
Brian,

I appreciate your response and the clarification. I shall ponder over it.

Meanwhile, rather than sending a data file, I am attaching the code I ran to generate R.MCO which I used in the calcs. below

MCO = get.hist.quote("MCO", start = "2010-01-04", end = "2011-10-17", quote = "AdjClose", compression = "d")
R.MCO = Return.calculate(MCO, method="compound")
R.MCO = as.xts(R.MCO)

I'd be keen to read your specific response.

thanks,
Bobby

> Subject: Re: [R-SIG-Finance] VaR and ES in PerformanceAnalytics
> From: brian@braverock.com
> To: fin_engr@hotmail.com
> CC: r-sig-finance@r-project.org
> Date: Mon, 24 Oct 2011 13:11:25 -0500
> 
> On Mon, 2011-10-24 at 11:49 -0400, financial engineer wrote:
> > I ran the VaR and ES using the below, and am trying to understand why the VaR(99%) 
> > is exactly equal to ES(99%). Is that how it is supposed to be.....
> 
> You didn't include your data to make this reproducible, so I need to
> speak in generalities.
> 
> From the documentation:
> 
>  Modified expected shortfall should always be higher than modified
>  Value at Risk. Due to estimation problems, this might not always
>  be the case. Set operational = TRUE to replace modified ES with
>  modified VaR in the (exceptional) case where the modified ES is
>  smaller than modified VaR.
> 
> If you're seeing this, I expect your data is highly skewed and/or
> kurtotic.
> 
> Cornish Fisher Modified VaR can exhibit what Jorion referred to as
> 'wrong tailed  behavior', migrating rapidly to zero or infinity.  While
> this is a problem, it is only a problem if you aren't paying attention.
> I use this as an indicator that I either don't have enough data or that
> my data is likely unreliable.  It is also often an indication that your
> preferred probability (99%) is too high for a reliable answer.
> 
> Look at function chart.VaRSensitivity to see how the VaR and ES play out
> at different probability thresholds. 
> 
> The good news is that it's really obvious when this happens, you can
> easily see it in the chart, and tell where you don't and probably
> shouldn't have confidence in your estimating powers.
> 
> Just because some other methodologies will give you an answer that
> claims to be precise doesn't mean that they are truly doing so.  Many
> many people have written about the pitfalls of looking for too high a
> precision in VaR/ES estimates.  I, for example, find 95% to be a good
> number on daily return data, as this indicates a 1 in 20 'bad day', so
> you can interpret the 95% ES as approximately your 'average one really
> bad day a month' on daily return data.  I have a colleague who uses 92%
> on monthly return data to estimate the 'average really bad month once a
> year' on his portfolios.
> 
> I prefer 'approximately correct' to 'precisely wrong' statistics.
> 
> I could speak in more specifics about the characteristics of your data
> with a reproducible example.
> 
> Regards,
> 
>   - Brian   
> 
> -- 
> Brian G. Peterson
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
> 
 		 	   		  
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