| name | analyzing-agency-mortgage-bonds |
| description | Evaluates agency MBS with prepayment modeling, OAS analysis, and convexity assessment. Use when analyzing agency MBS, modeling prepayments, or evaluating mortgage bond convexity. |
| tags | ["analysis","fixed-income"] |
| metadata | {"author":"casemark","practice_areas":["Fixed Income","Credit Research","Bond Trading"],"document_types":["Analysis Report"],"skill_modes":["Analysis"]} |
Analyzing Agency Mortgage Bonds
Evaluates agency MBS (Fannie Mae, Freddie Mac, Ginnie Mae) by modeling prepayment behavior, computing option-adjusted spread, and assessing negative convexity to support relative-value decisions in pass-throughs, CMOs, and specified pools.
When To Use
- Screening agency pass-throughs or CMO tranches for portfolio inclusion
- Comparing OAS across coupons, vintages, or specified-pool stories (e.g., low-balance, high-LTV, NY geo)
- Stress-testing mortgage portfolios against rate shocks or housing scenarios
- Evaluating roll-down, carry, and dollar-roll economics
- Assessing negative convexity exposure and hedge ratios for a book of MBS
Inputs To Gather
- Bond identifiers: CUSIP, pool number, or CMO tranche ID
- Pool-level data: WAC, WAM, WALA, original LTV, loan count, geographic concentration, loan size distribution, servicer
- Market data: current coupon TBA price, swap curve or Treasury curve, volatility surface (swaption vols), funding rate
- Prepayment model choice: dealer model (e.g., Bloomberg BCPS, Yield Book, Andrew Davidson) or proprietary CPR/CDR vectors
- Analysis horizon: static (spot OAS) vs. total-return over a holding period
- Scenario set: parallel shifts (±25/50/100/200 bp), curve twist, housing-price shock, refi-wave overlay
Workflow
-
Profile the collateral
- Pull pool-level factor data: current face, factor, WAC, WAM, WALA, avg loan size, geo mix, servicer
- Identify prepayment-relevant attributes: burnout level (months since origination vs. coupon incentive history), curtailment seasoning, credit characteristics
- For CMO tranches, map the cash-flow waterfall (sequential, PAC/support, IO/PO, Z-bond) and identify the effective collateral group
-
Generate prepayment projections
- Run the selected prepayment model to produce monthly CPR (or SMM) and CDR vectors under each rate scenario
- Decompose CPR into turnover, refinancing, curtailment, and default/liquidation components
- Compare model output to recent 1-month, 3-month, and 6-month actual speeds; flag divergences >15% as [VERIFY]
- Sensitivity-check the refi S-curve: confirm the model's media effect, burnout, and loan-size adjustments are reasonable for the pool's profile
-
Compute OAS and key risk metrics
- Build the cash-flow engine: apply monthly prepayment/default vectors to the amortization schedule, net of servicing and guarantee fees
- Discount cash flows over a Monte Carlo or lattice interest-rate model calibrated to the current swaption vol surface
- Output: OAS (bp), Z-spread, effective duration, effective convexity, key-rate durations (2y, 5y, 7y, 10y, 30y)
- For CMOs, report tranche-level metrics plus the average life window under slow/median/fast prepay assumptions
-
Assess convexity and optionality
- Calculate price response to ±50 bp and ±100 bp parallel shifts; derive empirical duration and convexity
- Quantify negative convexity cost: the OAS give-up attributable to the embedded prepay option versus a bullet swap at equivalent duration
- Evaluate PVBP asymmetry: how much more the bond loses in a rally vs. what it gains in a sell-off
- For specified pools, compute the pay-up (price premium over TBA) and the breakeven prepayment advantage needed to justify it
-
Run relative-value comparison
- Rank candidates by OAS per unit of effective duration or OAS vs. empirical vol-adjusted spread
- Compare carry + roll-down vs. comparable-duration Treasuries, swaps, or corporate bonds
- Assess dollar-roll specialness for TBA-eligible pools: implied financing rate vs. repo
- Factor in liquidity (bid-ask, daily volume, TBA deliverability) and balance-sheet cost (risk-weight, LCR treatment) [VERIFY regulatory capital treatment against current rules]
Output
- Executive summary: Recommended action (buy/hold/sell/switch) with 1-2 sentence rationale
- Collateral profile table: WAC, WAM, WALA, avg loan size, top-3 states, servicer, burnout indicator
- Prepayment exhibit: CPR vectors under base/bull/bear, comparison to recent actuals
- Risk metrics table: OAS, Z-spread, effective duration, effective convexity, key-rate durations
- Relative-value grid: OAS, carry, roll-down, and total-return rank across the comparison set
- Scenario matrix: Price and total-return outcomes under each rate/housing/refi scenario
- Key risks and caveats: Model risk, liquidity, regulatory-capital treatment, extension/contraction risk
Quality Checks
- Confirm OAS is computed against the correct benchmark curve (swaps vs. Treasuries) and state which was used
- Verify prepayment model vintage — stale model versions may mis-price the current refi incentive [VERIFY model version/date]
- Cross-check effective duration against empirical duration from recent price moves; flag discrepancies >0.3 years
- Ensure pay-up analysis for specified pools uses a consistent TBA base price (same settlement month)
- Validate that Monte Carlo path count is sufficient (typically >=500 paths) for OAS convergence within ±1 bp
- Confirm all prices are on the same settlement date and factor month
- Flag any pool with factor <0.10 as potentially illiquid with unreliable prepayment statistics
