Bayesian Modeling of Mortgage Prepayment Rates

This paper proposes a novel approach for modeling prepayment rates of pools of mortgages. Our goal is to establish a model that will give a good prediction for prepayment rates for individual pools of mortgages. The model incorporates the empirical evidence that prepayment is past dependent via Bayesian methodology. There are many factors that influence the prepayment behavior and for many of them there is no available (or impossible to gather) information. We implement this issue by creating a mixture model and construct a Markov Chain Monte Carlo algorithm to estimate the parameters. We assess the model on a large data set from the Bloomberg Database.