Brian,

Thanks for your answer. I am using scenarios in optimization. Scenarios are
drawn from a multivariate distribution that is built by fitting univariate
return distributions and then gluing the distibutions with a copula. Thus
the non-normality of assets should be taken into account.. I am assuming the
reason for my "findings" are that the CVaR puts "only" a linear penalty for
returns below the quantile and I am wondering how optimal this is for a
(highly) risk averse investor...

-John

2010/1/27 Brian G. Peterson <brian@braverock.com>

>  John Seppänen wrote:
>
>> Hi all!
>>
>> My question itself is not related to R so my apologies for that. I ran
>> scenario optimizations in S-Plus with respect to variance and CVaR as a
>> risk
>> measures (based on Scherer & Martin's (2005) book). My assets where
>> mostly negatively skewed and fat-tailed and I expected the resulting
>> portfolio from CVaR-optimization to have less tail-risk than the the
>> portfolio from variance-optimization. However, I noticed the opposite
>> which
>> is surpirising because the markowitz optimization is often accused of
>> being tail-risk maximization when assets are negatively skewed (e.g. hedge
>> funds).
>>
>> In many sources CVaR is said to be "the measure" for downside risk
>> measurement. However, I am not able to find a discussion about how well
>> CVaR
>> relates with the utility maximization framework.. who should optimize with
>> respect to CVaR if it increases the tail-risk? Computational easiness is
>> not
>> a good reason..   Any references or thoughts about the subject would be
>> appreciated..
>>
>>
> Use modified CVaR instead.  It handles non-normal distributions.
>
> And, being an *R* finance list, all that functionality is already available
> in R, including optimizing using modified CVaR as one of your objectives.
>
> Cheers,
>
>  - Brian
>
> --
> Brian G. Peterson
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
>
>
>

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