develop my understanding.

I sincerely appreciate your time and effort.

> A few years ago, on the suggestion of Pat, I implemented an option

> which allows to choose whether to use all the data for the initialization

> of the variance recursion or some other value e.g. for exponential

> smoothing

> backast. This can be found in the fit.control option (of ugarchfit) under

> 'rec.init':

>

> From the documentation:

>

> "The rec.init option determines the type of initialization for the

> variance recursion.

> Valid options are ‘all’ which uses all the values for the unconditional

> variance

> calculation, an integer greater than or equal to 1 denoting the number

> of data

> points to use for the calculation, or a positive numeric value less than

> one

> which determines the weighting for use in an exponential smoothing

> backcast."

>

> This is only for the variance recursion initialization, and not the

> conditional mean.

>

> Best,

>

> Alexios

>

> On 05/10/2015 01:49, Patrick Burns wrote:

> > I haven't studied the issue with

> > ARIMA, but it is my belief that it

> > is even less of an issue there.

> >

> > Maybe someone on the list has looked

> > into it and has a better sense of the

> > sensitivity -- rather than being like

> > the rest of us and not worrying about

> > it because no one else does.

> >

> > Pat

> >

> > On 05/10/2015 04:43, Samit Paul wrote:

> >> Thanks a lot Pat,

> >>

> >> I was more concerned about the second issue which you have pointed out

> >> well. From the link given (thanks again for the same), I understand that

> >> if the number of observations are more (around 2000), choice of starting

> >> value won't matter much in conditional variance estimation by GARCH(1,1)

> >> model.

> >>

> >> But is the same logic applicable for conditional mean estimation with

> >> the help of ARIMA model, too? Or do I have to take any precaution for

> >> the same?

> >>

> >> Best regards,

> >>

> >> Samit Paul

> >>

> >>

> >> On Sun, Oct 4, 2015 at 11:54 PM, Patrick Burns <

[hidden email]
> >> <mailto:

[hidden email]>> wrote:

> >>

> >> I have two possible interpretations

> >> of "starting values":

> >>

> >> 1) initial values of coefficients given

> >> to the optimizer of the likelihood

> >>

> >> 2) the value of the conditional variance

> >> at the time point before the first observation

> >>

> >> If you are talking about the first, I

> >> think you have little to worry about.

> >> The default optimization in 'rugarch' is

> >> reasonably good. But there are options

> >> to use different optimizers if you want to

> >> check the quality of the optimum.

> >>

> >> If you are talking about the second, then

> >> that won't be an issue as long as you have

> >> enough observations to make estimating a

> >> garch model useful. See:

> >>

> >>

>

http://www.portfolioprobe.com/2012/07/06/a-practical-introduction-to-garch-modeling/> >>

> >> Pat

> >>

> >>

> >> On 04/10/2015 16:52, Samit Paul wrote:

> >>

> >> Dear R users,

> >>

> >> I am trying to estimate conditional mean and variance of a

> >> financial return

> >> series using UGARCHSPEC and UGARCHFIt function of "rugarch"

> >> package. I am

> >> trying to fit basic ARMA(1,1)-GARCH(1,1) with Student - t

> >> distribution.

> >>

> >> Now, I am not sure how the starting values are considered in

> >> this case or

> >> whether I need to set it manually. Since the starting value

> >> is very

> >> important for the estimation purpose, there could be some robust

> >> method for

> >> calculation of the same.

> >>

> >> Any help in this regard will be highly appreciated.

> >>

> >> Regards,

> >>

> >> Samit Paul

> >>

> >> [[alternative HTML version deleted]]

> >>

> >> _______________________________________________

> >>

[hidden email] <mailto:

[hidden email]
> >

> >> mailing list

> >>

https://stat.ethz.ch/mailman/listinfo/r-sig-finance> >> -- Subscriber-posting only. If you want to post, subscribe

> >> first.

> >> -- Also note that this is not the r-help list where general R

> >> questions should go.

> >>

> >>

> >> --

> >> Patrick Burns

> >>

[hidden email] <mailto:

[hidden email]>

> >>

http://www.burns-stat.com> >>

http://www.portfolioprobe.com/blog> >> twitter: @burnsstat @portfolioprobe

> >>

> >>

> >

>

>

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