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In Exhibit 1, we show the enhanced data loaded from Bloomberg for 4 Freddie pools. Computed tunings reflect available data; in particular, high credit score, low LTV and significant loan sizes somewhat inflate refinancing. Bloomberg is gradually expanding its downloadable information universe; currently only geographic data (4 leading states) comes back. We noticed that enhanced data can be downloaded for CMOs too, but only geographic information. Valuation Consequence: Pay-up Pay-up can stem from low loan balances, high LTVs or state concentration. Contemporary MBS market offerings are full of new jargons: "LLB pay-up" (i.e. pay-up for low loan balances), "New York pay-up", etc. Pay-ups can also arise simply due to WAM and WAC difference from TBA assumptions which can lead to delivery arbitrages. For example, a broker would demand a slight pay-up (several ticks) for brand-new production of premium pools because their speed will likely be ramping up for several months. These market phenomena can be quantified using AD&Co OAS, version 5.2b. Practical Pay-up How to compute the practical pay-up assuming we know the holding period,
say 6 months? First, we compute theoretical pay-up assuming the regular,
next-month settlement. Then, we compute theoretical pay-up using forward
settlement in 7 months (our OAS system allows settling MBS as far as
12 months forward.) This is the pay-up we forfeit when selling the pool
at the TBA forward price. By subtracting this forward pay-up scaled
down for amortization and discounting from the next-month theoretical
pay-up, we obtained the practical pay-up we seek. >>>
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