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I constructed a model to forecast the prepayment rates for a mortgage loan portfolio (of mortgages in an emerging market) using probit regression on factors such as loan-to-value, PTI, time from settlement, and several characteristics of the borrower. However when testing with historical data the forecast is not so accurate. What is the best model to make accurate forecasts of prepayment rates for mortgage loans? |
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Pre-payment rates are difficult to forecast because of path dependency. The historical interest rate path - not just current market conditions and borrower characteristics - matters because borrowers may have exercised their right to call the mortgage bond and re-finance if rates had previously been at lower levels than the current rate. None on the variables you have identified account for these interest rate paths. For example, if rates are at 4% and have generally been rising there is considerably less prepayment risk then if rates are at 4% and have been declining. In a probit model, you could try adding a variable such as the distance of the 30-yr fixed mortgage rate from the lowest mortgage rate over the last X periods. A more typical and sophisticated approach is to model pre-payments and MBS valuations by monte carlo simulations of many interest rate paths. A simpler way to go about this would be to fit the terms to a PSA model. There are far fewer parameters here. You could also use option-adjusted spread analysis to determine borrower's optimal refinancing/pre-payment decision. Finally, along the lines of the monte carlo approach indicated above you could apply the two-factor Hull White model to simulate various interest rate paths. |
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