Bayesian Analysis

Bayesian forecasting of prepayment rates for individual pools of mortgages

Edward I. George, Ivilina Popova, and Elmira Popova

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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.

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Bayesian Anal., Volume 3, Number 2 (2008), 393-426.

First available in Project Euclid: 22 June 2012

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mortgages prepayment rates MBS pricing Bayesian forecasting


Popova, Ivilina; Popova, Elmira; George, Edward I. Bayesian forecasting of prepayment rates for individual pools of mortgages. Bayesian Anal. 3 (2008), no. 2, 393--426. doi:10.1214/08-BA315.

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