[R-SIG-Finance] Interpolating/comparing two irregulartime/price sequences?

[Rory Winston]

Thanks everyone for their extremely helpful comments on this issue.
Eric, that is a very interesting point you have raised. Did Peter
publish a paper on this topic? If so, do you happen to know the
title? I feel intuitively that the previous tick method should be more
reliable than interpolation for high-frequency data, although it would
be nice to see some research on this topic confirming this to be the
case.

Thanks
Rory


On Nov 8, 2007 9:33 PM, Eric Zivot <ezivot at u.washington.edu> wrote:
>
>
> Just a few quick comments on this issue
>
> The Olsen group book, Introduction to High Frequency Finance, discusses
> various interpolation schemes to align multiple irregularly spaced data. For
> realized variance modeling Peter Hansen at Stanford showed that one should
> use the "previous tick" method for aligning data to a common time clock and
> not an linear interpolation around neighboring ticks. The latter method
> leads to degenerate results as you sample more frequently since the
> quadratic variation of a line is zero.
>
> The type of alignment discussed below is handled in the timeSeries class in
> S-PLUS using the align() function. Diethelm Wuertz implemented a subset of
> this class in R and I think the align() function is there too.
>
>
>  ________________________________
>  From: r-sig-finance-bounces at stat.math.ethz.ch
> [mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Adrian
> Trapletti
> Sent: Thursday, November 08, 2007 3:37 AM
> To: rory.winston at gmail.com
> Cc: R-Finance
> Subject: Re: [R-SIG-Finance] Interpolating/comparing two irregulartime/price
> sequences?
>
>
>
> Rory,
>
> There is no best method for synchronizing high frequency data. It depends on
> the application. One of the pioneers for high frequency financial data
> modelling was http://www.olsen.ch . In the 90ies they published some
> articles where they used interpolation schemes to model irregularly spaced
> high frequency data with standard discrete time series methods. You can find
> some articles on their website. Currently, there is a lot of work on the
> topic realized variance/volatility, and when it comes to multivariate
> applications, you may find some methods there
> http://www.google.ch/search?hl=en&q=%22realized+covariance%22&btnG=Search&meta=
>
> Best regards
> Adrian
>
>
>
> Message: 1
> Date: Wed, 7 Nov 2007 18:31:16 +0000
> From: "Rory Winston" <rory.winston at gmail.com>
> Subject: [R-SIG-Finance] Interpolating/comparing two irregular
>  time/price sequences?
> To: r-sig-finance at stat.math.ethz.ch
> Message-ID:
>  <3f446aa30711071031j37936e36i933be63c90f9ce4c at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hi all
>
> I have two data frames, that both look like the following:
>
>
>
> > head(series1)
>
>  timestamp mid spread
> 1 1.194438e+12 2.10011 0.000260
> 2 1.194438e+12 2.10010 0.000290
> ...
>
> These two time sequences are sampled on price ticks, so the interval
> between ticks is stochastic and irregular. The time sequences are also
> of different lengths, i.e. one may have 8 hours worth of data, the
> other may have 4. My issue is that I want to compare these two series
> for similarity - they should be producing almost exactly the same
> data, although potentially at slightly different timestamps (hence the
> sampling irregularity). I can subset the data so that they span
> roughly the same time intervals, but the number of ticks in each
> series will be different. Basically what I am trying to achieve is
> some sort of constant interpolation based on a time index - so that if
> series A starts at 08:01, contains 10,000 ticks, and ends at 16:05,
> and series B starts at 08:00, contains 7,000 ticks, and ends at 16:06,
> I would like to be able to index from series A into series B at say,
> each timestamp in A. Using a simple example, for the following series
> A and B:
>
> A:
> time tick
> 16:01 2.05
> 16:02 2.06
>
>
> B:
> time tick
> 16:00 2.04
> 16:02 2.06
>
> I would like to be able to index from A into B at each tick from A, so
> I would get an output series that was the value of B at each time A
> ticked:
>
> C
> time tick
> 16:01 2.04 <--- constant interpolation from value of B @ 16:00
> 16:02 2.06
>
> Has anyone done anything like this before? I'm looking at the zoo
> package to see if it can help me, but I havent quite figured out how
> to do this kind of thing yet. Is this even a good way to checking
> whether series B is very similar to series A at the discrete tick
> intervals? Any better methods?(I guess another way might be to align
> the two subsetted series exactly and just take differences).
>
> Thanks
> Rory
>
>
>
>
>
> --
> Adrian Trapletti
> Wildsbergstrasse 31
> 8610 Uster
> Switzerland
>
> Phone : +41 (0) 44 9945630
> Mobile : +41 (0) 76 3705631
>
> Email : a.trapletti at swissonline.ch
>
>