When modelling prepayments in Securitized products, why is it that the standard model involves a transition matrix (Markov Chain) framework, where the probabilities of transitioning between different states of delinquency are modelled? I have searched and have been unable to find any literature corresponding to models that are used to directly predict the actual prepayment amounts ($ amount) for each time period, given a dataset containing that information. As this information is directly available why isn't a regression fit directly to that data to predict the actual prepayment amount? I would like to understand why this seemingly easier method has been eschewed in favor of the Discrete-time Markov Chain framework for prepayment modelling (which requires logistic regressions for each transition that is modelled). If there is literature on such modelling, would be grateful if someone could provide the corresponding links.