[R-SIG-Finance] Starting value of conditional mean and variance

[Samit Paul]

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>
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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>>
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> --
> Patrick Burns
> patrick at burns-stat.com
> http://www.burns-stat.com
> http://www.portfolioprobe.com/blog
> twitter: @burnsstat @portfolioprobe
>

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