[R-SIG-Finance] Correlation on Tick Data

Tue Jul 22 20:25:46 CEST 2008

If you are doing hypothesis testing, then much better
is to use a permutation test -- no distribution assumption
required.  Permutation tests are talked about in
http://www.burns-stat.com/pages/Tutor/bootstrap_resampling.html

Box-Cox transformation is not going to help with long-tails,
which is how returns are distributed, especially intraday returns.

Patrick Burns
patrick at burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")

BOB SAMOHYL wrote:
> Mark, 
>
> To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) or youll have to find a more appropriate distribution for your data. Or transform the data to normal. ThereÂ´s a box-cox transformation in R that can resolve this. Normality may represent returns data most of the time (maybe not) but imagineÂ  that it doesnt in a specific case and this discrepancy finds its way into a portfolio. Bob
>  
>
> --- Em ter, 22/7/08, markleeds at verizon.net <markleeds at verizon.net> escreveu:
> De: markleeds at verizon.net <markleeds at verizon.net>
> Assunto: Re: [R-SIG-Finance] Correlation on Tick Data
> Para: samohyl at yahoo.com
> Cc: r-sig-finance at stat.math.ethz.ch
> Data: TerÃ§a-feira, 22 de Julho de 2008, 13:43
>
> Thanks for pointing that out. I understand the linearity ( you're 
> saying i think that correlation will only pick up linear relation ) but 
> why normality ?
>
>
> On Tue, Jul 22, 2008 at 12:29 PM, BOB SAMOHYL wrote:
>
>   
>> I would go farther than just stationarity and include normality, and 
>> linearity in the relation, three suppositions for the correlation 
>> coefficient that are rarely examined.
>> Robert Wayne Samohyl, Ph.D. www.qualimetria.ufsc.br
>> fones: 55-48-3721-7001 University 55-48-9608-5056 celular  ï¿½ï¿½
>>
>> --- Em ter, 22/7/08, markleeds at verizon.net <markleeds at verizon.net> 
>> escreveu:
>> De: markleeds at verizon.net <markleeds at verizon.net>
>> Assunto: Re: [R-SIG-Finance] Correlation on Tick Data
>> Para: "Matthieu Stigler" <Matthieu.Stigler at gmail.com>
>> Cc: r-sig-finance at stat.math.ethz.ch
>> Data: Terï¿½ï¿½a-feira, 22 de Julho de 2008, 12:38
>>
>> just to elaborate a bit more on what matt said.
>>
>> you need to make sure you time series are stationary before you 
>> correlate them.  usually one does this by using a unit root test but, 
>> in your case, since you are dealing with currencies, as long as you 
>> are dealing with the returns streamsand not the prices themselves, 
>> there's really no need to use the unit root test.  returns should be 
>> stationary ( in general ).   if you're dealing with prices, then 
>> correlations
>> don't make sense because prices aren't ( in general ),
>> or atleast i've never seen prices that were.
>>
>>
>>
>> On Tue, Jul 22, 2008 at 10:14 AM, Matthieu Stigler wrote:
>>
>>     
>>> Hello
>>>
>>> If ES and YM are time series, you maybe should first test for 
>>> auto-correlation of the series. High auto-correlated series can lead 
>>> to the phenomen called as spurious regression, and then the 
>>> correlation coefficient is "too high".
>>>
>>> Hope this helps
>>>
>>> Mat
>>>
>>> Neil Gupta a ï¿½ï¿½crit :
>>>       
>>>> Hello R users.
>>>>
>>>> I was using R to calculate correlation of midquote returns on ES
>>>>         
> and 
>   
>>>> YM. ES
>>>> and YM are highly correlated at close to .97. However when I run
>>>>         
> the
>   
>>>> correlation on the MQ returns the correlation is close to 0.
>>>>         
> Should 
>   
>>>> I be
>>>> expecting this or am I doing something wrong? Others have told me 
>>>> this
>>>> should happen, but I do not understand why. If anyone can please 
>>>> explain I
>>>> would really appreciate.
>>>>
>>>> Many Thanks,
>>>> Neil
>>>>
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>>>>
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