[R-SIG-Finance] Winsorization

[ngottlieb at marinercapital.com]

Hi Pat:

I remember trying MVE many years ago when doing some things in finance
using Principal components
To create correlations first then do PCA.

Might be worth a look at Rousseau's old paper on MVE, been awhile but
sure easy to find again if you web search.

Interesting about least squares versus robust regression. Do you have
any studies showing
This, would be interested.

Neil 

-----Original Message-----
From: Patrick Burns [mailto:patrick at burns-stat.com] 
Sent: Thursday, September 18, 2008 12:47 PM
To: Gottlieb, Neil
Cc: brian at braverock.com; r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] Winsorization

Neil,

I don't recall having the opportunity to think of MVE in finance.  But I
have tried the moral equivalent in regression (when building risk
models).  The results were that high-breakdown regression did worst.
Best was Huber M-estimation with quite mild robustness.
Least squares was almost as good as the best, and better than almost all
of the robust regressions.

Pat

ngottlieb at marinercapital.com wrote:
> Patrick:
>
> Have you looked at Rousseau's, minimum volume ellipsoids (MVE) for 
> handling outliers?
>
> Curious if so how you found this for handling outliers? 
>
> Neil
>
> -----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.
> Peterson
> 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.
>
> Regards,
>
>     - Brian
>
> --
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
>
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