i was working on some financial data on matlab and using it for time series forecasting. there is a function in it periodicreturns(TotalReturnPrices,Period). if i choose a period of say 5 days it gives me rolling return periodic values with period 5. so for example i have prices like [100, 110, 120, 130, 120, 110, 100, 90, 95, 100, 105, 107, 100, 110, 108] so using the above function i get the returns as following: 1) (120-100)/100 = 0.20; 2) (110-110)/110=0; 3) (100-120)/120=0.1667 etc... so i have around 10 rolling returns like this. now if i had say 1000 daily prices and i calculate rolling returns with period of say one month (around 26 days) and i have 974 such kind of rolling returns. can i use these returns for time series model estimation (arima, arima-garch etc) and forecasting? will using these kind of rolling returns introduce any kind of stationarity issues, co-integration errors etc? i'am a novice in time series so i don't have the slightest clue of errors using spurious data. but i just thought using rolling returns are we using overlapping data or data too close to each other. your help in this matter would be highly obliged. thanks azim
