Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed-Rate Mortgage-Backed Securities

We assess nonparametric kernel-density regression as a technique for estimating mortgage loan prepayments—one of the key components in pricing highly volatile mortgage-backed securities and their derivatives. The highly nonlinear and so-called irrational behavior of the prepayment function lends itself well to an estimator that is free of both functional and distributional assumptions. The technique is shown to exhibit superior out-of-sample predictive ability compared to both proportional-hazards and proprietary-practitioner models. Moreover, the best kernel model provides this improved predictive power utilizing a more parsimonious specification in terms of both data and covariates. We conclude that the technique may prove useful in other financial modeling applications, such as default modeling, and other derivative pricing problems where highly nonlinear relationships and optionality exist.     

Mortgagor Motivations in Prepayments for Adjustable Rate Mortgages

This paper provides the first rigorous analysis of residential adjustable mortgage prepayment using individual ARM mortgage data in Singapore. The prepayment rate for residential mortgages is low and is dominated more by macroeconomic factors than mortgage–specific factors. Specifically, the prepayment rate is increasing in residential property prices, but decreasing in income as proxied by GDP and volatility in mortgage rates. There is weak evidence to suggest that prepayment is increasing in the borrower's age, mortgage rate hikes, cash–availability variables and sentiments of the stock market, and decreasing in the price premium over valuation, payment–to–income ratio, loan–to–value ratio, loan term and floor level of the property.