[R-SIG-Finance] high frequency data analysis in R
comtech.usa at gmail.com
Thu May 21 22:43:14 CEST 2009
In fact, I have the whole jump processes of best bid, and best ask, at
a continuous level (in the sense of time-stamped arrival data), and
also the jump process of the last trade price, at a continuous level
(in the sense of time-stamped arrival data).
I don't understand why you say I lose information. Of course, I lose
the arrival information by "flattening" the arrivals. But it's the
regularly spaced data that's studied by the correlation, right?
Any more thoughts?
On Thu, May 21, 2009 at 11:44 AM, <markleeds at verizon.net> wrote:
> hi: but if you make it regularly spaced, then you will be removing data and
> therefore possibly information. i'm sure what you're doing is what is
> usually done and it's probably fine ( i really don't know to be totally
> honest ) but i'm just saying that irregularly spaced data is not that simple
> if you don't want to make assumptions.
> On May 21, 2009, 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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