VECTORS™ Prepayment Models draw on the collective experience of leading experts in the industry to address your valuation and risk management concerns. Leverage the power of Andrew Davidson and Co., Inc.'s market-standard tools or customize the tools to reveal the embedded risk and value in your portfolio. Our robust and stable prepayment models give you the insight you need to make investment decisions as well as the power to monitor and adjust those models based on changes in the marketplace. Click here for printer friendly version of Vectors™ Analytics Brochure.
OUR APPROACH TO PREPAYMENT MODELING
VECTORS™ Prepayment Models calculate a vector of monthly prepayment speeds for agency, non-agency prime and sub-prime loans/pools for fixed and adjustable-rate mortgages. Model factors include housing turnover, interest rate refinancing, cash-out refinancing (due to home price appreciation), credit cure, aging, seasonality, spread-at-origination, burnout and yield curve spread. The models use enhanced loan level details to adjust prepayment speeds based on the characteristics of the loan. The agency model considers loan size, original loan-to-value, credit score, regional home price indices, geographic location, property type, loan purpose and occupancy type to adjust speeds.
The non-agency model considers loan level original loan-to-value and original loan size to adjust speeds. The ARM model can forecast speeds on loans with interest-only (IO) and prepayment penalty periods. The suite of models also includes a model for option ARMs.
ACTIVE-PASSIVE DECOMPOSITION (APD)
Burnout in a mortgage pool is modeled by separating the borrowers in the pool into two separate groups: active and passive. This active-passive decomposition allows the model to quantify and forecast the different behavior of the borrowers who can and cannot refinance in an efficient manner.
RISK AVERSION METHODOLOGY
Borrowers are inherently risk averse to rate resets. To model prepayment behavior near ARM reset dates, the model uses a forward averaging methodology. The model first forecasts the expected interest rate the borrower will face at reset, based on the cap/floor/margin structure of the loan, and the term structure of interest rates. It then uses the moving six-month forward average to calculate the effective loan rate for that month. In an upward sloping yield-curve environment, as the coupon keeps going up so does the prepayment speed. Speeds reach a peak around the reset date.
FEATURES AND CAPABILITIES:
SEE FOR YOURSELF HOW THE MODEL IS PERFORMING
Dynamic performance reports provide an objective view of actual vs. forecasted results. Run these reports on our website, specifying the analysis period and cohort definition by vintage, coupon bucket or as-of date to generate user-defined displays of how the model is performing versus actual speeds.
TUNING THE MODEL
Visible tuning dials allow you to change model forecasts for a specific portfolio or during changing market conditions.
Current tuning dials allow the user to tune Refi, Turnover, Cashout, Credit Cure, Aging, SATO, Curve Spread, Lag, Scale, Slide and Burnout.
VECTORED TUNING
Vectored tuning allows users to specify a time dimension to tuning parameters. Users can select the number of periods the tuning parameters are in effect.
CUSTOM PREPAYMENT MODELS
We can design custom prepayment models to analyze specific pools of loans. Once the models are developed and tested, AD&Co will program software and subroutines for the models that can easily be integrated into any proprietary system or an existing vendor package.
MODEL DELIVERY
In addition to stand-alone Excel and web-based versions of our models, we offer dynamically linked libraries or shared objects that are easily incorporated into most proprietary risk management and valuation solutions.
VECTORS™ prepayment models are available on a wide variety of platforms for easy integration into leading analytical risk management systems:
VENDOR PARTNERS
RELATED RESEARCH
Fixed-Rate Agency MBS Prepayments & Model Enhancements
Divide & Conquer: Exploring New OAS Horizons, Part I Active-Passive Decomposition
