WIth respect to mgarchBEKK, it had been seemingly (actually??) been
abandoned a long time ago.  I currently host a patched version here:

http://www.quantmod.com/download/mgarchBEKK/

Clear case of "your mileage may vary"

Best,
Jeff

On Tue, Aug 2, 2011 at 11:01 AM, alexios <alexios@4dscape.com> wrote:

> Dear Zoe,
>
> The study of volatility spillover effects between markets is usually
> studied using multivariate GARCH models, which take into account lagged as
> well as own shocks and volatility. See for example the BEKK, VEK and
> Generalized DCC models. For bivariate modelling this 'should' be
> computationally feasible/straightforward, but you should look outside the
> rgarch package at present for these type of multivariate GARCH models (hint:
> mgarchBEKK, ccgarch).
>
> Regards,
>
> Alexios
>
>
> On 02/08/2011 15:55, XI ZHANG wrote:
>
>> sorry to bother all,
>> I am recently doing a topic about spillover effect between two markets.
>> And I want to use AR(1)-GJR-GARCH(1,1)-M Model. I find that rgarch
>> package has *functions* for univariate GARCH model, including GJR.
>> My GJR model is in the attachment.
>>
>>
>>
>> where Ɛ_(j,t-1)^2 is the squared volitility of counterpart market,
>> Ɛ_(i,t-1)^2  is the squared volitility of domestic market. S represent
>> good news (S=0)or bad news(S=1). and i write the R code as followings,
>>
>>  variance.model=list(model="**gjrGARCH",garchOrder=c(1,1),
>>>
>> external.regressors=lagvoljp)
>>
>>> mean.model=list(armaOrder=c(1,**0),include.mean=TRUE,**
>>> garchInMean=TRUE,inMeanType=2,**arfima=FALSE,external.**regressors=ljp)
>>>
>>
>>  spec=ugarchspec(variance.**model=variance.model,mean.**model=mean.model,
>>>
>> distribution.model="norm")
>>
>>> fit3.sh=ugarchfit(data=shl,**spec=spec,out.sample=0,solver=**"solnp")
>>>
>>
>> But then I figure out that in normal GJR model, the fomula is
>> and the function written in rgarch package is also based on it
>> which means it catches news from domestic market.
>>
>> But in my model, it should catch news from counterpart market,
>> I was thinking of taking S part as an another external regressors
>> since if Ɛ<0,S=1 elseS=0,so I write codes as
>>
>>  resjp[resjp>0]<-0
>>>
>>
>>  voljpgjr<-resjp^2
>>>
>>
>>  variance.model3sh=list(model="**fGARCH",garchOrder=c(1,1),
>>>
>> submodel="GARCH", external.regressors=cbind(**lagvoljp,voljpgjr))
>>
>>  mean.model3sh=list(armaOrder=**c(1,0),include.mean=TRUE,**
>>> garchInMean=TRUE,inMeanType=2,**arfima=FALSE,external.**
>>> regressors=lagsh)
>>>
>>
>>  spec3sh=ugarchspec(variance.**model=variance.model3sh,mean.**
>>> model=mean.model3sh,
>>>
>> distribution.model="norm")
>>
>>  fit3.sh=ugarchfit(data=shl,**spec=spec3sh,out.sample=0,**solver="solnp")
>>>
>>
>>  fit3.sh
>>>
>>
>>
>> But the result is still disappointed that the coefficient which reflect
>> volatility spillover effect is always not significant and almost 0. I
>> tried other dataset, but the result is the same.
>>
>>
>> Here is my question,
>> How could i change the code to let the S reflects the effect from
>> counterpart market? Are there any codes I write have problems and
>> un-reasonable part?
>>
>> Thisi is important for me!
>> Thank you so much!
>>
>> Sincerely,
>> Zoe
>>
>>
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>>
>
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-- 
Jeffrey Ryan
jeffrey.ryan@lemnica.com

www.lemnica.com
www.esotericR.com

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