The other components of the OAS instability can indeed be attributed to the market technicalities and risks that mortgage investors are often willing to take. For example, the liquidity component would fall into this “imaginary” category – its volatility is not realized until assets are sold. Interestingly enough, a part of prepay model risk can be considered compensation for market fears that refinancing and turnover speeds cannot be predicted perfectly. The boundary between bias and risk is not easy to draw in practice; however, there exists definitional difference between them. A prepay model bias is a drawback of a particular physical (real-world) prepay model that is employed by an OAS system. In essence, any concrete prepay model can be biased versus the best guess of the market. Unlike a prepay model bias, prepay model risk comes from the conceptual inability to predict prepayments and accounts for the difference between the real-world and a risk-neutral prepay world. The imaginary forces don’t alter expected cash flows and incomes; rather they simply explain the cause of them. For example, investing in an illiquid asset generates income that is due to the lack of liquidity and related initial price break, not the skills of the portfolio manager.

Let us return to prepayment bias, which is a real factor not only affecting market prices, but cash flows and income forecasts, too. For buy-and-hold investors, it may become the dominant risk after the interest rate risk is fully hedged. Below we show how one can quantify this risk and what kind of risk management ideas can come from our analysis.

A basic VAR-type illustration

Let us assume, for simplicity, that we use the AD&Co prepay model, but allow the turnover and refinancing scales (AKA “tunings” or “dials”) to carry a 15% uncertainty (standard deviation). For this conceptual presentation, we ignore complex dynamics of prepay model factors. For example, current turnover rate can be documented and known better than current refinancibility; both errors can exist at time zero and diffuse forward.

Figures 1 (flat curve) and 2 (steep curve) show prepay model risk profiles for TBAs arising from refinancing acceleration (red), turnover deceleration (green); the resultant Pythagorean VARs (violet) assume both errors are mutually independent. Since we can’t be sure about the exact magnitude of prepay errors, we calculated VARs using three extreme assumptions: “15/15” (refinancing and turnover errors are each of 15% magnitude), “30/15” (turnover dominates), and “15/30” (refinancing dominates).

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