In article <92n1f0$ji9$[EMAIL PROTECTED]>,
"Nilufer Pettersson-Arm" <[EMAIL PROTECTED]> wrote:
> I am trying to build an ARIMA model for the movements of the returns
of a
> stock. I have differentiated my data series once to make it
stationary.
> The autocorrelations and partial autocorrelations do not show any
clear
> pattern to indicate a model. I have tried all kinds of low-order
models,
> but they fit the data VERY poorly. However, if I differentiate it
three
> times or more, the fit gets better. But, what does this mean? The
series
> is stationary after the first differencing and should require no
further
> differencing. Is it that further differencing only smoothes the curve
out?
> Is it possible that a process like this cannot be modelled with ARIMA?
>
> Any help would be greatly appreciated.
>
> Nilufer
>
> ARIMA model identification can be quite tricky when the series has
either
1. Unusual Values ( Pulses )
2. Seasonal Pulses ( Say every Monday for example )
3. Level shifts ( Mean of the series shifts at different points)
4. Local Time Trends
OR
5. If the underlying model CHANGES or PARAMETERS CHANGE
OR
6. If the variance of the errors
a. Changes with the level ( Power Transformation needed ( eg. Logs
,,square roots etc.
b Changes at distinct points in time ( Regime Change in Variance
)
or c. Changes stochastically through time
Why worry about these things ...Don't Worry Be Happy ...use AUTOBOX
....It is designed to take the worry ...fretfulness out of modelling
Please see http://www.autobox.com and in particular
http://www.autobox.com/teach.html
Please download a free trial ( 14 day unrestricted useage ) and see for
yourself how AUTOBOX performs
http://www.autobox.com/abx5.exe
Happy Newe Year !
Dave R
Sent via Deja.com
http://www.deja.com/
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