Starting value of conditional mean and variance

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 <patrick at burns-stat.com
<mailto:patrick at burns-stat.com>> 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

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