Commercial Mortgage Prepayments Under Heterogeneous Prepayment Penalty Structures

Much of the literature on pricing commercial mortgages and commercial mortgage-backed securities has assumed homogeneity in prepayment penalty structure. In this paper, we provide evidence that such an assumption is inappropriate and examine the effect of penalty structures observed in actual contracts. After conducting preliminary simulations, we present hazard models estimated from data on 1,165 multifamily mortgage loans to show how empirical prepayment rates vary with alternative penalty structures. While yield maintenance and lockout provisions are relatively more effective than fixed or step down structures in reducing or postponing prepayment, none completely eliminates the risk. Our empirical results generally confirm the theoretical findings of Kelly and Slawson (2001).

Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression

Developing a good prepayment model is a central task in the valuation of mortgages and mortgage-backed securities but conventional parametric models often have bad out-of-sample predictive ability. A likely explanation is the highly non-linear nature of the prepayment function. Non-parametric techniques are much better at detecting non-linearity and multivariate interaction. This article discusses how non-parametric kernel regression may be applied to loan level event histories to produce a better parametric model. By utilizing a parsimonious specification, a model can be produced that practitioners can use in valuation routines based on Monte Carlo interest rate simulation.

Pricing mortgages: an options approach

Mortgages ; Options (Finance) ; Prices

Bayesian forecasting of Prepayment Rates for Individual Pools of Mortgages

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.State of Texas Advanced Research Program 003658-0763National Science Foundation CMMI-0457558, DMS-0605102Civil, Architectural, and Environmental Engineerin

Pricing Mortgages: An Interpretation of the Models and Results

Mortgages, like all debt securities, can be viewed as risk-free assets plus or minus contingent claims that can be usefully viewed as options. The most important options are: prepayment, which is a call option giving the borrower the right to buy back the mortgage at par, and default, which is a put option giving the borrower the right to sell the house in exchange for the mortgage. This paper reviews and interprets the large and growing body of literature that applies recent results of option pricing models to mortgages. We also provide a critique of the models and suggest directions for future research.

Financial simulations on a massively parallel connection machine

Includes bibliographical references (p. 23-24).by James M. Hutchinson & Stavros A. Zenios