtained from the DCCGARCH
model, I want to test for structural shifts in conditional correlations.
To be precise, I want to test whether the conditional correlations
significantly increase in the turmoil period / during the Subprime
crisis.
The regression model is AR(1)GARCH(1,1), using a dummy variable specified as:
*** the equations, you can find in the attachment ***
where the first equation is the conditional correlation among the two indices during the Subprime crisis, Dt is a dummy variable for the turmoil period, and the second equation (hij,t) is the conditional variance of eij,t
The aim is, of course, to find the estimates of the regression model
on structural shifts in the conditional correlations obtained in the
DCCGARCH model. I found an information that there is no function
for AR(1)GARCH(1,1) regression model. That's why it has to be done in
two steps:
1) estimate the AR parameters 2) estimate the GARCH part of the model on the residuals from the AR model And this would be my rather poor idea of how to do it... library(timeSeries)
library(fSeries)library(tseries)step1 = arma(DCCrho, order = c(1,0), include.intercept = TRUE)
step1$res
step11 = na.remove(step1$res)step2 = garch (step11, order = c(1,1), include.intercept = TRUE)
To be honest I have no clue
how to do it. I don't even now why do I get a missing value as a result
of step1 (step1$res[1]) and how to account for it? Above, I just removed
it but then I have a smaller number of observations...and this is
probably wrong.
And then these GARCH estimates on the residuals...does the code for that make sense at all?
Hopefully, someone will find time to
give me a hand because I have to solve the problem and I reached the
point where I cannot move forward without someone's help. There is not
much information on how to apply DCCGARCH model and AR(1)GARCH(1,1)
regression model in the Internet. Hopefully, some of you are familiar
with it.
Thank you very much in advance, people of good will, for looking at what I wrote and helping me.
Best regards Marcin
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Hello,I am new to Garch and have some problem when using garchFit in fGarchI have a simple data series as:y < c(0.,0.0067691,0.00681523,0.00210637,0.00581551,0.00211864,0.00477328,0.00212427,0.01658235,0.00921166,0.00867214,0.01139768,0.00544367,0.00811034,0.00161421,0.00539085,0.00753908,0.01950224,0.00328767,0.00547347,0.01152909,0.,0.00110497,0.00110619,0.01537647,0.00272109,0.00054333,0.02030248,0.0050014,0.00666299,0.01592128,0.00327333,0.01032899,0.00054069,0.0010805,0.00753097,0.00107124,0.00482963,0.00647601,0.00054157)
When I use this in g < garchFit(~arma(1,0)+garch(1,1),y)
I got the warning msg: In sqrt(diag(fit$cvar)) : NaNs produced
I guess this is to do with optimizer (nlminb) here but I m not sure what kind of control parameter I should try to handle this.
Thank you.
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Hi Yihao, not very sure on your situation, but are you sure that your data have any Garch effect at all? Primafacie it seems that your data dont have. Why you want to fit garch model in such case?
Thanks and regards,
_____________________________________________________
Arun Kumar Saha, FRM
QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
Visit me at: http://in.linkedin.com/in/ArunFRM_____________________________________________________


Hi, Arunthanks for your reply. I checked my dataset and figured seems there was problem in my dataset. Thank you.
Best,
Yihao
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Not real familiar with DCCGARCH but I will look into it so you are not alone. I used the rgarch package here with good results
http://timelyportfolio.blogspot.com/2011/05/wonderfulnewblogtimeseriesireland.htmlKent
On May 24, 2011, at 5:53 AM, Marcin P?ï¿½ciennik < [hidden email]> wrote:
> Hello,
> I sent this message a couple of times to rhelp group but unfortunately did not get any response that would be helpful...
>
> I have a rather complex problem... I will have to explain everything in detail because I cannot solve it by myself...i just ran out of ideas. So here is what I want to do:
> I take quotes of two indices  S&P500 and DJ. And my first aim is to estimate coefficients of the DCCGARCH model for them. This is how I do it:
>
>
> library(tseries)
> p1 = get.hist.quote(instrument = "^gspc",start = "20050107",end = "20090904",compression = "w", quote="AdjClose")
> p2 = get.hist.quote(instrument = "^dji",start = "20050107",end = "20090904",compression = "w", quote="AdjClose")
> p = cbind(p1,p2)
> y = diff(log(p))*100
> y[,1] = y[,1]mean(y[,1])
> y[,2] = y[,2]mean(y[,2])
> T = length(y[,1])
>
> library(ccgarch)
> library(fGarch)
>
> f1 = garchFit(~ garch(1,1), data=y[,1],include.mean=FALSE)
> f1 = f1@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$out deliver me the estimated coefficients of the DCCGARCH model. And here is my first question:
> How can I check if these coefficients are significant or not? How can I test them for significance?
>
> and the second one:
> What is actually dccpara and why do I get totally different DCCalpha and DCCbeta coefficients if I change dccpara from c(0.2,0.6) to, let's say, c(0.01, 0.98) ? What determines which values should be chosen?
>
> Ok. This would be it when it comes to DCCGARCH.
>
> Now, using conditional correlation obtained from the DCCGARCH model, I want to test for structural shifts in conditional correlations. To be precise, I want to test whether the conditional correlations significantly increase in the turmoil period / during the Subprime crisis.
> The regression model is AR(1)GARCH(1,1), using a dummy variable specified as:
>
>
>
>
> *** the equations, you can find in the attachment ***
>
>
>
>
>
> where the first equation is the conditional correlation among the two indices during the Subprime crisis, Dt is a dummy variable for the turmoil period, and the second equation (hij,t) is the conditional variance of eij,t
>
>
> The aim is, of course, to find the estimates of the regression model on structural shifts in the conditional correlations obtained in the DCCGARCH model.
>
> I found an information that there is no function for AR(1)GARCH(1,1) regression model. That's why it has to be done in two steps:
> 1) estimate the AR parameters
> 2) estimate the GARCH part of the model on the residuals from the AR model
>
> And this would be my rather poor idea of how to do it...
>
>
> library(timeSeries)
> library(fSeries)
> library(tseries)
> step1 = arma(DCCrho, order = c(1,0), include.intercept = TRUE)
> step1$res
> step11 = na.remove(step1$res)
> step2 = garch (step11, order = c(1,1), include.intercept = TRUE)
>
>
> To be honest I have no clue how to do it. I don't even now why do I get a missing value as a result of step1 (step1$res[1]) and how to account for it? Above, I just removed it but then I have a smaller number of observations...and this is probably wrong.
> And then these GARCH estimates on the residuals...does the code for that make sense at all?
>
>
> Hopefully, someone will find time to give me a hand because I have to solve the problem and I reached the point where I cannot move forward without someone's help. There is not much information on how to apply DCCGARCH model and AR(1)GARCH(1,1) regression model in the Internet. Hopefully, some of you are familiar with it.
>
> Thank you very much in advance, people of good will, for looking at what I wrote and helping me.
>
> Best regards
> Marcin
>
>
> <Equations.pdf>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rsigfinance>  Subscriberposting only. If you want to post, subscribe first.
>  Also note that this is not the rhelp list where general R questions should go.
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