[R-SIG-Finance] high frequency data analysis in R
Shane Conway
shane.conway at gmail.com
Thu May 21 20:15:06 CEST 2009
Some resources:
If you want to deal with irregular data, Eric Zivot's book on
financial time series mentions some operators that are available in
S-Plus based on this Zumback/Muller paper:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=208278. These
could be easily adapted to R. Ruey Tsay's book also has a chapter
that touches on it. In general terms, Dacorogna et. al. is a good
overview.
And as already mentioned, definitely look at the realized package:
http://students.washington.edu/spayseur/realized/.
On Thu, May 21, 2009 at 12:51 PM, Hae Kyung Im <hakyim at gmail.com> wrote:
> Relating the approach that turns irregular data into regular one,
> I guess it's a complex question and how you approach it will depend on
> the specific problem.
>
> With your method, you would assume that the price is equal to the last
> traded price or something like that. If there is no trade for some
> time, would it make sense to say that the price is the last traded
> price? If you wanted to actually buy/sell at that price, it's not
> obvious that you'll be able to do so.
>
> Also, if you only look at the time series of instantaneous prices, you
> would be losing a lot of information about what happened in between
> the time points. It makes more sense to aggregate and keep, for
> example, open, high, low and close. Or some statistics on the
> distribution of the prices between the endpoints.
>
> If what you need to calculate is correlations, then I would look at
> the papers that Liviu suggested. It seems that synchronicity is
> critical. I heard there is an extension of TSRV to correlations.
>
> If you only need to look at univariate time series, you may be able to
> get away with your method more easily. It may not be statistically
> efficient but may give you a good enough answer in some cases.
>
>
> HTH
> Haky
>
>
>
> On Thu, May 21, 2009 at 10:38 AM, Michael <comtech.usa at gmail.com> wrote:
>> My data are price change arrivals, irregularly spaced. But when there
>> is no price change, the price stays constant. Therefore, in fact, at
>> any time instant, you give me a time, I can give you the price at that
>> very instant of time. So irregularly spaced data can be easily sampled
>> to be regularly spaced data.
>> What do you think of this approach?
>>
>> On Thu, May 21, 2009 at 8:21 AM, Michael <comtech.usa at gmail.com> wrote:
>>> Thanks Jeff.
>>>
>>> By high frequency I mean really the tick data. For example, during
>>> peak time, the arrival of price events could be at about hundreds to
>>> thousands within one second, irregularly spaced.
>>>
>>> I've heard that forcing irregularly spaced data into regularly spaced
>>> data(e.g. through interpolation) will lose information. How's that so?
>>>
>>> Thanks!
>>>
>>> On Thu, May 21, 2009 at 8:15 AM, Jeff Ryan <jeff.a.ryan at gmail.com> wrote:
>>>> Not my domain, but you will more than likely have to aggregate to some
>>>> sort of regular/homogenous type of series for most traditional tools
>>>> to work.
>>>>
>>>> xts has to.period to aggregate up to a lower frequency from tick-level
>>>> data. Coupled with something like na.locf you can make yourself some
>>>> high frequency 'regular' data from 'irregular'
>>>>
>>>> Regular and irregular of course depend on what you are looking at
>>>> (weekends missing in daily data can still be 'regular').
>>>>
>>>> I'd be interested in hearing thoughts from those who actually tread in
>>>> the high-freq domain...
>>>>
>>>> A wealth of information can be found here:
>>>>
>>>> http://www.olsen.ch/publications/working-papers/
>>>>
>>>> Jeff
>>>>
>>>> On Thu, May 21, 2009 at 10:04 AM, Michael <comtech.usa at gmail.com> wrote:
>>>>> Hi all,
>>>>>
>>>>> I am wondering if there are some special toolboxes to handle high
>>>>> frequency data in R?
>>>>>
>>>>> I have some high frequency data and was wondering what meaningful
>>>>> experiments can I run on these high frequency data.
>>>>>
>>>>> Not sure if normal (low frequency) financial time series textbook data
>>>>> analysis tools will work for high frequency data?
>>>>>
>>>>> Let's say I run a correlation between two stocks using the high
>>>>> frequency data, or run an ARMA model on one stock, will the results be
>>>>> meaningful?
>>>>>
>>>>> Could anybody point me some classroom types of treatment or lab
>>>>> tutorial type of document which show me what meaningful
>>>>> experiments/tests I can run on high frequency data?
>>>>>
>>>>> Thanks a lot!
>>>>>
>>>>> _______________________________________________
>>>>> R-SIG-Finance at stat.math.ethz.ch mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Jeffrey Ryan
>>>> jeffrey.ryan at insightalgo.com
>>>>
>>>> ia: insight algorithmics
>>>> www.insightalgo.com
>>>>
>>>
>>
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>
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