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I need to analyse a bunch of weekly time series that reflect the turnovers of various companies. I already read that return rates or share prices show stochastic patterns that can be modelled by a random walk. However, such time series usually correspond to continuous functions (curves), whereas turnover values can go up and down dramatically between two successive weeks. For example:

Week t: 1 mio Euros

Week t+1: 0 Euros

QUESTION: So my question is whether the choice of a random walk model would still be justified or not.

My plan is to model the timely courses of turnover figures by a random walk model that allows for a drift because analyzing the distribution of drifts is my final goal.

My apologies for weaknesses in the explanation - I am not from finance originally.


In the meantime, I figured out that the core of my question refers to the distinction between so-called stock and flow variables. The revenue per week is a flow variable, whereas a share price at a specific point in time is a stock variable.

However, it remains unclear to me whether both types of variables can be treated with the same stochastic models.

The random walk with drift model is described in 'Introductory Time Series with R (Use R!)' by Paul S. P. Cowpertwait. In the book it is applied to a time series of stock variables (share prices). It allows to analyse whether there exists a positive drift (i.e. a mean increase of prices) in a time series of prices, which is mainly determined by unknown and unpredictable (i.e. stochastic) effects. But the question remains, whether this model can also be used for flow variables.

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  • $\begingroup$ Hi @user1881788 and welcome to quant.SE! could you edit your question posting the references about the paper/article you're talking about? $\endgroup$ – Quantopik Apr 30 '15 at 0:10

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