Calculate mortgage pipeline exposure in layers: measure active, unclosed loan volume; apply a documented pull-through forecast to estimate funded volume; translate both loans and hedges into comparable duration- or price-sensitivity measures; then compare current coverage with the target produced by your approved model. The resulting gap—not a blanket 100% rule—determines whether the book is under-covered, over-covered, or within limits.
What mortgage pipeline exposure and hedge coverage mean
Pipeline exposure is the interest-rate-sensitive balance of loans that are locked or committed but have not yet closed and been delivered. In a basic report it is the active unpaid principal balance (UPB) of that population. Hedge coverage is the active position in instruments such as to-be-announced (TBA) mortgage securities that is intended to offset the pipeline’s price risk.
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Keep the two quantities separate. Mortgage Capital Trading (MCT) describes pipeline volume as the current dollar amount of active loans in the hedge pipeline and hedge position as the active TBA coverage. A $20 million pipeline and a $16 million TBA short are not the same number, even if they are managed together.
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The practical calculation has four increasingly useful views:
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- Notional volume: dollars of active loans and dollars of hedge instruments.
- Expected funded volume: the pipeline after applying forecast pull-through.
- Duration-weighted exposure: an estimate of how each side changes when rates move.
- Coverage gap: the difference between modeled current coverage and the approved optimal target.
A notional percentage is a screening measure. It cannot, by itself, account for fallout, different durations, servicing values, basis movements, convexity, delivery commitments, or transaction costs.
Assemble the inputs on one consistent basis
Choose an as-of date and time, then freeze the data used for that calculation. The pipeline, hedge position, pull-through assumptions, prices, and durations should all refer to the same cut-off. A daily report that mixes yesterday’s locks with today’s TBA position can show a false surplus or deficit.
Define the unclosed population
Most institutions include active locks or commitments that have not closed and been delivered. Exclude funded loans that have already been committed or sold unless the institution’s documented framework deliberately retains their risk. Pipeline administration and warehouse administration are related but distinct control populations under interagency guidance.
Record at least the following for every loan or homogeneous cohort:
- Active UPB or the other notional basis used by the hedge policy.
- Product and program, including coupon or note-rate bucket where relevant.
- Expected closing or delivery date.
- Commitment or hedge bucket to which the loan is assigned.
- Forecast pull-through and the vintage or model version behind it.
- Pipeline duration, price sensitivity, or other approved rate-risk factor.
- Any servicing, mortgage-servicing-right (MSR), mandatory-delivery, or execution attributes that alter the hedge requirement.
Match the hedge position to the same buckets
Inventory the TBA or other hedge positions attributable to the same products, coupons, settlement dates, and risk buckets. A single portfolio total can hide a mismatch: a 30-year cohort may be over-hedged while a different coupon or delivery month is under-hedged. Report the hedge notional, instrument duration or sensitivity, market value, counterparty, and any netting permitted by policy.
Step 1: estimate expected funded volume with pull-through
Pull-through converts locked or committed loans into an estimate of what will actually close and fund. Fannie Mae’s glossary defines realized pull-through as “the volume of loans closed and funded divided by the volume of loans committed over a period of time expressed as a percentage.” For a simple cohort:
Expected funded volume = active pipeline volume × forecast pull-through
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For example, $12 million of active locks with a 72% forecast produces $8.64 million of expected funded volume. The arithmetic is straightforward; the forecast is the model-risk decision.
Use a forecast, not an unexamined trailing average
A realized rate from a prior period can be a starting point, but current loans may have different borrower, underwriting, product, market, or time-to-close characteristics. Hedging models commonly estimate pull-through by loan or cohort and aggregate the results by program or hedge bucket. Document the data window, segmentation, overrides, and reason for each material override.
Track actual funded outcomes against each forecast vintage. Interagency supervisory guidance expects mortgage-banking models to compare predicted performance with actual performance and to recalibrate assumptions when experience changes. A forecast that is consistently high leaves the institution short of hedge coverage after fallout; one that is consistently low leaves it carrying excess coverage and avoidable pair-off or trading costs.
Step 2: calculate nominal coverage as a first check
The basic ratio is:
Nominal coverage = active hedge position ÷ active pipeline volume
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If the pipeline is $20 million and the attributable hedge is $15 million, nominal coverage is 75%. This percentage says only how many pipeline dollars are offset by hedge notional. It is not a recommended target and it does not prove that 75% of the rate risk is hedged.
Fallout explains why a hedge may be smaller than the lock balance. If only 70% of locks are expected to fund, hedging the full $20 million can create an over-covered position when the expected $14 million funds. Conversely, a model may retain additional coverage for servicing or duration effects even when expected funded volume is below the lock balance.
