Using mid-quote tick data at a very high frequency will create a zero
correlation due to micro-structure noise.  Bid-ask bounce creates an upwards
bias in variance at high frequencies and non-synchronous trading creates a
bias toward zero in covariance.  Therefore, if you create a correlation you
will calculate (bias toward zero)/(upwards bias * upwards bias)  leading to
a bias toward zero.

I am in the process of releasing a new version of my realized variance,
covariance and correlation library.  You can find more information about the
problem above, and proposed solutions, in my user's manual (which also will
be updated soon).

User's Manual:
http://students.washington.edu/spayseur/realized/users.manual.08.pdf

Realized Library:
http://students.washington.edu/spayseur/realized

Bottom line... if you want a quick but NOISY estimate using tick data, don't
use every tick.  The literature says to use every other tick, or every 10th
tick, depending on your data.  The bias disappears as you decrease your
sampling frequency, but you trade this bias for noise.  (It is best to
investigate exactly how noisy this is or to try another type of estimator...
which you can do w/ my library.)

Scott


On Tue, Jul 22, 2008 at 8:51 PM, Eric Zivot <ezivot@u.washington.edu> wrote:

> I forgot to mention that my former Phd student Scott Payseur wrote the
> Realized R package that has functions to compute realized variance and
> covariance using all of the latest techniques.
>
>
> ****************************************************************
> *  Eric Zivot                                                  *
> *  Professor and Gary Waterman Distinguished Scholar           *
> *  Department of Economics                                     *
> *  Box 353330                  email:  ezivot@u.washington.edu *
> *  University of Washington    phone:  206-543-6715            *
> *  Seattle, WA 98195-3330                                      *
>                                                 *
> *  www:  http://faculty.washington.edu/ezivot                  *
> ****************************************************************
>
> On Tue, 22 Jul 2008 markleeds@verizon.net wrote:
>
>
>>  gotcha. so what you are saying is that you need normality for
>> hypothesis testing, statistical inference purposes. that's fine and
>> thanks again
>> for pointing that out.
>>
>>
>>
>>
>> On Tue, Jul 22, 2008 at  1:21 PM, 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. There´s a box-cox transformation in R that can resolve
>> this. Normality may represent returns data most of the time (maybe not)
>> but imagine  that it doesnt in a specific case and this discrepancy
>> finds its way into a portfolio. Bob
>>
>>
>> --- Em ter, 22/7/08, markleeds@verizon.net <markleeds@verizon.net>
>> escreveu:
>> De:  markleeds@verizon.net <markleeds@verizon.net>
>> Assunto: Re: [R-SIG-Finance] Correlation on Tick Data
>> Para: samohyl@yahoo.com
>> Cc: r-sig-finance@stat.math.ethz.ch
>> Data: Terça-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  ��
>>> --- Em ter, 22/7/08, markleeds@verizon.net <markleeds@verizon.net>
>>> escreveu: De: markleeds@verizon.net
>>>
>>  <markleeds@verizon.net>
>>
>>> Assunto: Re: [R-SIG-Finance] Correlation on Tick Data Para: "Matthieu
>>> Stigler" <Matthieu.Stigler@gmail.com> Cc:
>>> r-sig-finance@stat.math.ethz.ch Data: Ter��a-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 ��crit :
>>>>
>>>>> 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
>>>>>        [[alternative HTML version deleted]]
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