Re: st: rolling moments

[annoporci <annoporci@gmail.com>]

I forgot to add, this is my best shot:

 rolling sd=r(sd) skewness=r(skewness) kurtosis=r(kurtosis), window (60)  
clear: summarize, detail

The problem is that it generates a lot of gaps because of missing data.


On Sat, 22 Dec 2012 23:28:22 +0800, annoporci <annoporci@gmail.com> wrote:

> Dear Statalist,
>
> I have financial data whose statistical properties I'd like to analyze.  
> I'm new to this so any pointer would be appreciated.
>
> I would like to do the following:
>
> plot a moving average of the mean/sd/skewness/kurtosis, where either I  
> would use a fixed horizon, e.g. 60 observations (add one drop one as you  
> move with time) or I would use exponentially declining weights on  
> distant observations, for a fixed predetermined weight parameter.
>
> I have found user-contributed programs to do that for the mean/sd, but  
> not for the skewness and kurtosis. I'm especially interested in the  
> kurtosis. In fact, I would like to also compute semi-kurtosis, e.g.  
> semi-kurtosis for negative returns or for returns below average.
>
> I could probably work this out on my own by writing down the formulas in  
> terms of the first 4 moments, but I have read exchanges that suggest  
> that efficiency issues may be important, so I'd like to start on the  
> right track, and would thus appreciate your suggestions.
>
> my dataset has the date and the daily return for different portfolios  
> x1, x2, x3, etc. over a 30 year period, so efficiency in the code is  
> likely to be necessary.
>
> many thanks,
>
> Patrick.
>
> my main reference:
> http://www.stata.com/statalist/archive/2008-08/msg00153.html
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