There is a huge literature on the behavior of stock returns that you should
look at before trying to fit models. A good place to start is chapter 2 of
The Econometrics of Financial Markets by Campbell, Lo and Mackinnlay
(Princeton University Press). Another good source is Modeling Financial Time
Series by Stephen Taylor (Wiley).
Note: Overdifferencing a time series creates spurious correlations so there
is no point trying to model the 2nd, 3rd or higher difference of stock
prices. ez
"Nilufer Pettersson-Arm" <[EMAIL PROTECTED]> wrote in message
92n1f0$ji9$[EMAIL PROTECTED]">news:92n1f0$ji9$[EMAIL PROTECTED]...
> 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
>
>
>
>
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