Improved Prepayment Modeling for OAS Analysis

Abstract

SAM CHOI is a consultant in the fixed income research department at BARR4 in Berkeley, California. n early 1992, prepayments on mortgage-backed securities (MBS) began their climb to historic levels and caught many investors by surprise. Many long-term prepayment models, which are built using many years of historical data, severely underestimated prepayments of MBS during this past period. The simple explanation for this inaccuracy is that there is no historical precedent for the recent prepayment wave. The new generation of prepayment models must now be built to allow for constantly changing and unprecedented market conditions, and at the same time work within the framework of tradtional option-adjusted spread (OAS) analysis. One way to approach this problem is to develop a model with two components: a short-term component that incorporates only recent data; and a long-term component that incorporates all available historical data, and maintains the traditional, robust behavioral model properties that are necessary for OAS analysis. From an investor’s perspective, short-term accuracy in prepayment predictions is extremely important. Many of the bonds IOs, POs, Jump Zs, TAG, and so on are extremely sensitive to prepayments. Average lives, durations, and fitted prices fluctuate dramatically with even moderately different prepayment assumptions for these tranches. As a good example, let’s look at an FNMA PAC-PO (series 1993-19 Class K) with a PSA band of 140-225 under the different prepayment scenarios in Exhibit 1. Clearly, accurate prepayment projections are imperative for accurate valuation and risk characterization of mortgage-backed bonds.