Luenberger discusses the task of creating risk-neutral probabilities from
any set of observed returns. I cannot find the specific reference in his
book (Investment Science) but there is a discussion paper by Winston
(google) that applies his method using Excel and @Risk to a sample of
Microsoft returns. I think it would be relatively simple to program it in R.

Charles Ward

On 24 November 2011 19:43, Brian G. Peterson <brian@braverock.com> wrote:

> On Thu, 2011-11-24 at 16:19 +0100, Joachim Breit wrote:
> > Thank you for the interesting link.
> >
> > I agree that you can use a (fit of a) stable distribution for sampling
> > purposes. But please: Why not simply use the raw data and sample from
> > that? What could be a better starting point? Again, it is clear that
> > you cannot use the raw return series as it comes off your data feed;
> > there is a need for adjusting. But there cannot be a better fit to the
> > raw data than the raw data itself...
>
> Of course you start with sampling from your data, but if all you do is
> sample from the data, with no 'noise', then all you are doing is
> smoothing out the prior observations.  This may lead you to a false
> sense of security, and give you an incorrect idea of how likely a fat
> tailed event is.  So by adding noise from a stable or other fat tailed
> distribution (skewed Student's T is also popular), you are believing
> that your prior data is both stationary and fully representative.
>
> Regards,
>
>  - Brian
>
> --
> Brian G. Peterson
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
>
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