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

[Patrick Burns]

On 12/01/2012 10:05, riccardo visca wrote:

[...]

> So I think you are right and wrong ...

I absolutely agree.  Though we may
still have a discussion on which is which.

Pat

>
>
>
> ------------------------------------------------------------------------
> *Da:* Patrick Burns <patrick at burns-stat.com>
> *A:* riccardo visca <riccardovisca at yahoo.it>
> *Cc:* "r-sig-finance at r-project.org" <r-sig-finance at r-project.org>
> *Inviato:* Giovedì 12 Gennaio 2012 9:09
> *Oggetto:* Re: [R-SIG-Finance] dynamic window size in rolling linear
> regression?
>
> Throwing away data *is* good if the
> process changes and that data is not
> applicable to the future.
>
> Exponential weights throw away lots of
> data -- though they leave a very small
> amount of weight (at least) for all
> datapoints.
>
> Pat
>
> On 11/01/2012 18:08, riccardo visca wrote:
>  > 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
> <mailto:patrick at burns-stat.com>>
>  > *A:* r-sig-finance at r-project.org <mailto:r-sig-finance at r-project.org>
>  > *Inviato:* Mercoledì 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!
>  > >
>  > > [[alternative HTML version deleted]]
>  > >
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>  >
>  > --
>  > Patrick Burns
>  > patrick at burns-stat.com <mailto:patrick at burns-stat.com>
> <mailto:patrick at burns-stat.com <mailto:patrick at burns-stat.com>>
>  > http://www.burns-stat.com
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>  > twitter: @portfolioprobe
>  >
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>
> --
> Patrick Burns
> patrick at burns-stat.com <mailto:patrick at burns-stat.com>
> http://www.burns-stat.com
> http://www.portfolioprobe.com/blog
> twitter: @portfolioprobe
>
>

-- 
Patrick Burns
patrick at burns-stat.com
http://www.burns-stat.com
http://www.portfolioprobe.com/blog
twitter: @portfolioprobe