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List:       r-sig-finance
Subject:    Re: [R-SIG-Finance] VaR and ES in PerformanceAnalytics
From:       financial engineer <fin_engr () hotmail ! com>
Date:       2011-10-24 19:25:16
Message-ID: COL118-W46E277B5172E91590DDF05EFEF0 () phx ! gbl
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Brian,

I appreciate the clarification.

That explains why the ES@99% is exactly the same as the VaR at that level. \
Interestingly, as Michael pointed out, he's experiencing the same with SPY data too - \
I guess it would be worth testing on other names as well, but it seems like there's a \
high probability that the ES & VaR could be the same at those high levels of \
confidence. 

I do agree that too much precision is probably not worth it.


thanks much,
Bobby


P.S. Also wondering if there are any modules in R to conduct risk analysis when \
options are added to the portfolio...


> 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 14:06:30 -0500
> 
> On Mon, 2011-10-24 at 14:20 -0400, financial engineer wrote:
> > 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. 
> 
> I've attached the output of chart.VaRSensitivity on the MCO data.
> 
> You can see how the modified VaR tracks to the historical VaR very well,
> much better than the Gaussian approximation.  
> 
> You can also see where the modified ES breaks down, at around 98%, and
> starts climbing towards zero.  The operational assumption will return
> the modified VaR.
> 
> You can also see that at high probability levels modified ES will give
> larger loss estimates than the historical.  I tend to think conservative
> risk estimates are a good thing, but others have opinions that differ on
> the desirability of this.
> 
> I previously gave my rationale for using lower p values than p=.99 with
> daily or lower frequency data.
> 
> Regards,
> 
> - Brian
> 
> -- 
> Brian G. Peterson
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
 		 	   		  
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