Bayesian Forecasting of Prepayment Rates for Individual Pools of Mortgages
Texas State University - San Marcos
University of Texas at Austin
University of Pennsylvania - Statistics Department
January 22, 2008
This paper proposes a novel approach for modeling prepayment rates of 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 Bayesian mixture model and construct a Markov Chain Monte Carlo algorithm to estimate the parameters. We assess the model on a data set from the Bloomberg Database. Our results show that the burnout effect is a significant variable for explaining normal prepayment activities. This result does not hold when prepayment is triggered by non-pool dependent events. We show how to use the new model to compute prices for Mortgage Backed Securities. Monte Carlo simulation is the traditional method for obtaining such prices and the proposed model can be easily incorporated within simulation pricing framework. Prices for standard Pass-Throughs are obtained using simulation.
Number of Pages in PDF File: 31
Keywords: Mortgage Prepayment Rates, Mortgage Backed Securities Pricing, Gibbs Sampling
JEL Classification: G0, G21, C5, C11, C15working papers series
Date posted: March 20, 2008 ; Last revised: May 22, 2012
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