[R-SIG-Finance] Winsorization

ngottlieb at marinercapital.com ngottlieb at marinercapital.com
Thu Sep 18 16:27:38 CEST 2008


Have you looked at Rousseau's, minimum volume ellipsoids (MVE) for
handling outliers?

Curious if so how you found this for handling outliers? 


-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Brian G.
Sent: Thursday, September 18, 2008 6:47 AM
To: Patrick Burns
Cc: r-sig-finance at stat.math.ethz.ch; ??????
Subject: Re: [R-SIG-Finance] Winsorization

On Thu, 2008-09-18 at 11:00 +0100, Patrick Burns wrote:
> I disagree with Ajay about the value of Winsorization.
> Yes, it is ad hoc but it is simple to understand and often results in 
> reasonable answers.
> It certainly depends on the context but if we are talking about 
> financial returns, then I haven't had positive experience with 
> traditional statistical robustness.
> (Given that my thesis was on robustness, I don't say this lightly.)  
> Robustness often gives inferior answers in finance (in my experience) 
> even when it is obvious that it "should" be the proper thing to do.  
> This is a phenomenon that I don't understand.

I have to agree with Patrick.  We proposed an extension above and beyond
classic Winsorization that would only reduce the outliers that occured
beyond a certain confidence level (e.g. 95% or 99%).  Traditional robust
methods have a tendency to ignore outliers rather than simply reduce
their influence.  In measuring risk, this is clearly quite dangerous.
We found that by only cleaning outliers beyond a certain confidence, we
got much more stable and accurate out of sample predictions on a variety
of risk measures (as well as predictions that compared well to kernel
estimation and Monte Carlo methods with lower computational burden).

Like I said in my previous email, code and documentation available upon
request or in the next version of PerformanceAnalytics.


    - Brian

Ph: 773-459-4973
IM: bgpbraverock

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