How to simulate stock prices with stochastic time change subordinated arithmetic Brownian Motion?

the idea is to simulate price returns thus to be normally distributed i 'am trying to use subordinated arithmetic brownian motion subordinated to time activity (volume) stock prices are following GBM then you can say $$dS_t=μS_tdt+σS_tdW_t$$ where the time considered is not the calendar time but activity time (Ané & Geman 2000). I faced problems while implementing it in matlab so any help would be appreciated.

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Can you say what these problems were that you faced in Matlab? Can you post your Matlab code here? –  chrisaycock Sep 23 '13 at 16:10

Here it is. Returns here are normally distributed by construction. It doesn't involve time scale, you can use time, volume, or any other "activity".

>> sigma = 0.001;
>> mu = 0;
>> returns = mu + sigma * randn(1000,1);
>> price = cumprod(1 + returns);
>> plot(price)


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