[R-SIG-Finance] Option valuation for arbitrary distribution using monte carlo simulation

[Joachim Breit]

This is the paper:
http://www.realoptions.org/papers1999/WINSTONOptions.PDF

Am 25.11.2011 09:48, schrieb Charles Ward:
> 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 at braverock.com
> <mailto:brian at 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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