Compare common measures
| Measure | Formula or basis | What it answers | What it omits |
|---|---|---|---|
| Pipeline volume | Active unclosed UPB | How many loan dollars are in the defined population? | Fallout and rate sensitivity |
| Expected funded volume | Pipeline volume × forecast pull-through | How many dollars are expected to fund? | Duration, servicing, basis, and model error |
| Nominal coverage | Hedge notional ÷ pipeline volume | How much lock notional is offset? | Different instrument sensitivities and the proper target |
| Duration-weighted coverage | Hedge sensitivity ÷ pipeline sensitivity | How closely do the positions respond to a rate move? | Model assumptions, basis, convexity, and execution effects |
Step 3: convert the book to duration-weighted exposure
Mortgage loans and TBA positions with equal notional amounts can gain or lose different amounts when rates change. A sensitivity-weighted view puts both sides on a more comparable basis:
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Duration-weighted pipeline exposure = expected funded exposure × pipeline duration factor
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Duration-weighted hedge coverage = hedge notional × hedge duration factor
The factors may be expressed as effective duration, dollar value of a basis point, price sensitivity, or another approved measure. Use the same sign convention and scaling on both sides. A model might represent a short TBA as an offsetting positive hedge to a long pipeline; another system may show the short with a negative market-value sign. The report must explain its convention rather than mixing signs from different systems.
Set the target from the approved model
Compare current duration-weighted coverage with the model-defined optimal coverage. MCT’s Hedging 201 example describes optimal coverage as forecast pull-through less a theta adjustment, then compares duration-weighted current coverage with that target to produce a long or short percentage or dollar gap. Theta and duration factors are model inputs, not universal industry constants. They should be approved, validated, periodically reviewed, and changed through documented governance.
Servicing can make the target differ from pull-through alone. Mortgage-servicing assets may lose value when rates fall and refinancing or prepayments accelerate. The Federal Reserve, FDIC, OCC, and OTS advisory issued February 25, 2003, says: Successful hedging systems will mitigate the impact of prepayments on MSA values and the effects of interest-rate risk in the mortgage pipeline and warehouse.
A pipeline-only ratio can therefore understate total mortgage-banking risk if servicing exposure is material.
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Once both current and optimal coverage are expressed on the same basis, calculate:
Coverage gap = current duration-weighted coverage − optimal duration-weighted coverage
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Express the result in the unit used by policy: percentage points, hedge notional, dollar value of a basis point, or market value. A negative gap indicates less coverage than the model target; a positive gap indicates more.
Under-covered or “long”
MCT’s simplified example calls a position below target under-covered or “long” and illustrates selling additional TBA coverage. Before trading, confirm the instrument, coupon, settlement month, basis relationship, counterparty capacity, liquidity, and approved limits. The trade should correct the modeled risk, not merely push a notional ratio toward 100%.
Over-covered
A position above target may require pairing out, reducing or reallocating coverage, or allowing funded-loan commitments to absorb the hedge. Pairing out can create trading costs, and mandatory commitments may impose pair-off fees when specified product volume is not delivered by the required date. The treasury or secondary-marketing policy should specify who approves the adjustment and how the resulting cost is recorded.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Worked illustrations and their limits
MCT’s $10 million rate-shock illustration
MCT’s 2022 Hedging 101 white paper uses a deliberately simplified scenario: a $10 million pipeline, 100% pull-through, a 25-basis-point rate rise, and a 4-to-1 price-to-rate multiple. It illustrates a 100-basis-point price loss on the pipeline. Shorting $10 million of TBAs at an assumed price of 101 is shown as producing a one-point hedge gain to offset that one-point pipeline loss.
Those are scenario inputs and arithmetic, not a live market quote or an independently tested hedge result. Real pipelines experience fallout, different loan and TBA durations, basis changes, convexity, servicing effects, and execution costs. MCT uses the example to motivate loan-level pull-through and a model-derived optimal hedge rather than mechanical 100% notional coverage.
New-lock and trade-recycling illustration
The same white paper describes $2 million of new locks with 75% expected pull-through, implying $1.5 million of expected funded principal and illustrative TBA coverage. It then describes $1.5 million of funded loans being committed to investors and the released coverage being retained against new lock needs. MCT calls this operational practice trade recycling.
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Illustrative 85% optimal coverage
MCT’s Hedging 201 material includes an illustrative $10 million pipeline with an 85% optimal-coverage recommendation, implying $8.5 million of TBA coverage. The 85% figure belongs to that example’s assumptions; it is not a general target for lenders or a substitute for an institution’s validated model.
