Method 2: Risk-neutral prepayment modeling
Let us once again consider the risky return (1) and contemplate how we could generate the same return without taking the explicit measurements of dP/dx. What if we add an artificial drift rate of ps to the dynamics of our factor x ? The price of a risky instrument will get an additional drift of ps(dP/dx) per annum — which, in turn, constitutes the exact additional return required by formula (1)! Concisely, if the market fears that factor x can move up (or down), the pricing model should move it up (or down) at the rate proportional to the product of factor volatility and price of risk.

With this discovery, we can officially state that the explicit risk accounting for prepayment risk, or risks, is equivalent to the transformation of a "physical" prepayment model by adding the drift of risk factor or factors to their respective "feared" directions. This modified prepayment model is called risk-neutral. Physical models are usually designed by statisticians and reflect objective historical prepayments and trends; risk-neutral models incorporate prices of instruments. From what we've asserted above, a risk-neutral prepayment model will likely feature faster refinancing and slower turnover than the physical ("objective") model. Do not forget, however, that all agency instruments will be priced at prOAS, not OAS, when we use a risk-neutral prepay model.

Those who build, use or simply understand the concept of risk-neutrality in the interest rate market will find themselves in familiar waters. We don't employ "physical" interest rate models when we value rate derivatives. Everyone would say we use risk-neutral models calibrated to prices of widely traded instruments, say, swap and swaptions. Is it so much different from fudging a prepay model to prices of widely traded TBAs?

Risk or bias?
Suppose we designed a risk-neutral prepay model with features that incorporated market fears. Some may argue that this transformation may actually reflect a bias in the physical model. For example, the historical refinancing S-curve can already be biased if it does not reflect systematic enhancements in regulation that make refinancing hurdles lower. Investors expect the refinancing process to ease in the future, which will trigger refinancing decisions with lesser rate incentive.

Even if a physical prepay model is biased, we should not worry too much when transforming it into a risk-neutral model. Much like a steep forward curve may well reflect both risk and expectations, constructing a risk-neutral prepayment model requires no prior separation of these factors. According to the APT, expectation and risk are inseparable, from a valuation perspective. We saw that we added a systematic artificial drift term to the dynamics of our factor x that accounts for risk. We would do exactly the same if we noticed a bias in the model. >>>