[R-SIG-Finance] Causal version of HP filter and Kernel Smoothing in R?

Brian G. Peterson brian at braverock.com
Sat Feb 25 00:00:27 CET 2012

As usual, it helps to use the correct terminology.

The term usually employed is not 'causal' but 'one sided' or 'two sided'
filters.  In classic state space models, the two sided filter is often
called a 'smoother', and the one-sided version is called a 'filter'.
See any introduction to Kalman filters for examples, since the Kalman
may easily by one sided or two sided.

High pass filters are also quite trivial, as equation 4 in your
reference demonstrates.

I may be incorrect, having spent only a few moments on it, but I see
nothing in this paper to indicate that the kernel smoothing in equation
6 is not equally trivial.

Marc Wildi has written extensively on the topic of real time (one-sided)
filters, and his R code is public.

On Fri, 2012-02-24 at 16:47 -0600, Michael wrote:
> Thanks Brian.
> http://xfi.exeter.ac.uk/workingpapers/0804.pdf
> My understanding is that those kernel smoothers and HP filters are all
> non-causal...
> i.e. they peek into future from time-series point-of-view...
> Therefore, I am looking for the Causal version.
> Thank you!
> On Fri, Feb 24, 2012 at 4:41 PM, Brian G. Peterson
> <brian at braverock.com> wrote:
>         On Fri, 2012-02-24 at 15:39 -0600, Michael wrote:
>         > Hi all,
>         >
>         > I am reading a paper talking about extracting low frequency
>         trend in FX
>         > markets and then devising trading strategies based on those
>         low frequency
>         > trends.
>         >
>         > I was wondering if there are Causal version of HP filter and
>         kernel
>         > Smoothing functions in R, as mentioned in that paper?
>         >
>         > I did quite some search but couldn't find any ... Could you
>         please help me?
>         It would be easier for people to decide whether to help you if
>         you
>         actually provided the reference to the paper you are looking
>         to
>         replicate.
>         There are many kernel smoothing methods in various R packages,
>         which
>         your 'quite some search' I am sure uncovered, *and* kernel
>         smoothing
>         mechanisms are typically rather trivial to code.  So without
>         the
>         reference it is hard to even begin to evaluate which of them
>         might do
>         what you are looking for.  Also, it would be polite for you to
>         indicate
>         in what way the kernel smoothing mechanisms provided by
>         specific
>         packages do not match the methodology you desire.
>         --
>         Brian G. Peterson
>         http://braverock.com/brian/
>         Ph: 773-459-4973
>         IM: bgpbraverock

Brian G. Peterson
Ph: 773-459-4973
IM: bgpbraverock

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