Controls that keep the calculation reliable
Fallout and model validation
- Store the forecast pull-through used at each as-of date, not only the latest revised estimate.
- Compare forecast versus actual funded volume by cohort, product, and time-to-close.
- Investigate persistent bias, large overrides, and abrupt changes in fallout.
- Require independent model review and documented approval for material methodology changes.
Duration, convexity, and servicing
Mortgage cash flows change as rates and prepayment incentives change. Effective duration can shorten when refinancing accelerates and lengthen when borrowers become trapped by higher rates. The MCT examples refer to servicing-related negative convexity as one reason optimal coverage may differ from pull-through alone. Include MSAs and other servicing exposures in the enterprise interest-rate-risk view, with reasonable and supportable assumptions and validation.
Basis and execution
A TBA hedge is not a perfect substitute for a loan. Coupon, product, geography, loan characteristics, settlement timing, and market liquidity create basis risk. Trading costs, margin, bid-ask spreads, financing, and counterparty limits affect the economic result. The cited sources do not establish one universal hedge instrument, cost assumption, or coverage percentage.
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Interagency guidance expects systems that measure and control interest-rate risk, monitor hedge effectiveness and concentrations, and provide management information on positions, mark-to-market, and rate-shock exposures. Run expected and stressed scenarios rather than relying only on a current-rate snapshot. Stress the fallout forecast, parallel and nonparallel rate moves, basis widening, servicing values, counterparty failure, and delivery delays where relevant.
A useful management report shows, by bucket:
- Pipeline UPB, expected funded UPB, hedge notional, and nominal coverage.
- Pipeline and hedge sensitivity, current duration-weighted coverage, optimal target, and gap.
- Mark-to-market, realized and unrealized hedge results, and transaction costs.
- Forecast-versus-actual pull-through, exceptions, overrides, limit usage, and concentrations.
- Rate-shock results for earnings and capital, including servicing exposure.
A repeatable daily calculation workflow
- Set the cut-off: record the date, time, market data source, and reporting entity.
- Extract the population: include active unclosed locks or commitments according to policy; remove funded or delivered loans unless retained risk is explicitly included.
- Segment the loans: group by product, coupon, expected close date, commitment or hedge bucket, and any characteristic that changes fallout or sensitivity.
- Assign pull-through: apply the approved loan-level or cohort forecast, record vintage and overrides, and calculate expected funded volume.
- Reconcile hedges: map each TBA or other instrument to the same bucket and confirm notional, settlement, duration, market value, and counterparty.
- Compute nominal coverage: divide hedge notional by active pipeline volume as a diagnostic only.
- Compute sensitivity-weighted coverage: apply the approved duration, DV01, or price-sensitivity factors to expected funded exposure and hedge positions.
- Compare with target: calculate the model-defined optimal coverage and the current gap using identical units and sign conventions.
- Review exceptions: investigate large changes, concentration breaches, stale prices, unusual fallout, basis mismatches, and missing allocations.
- Execute or approve action: sell, buy back, pair out, reallocate, or recycle coverage only within documented authority, counterparty, liquidity, and delivery limits.
- Archive the evidence: retain inputs, formulas, model version, approvals, trades, and post-period forecast-versus-actual results.
Common calculation mistakes
- Calling locks the exposure after funding: funded and delivered loans belong in a different population unless retained risk is explicitly modeled.
- Using 100% as the default target: fallout and servicing can make the optimal ratio lower or higher than full notional coverage.
- Multiplying by a stale pull-through rate: current cohorts need current assumptions and documented overrides.
- Comparing unlike sensitivities: equal loan and TBA dollars do not guarantee equal price response.
- Ignoring basis and delivery terms: a notional offset can still lose money if the hedge and loans move differently or a commitment fails.
- Recycling without matching risk: released coverage is usable only when the replacement population is an appropriate risk match.
- Reporting only a portfolio total: aggregate coverage can conceal coupon, product, settlement, or counterparty concentrations.
- Skipping back-testing and stress: a model that looks accurate in stable markets can fail when rates, prepayments, or fallout move sharply.
What a defensible answer looks like
A defensible report can show the active pipeline, expected funded amount, nominal hedge ratio, duration-weighted current coverage, model target, and dollar or percentage gap for every material bucket. It also states the as-of time, pull-through methodology, duration convention, servicing treatment, basis assumptions, limits, and approval status. That transparency lets management distinguish a genuine rate-risk imbalance from a data-timing issue or a deliberate temporary position.
The calculation is therefore not a single percentage. It is a controlled process that starts with the right loans, forecasts what will fund, measures rate sensitivity on both sides, and connects any trade to documented limits and delivery obligations.
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