[R-SIG-Finance] high frequency data analysis in R
Hae Kyung Im
hakyim at gmail.com
Thu May 21 18:51:23 CEST 2009
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.
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?
>> 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:
>>> 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
>>>> 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
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>>> Jeffrey Ryan
>>> jeffrey.ryan at insightalgo.com
>>> ia: insight algorithmics
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