casemark/analyzing-agency-mortgage-bonds

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

1. 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

2. 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

3. 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

4. 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

5. 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]

6. Stress-test and scenario analysis

   • Run total-return projections over the target horizon under base, bull, and bear rate scenarios plus a refi-wave overlay
   • Shock housing prices (–10%, –20%) and evaluate CDR sensitivity for credit-risk transfer reference pools if applicable
   • Identify breakeven spread widening: how many bp of OAS widening erases the carry advantage over the alternative

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