[R-SIG-Finance] dynamic window size in rolling linear regression?

[Eric Zivot]

How to pick an optimal window depends, of course, on what optimal means.
Peseran and Timmermann have numerous articles related to this topic,
although they tend to focus on optimal forecasting in the presence of
structural change (just search on their names in SSRN and you will see a ton
of papers). Here is a recent paper:
http://www.econ.cam.ac.uk/faculty/pesaran/wp11/PPP-30-Oct-2011.pdf  Their
analysis clearly defines what "optimal" means and they derive optimal
weights for the data to satisfy their optimality criteria. 

-----Original Message-----
From: r-sig-finance-bounces at r-project.org
[mailto:r-sig-finance-bounces at r-project.org] On Behalf Of riccardo visca
Sent: Wednesday, January 11, 2012 10:08 AM
To: Patrick Burns; r-sig-finance at r-project.org
Subject: Re: [R-SIG-Finance] dynamic window size in rolling linear
regression?

What about using an expanding window with exponential weights to make the
coefficients more adaptive? Throwing away data is not good.
Still you need to have weights that are inverse of conditional variance to
correct eteroschedasticity.

It could be a lot more efficient computationally than Kalman and one could
use robust or lasso, ridge, pls... 
Food for thoughts...




________________________________
 Da: Patrick Burns <patrick at burns-stat.com>
A: r-sig-finance at r-project.org
Inviato: Mercoledl 11 Gennaio 2012 17:35
Oggetto: Re: [R-SIG-Finance] dynamic window size in rolling linear
regression?

Let's think about what you are asking for.

You want to change the window size in order (I presume) to get better
predictions.  So it seems to me that you would need a variable that has
information about the pertinence of past data to the future.

I could imagine volatility being such a variable in some circumstances.  I
don't know of any work along those lines -- I'd be interested to hear of
any.

My usual practice is to have weights that descend linearly.  In comparison
to exponentially decaying weights this puts more weight on the older data,
and hence is often a more stable estimate.  It has the advantage over equal
weighting that the window size is of less importance.

On 11/01/2012 17:11, Michael wrote:
> Hi all,
>
> In application of linear regression to financial time series, we 
> always have a parameter which is the window size.
>
> It's clear that a lot of results are sensitive to this parameter...
>
> Is there a way to make this parameter dynamic, or are there 
> statistical procedures to select such parameter dynamically and/or
"optimally"?
>
>  From a trading strategy perspective, is there a way to make this 
> parameter dynamically chosen?
>
> Thanks a lot!
>
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>
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--
Patrick Burns
patrick at burns-stat.com
http://www.burns-stat.com
http://www.portfolioprobe.com/blog
twitter: @portfolioprobe

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