high frequency data analysis in R
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? 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!
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