Volatility Modeling what's the right approach? / Time Series predictive modeling of future balance in a bank account I'm trying to model a future balance for any time t in a list of bank accounts.
I have historical balance & transactional data, and other factor data that affects the balance of an account (such as interest payments, fees, etc) that I'll try to fit in the model as well.
My question is, what model/kind of model should I use to estimate a future value of the Balance variable? 
A GARCH/ARCH model?
Would a multivariate regression be sufficient to accurately predict future values?
I don't have much background experience in econometric analyses, so I would appreciate if you could just point me to the right direction and I'll research and dig deeper into it.
 A: The common industry approach is to model pools of accounts, grouped by vintage and other characteristics. It's often assumed that the lagged rates drive the decay rates. You can try to do a fancy analysis like GARCH, but I guarantee that it won't work better than simple approaches. There's too much noise on account level analysis, you'll abandon it eventually
A: I would consider a daily analysis using the historical daily balance data. Cash infusion and withdrawal activity often are systematic behavior/habits/ by day-of-the-week , day-of-the-month , holiday effects , week-of-the-month effects, month-of-the-year effects et al. The approach would be similar to Is is possible to fit discrete data to a continuous distribution, and use this to simulate discrete outcomes? predicting # of people expected for daily lunch and in terms of daily demand for cash here http://autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation slide 50 plus.
I would just try to form a daily predictive model for each separate account to build up useful examples. If the data set is long enough interest rates might be meaningful .
