modeling-prepayment-and-default-scenarios

name	modeling-prepayment-and-default-scenarios
description	Builds CPR/CDR/severity vectors with scenario analysis across interest rate and economic environments. Use when modeling prepayment behavior, projecting default scenarios, or stress testing pool performance.
tags	["modeling","structured-finance"]
metadata	{"author":"casemark","practice_areas":["Structured Finance","Securitization","ABS/MBS/CLO"],"document_types":["Financial Model"],"skill_modes":["Modeling","Forecasting"]}

Modeling Prepayment And Default Scenarios

Builds CPR/CDR/severity vectors with scenario analysis across interest rate and economic environments for ABS, MBS, and CLO pools.

When To Use

• Projecting cash flows on a securitization tranche under multiple prepayment and default paths
• Stress testing pool performance for rating agency submissions or internal risk review
• Building base/upside/downside vectors for deal pricing or ongoing surveillance
• Evaluating how rate shifts, unemployment changes, or HPA movements alter bond returns
• Comparing model output to agency benchmarks (e.g., Moody's Idealized CDR curves, S&P LEVELS)

Inputs To Gather

• Pool tape: loan-level data including balance, coupon, LTV, FICO/credit score, seasoning, geography, loan purpose, occupancy type
• Collateral type: RMBS (agency/non-agency), auto ABS, student loan ABS, CLO, CMBS, or other — each drives model selection
• Rate environment assumptions: current benchmark curve (SOFR/Treasury), forward curve, and any shocked curves (+/- 100, 200, 300 bps)
• Macro scenarios: baseline, mild stress, severe stress — specify unemployment rate, HPI path, GDP growth as applicable
• Historical performance data: prior remittance reports, delinquency rolls, loss/recovery history for the deal or comparable deals
• Structural features: lockout periods, prepayment penalties, call provisions, clean-up calls, step-down triggers
• Target output granularity: monthly vs. quarterly vectors; single-path vs. matrix of scenarios

Workflow

1. Classify collateral and select model framework

   • RMBS: use PSA-based CPR ramp or econometric prepayment model (turnover + refinance incentive + burnout + seasoning)
   • Auto/consumer ABS: use ABS speed ramp (absolute prepayment speed or monthly payment rate)
   • CLO: model voluntary and involuntary prepayments separately; apply CDR based on rating cohort and industry mix
   • CMBS: apply balloon risk, voluntary defeasance/yield maintenance, and involuntary default vectors
   • [VERIFY] Confirm whether the deal uses single monthly mortality (SMM) or annualized CPR convention

2. Build CPR vectors

   • Construct a base-case CPR curve reflecting current rate incentive, seasoning, and borrower characteristics
   • Layer refinance incentive function: map WAC-to-market-rate spread against historical S-curves for the collateral type
   • Apply burnout adjustment: reduce refi sensitivity for pools with extended exposure to in-the-money conditions
   • Apply seasoning ramp: new-origination pools follow a ramp to steady-state CPR (e.g., 30-month PSA ramp for RMBS)
   • Generate shocked vectors: shift rate environment and recalculate incentive-driven CPR for each scenario

3. Build CDR and severity vectors

   • Specify baseline CDR curve: use historical roll-rate analysis (current → 30 → 60 → 90 → default) or rating agency benchmarks
   • Apply macro overlays: increase CDR under stress scenarios by mapping unemployment and HPI shocks to default frequency multipliers
   • Set loss severity assumptions by collateral type:
     • RMBS: 30-50% severity typical; vary by LTV band and state foreclosure timeline [VERIFY]
     • Auto ABS: 40-60% severity on defaulted units; adjust for used vs. new, recovery lag
     • CLO: recovery rates by seniority (senior secured ~70%, second lien ~40%, unsecured ~25%) [VERIFY]
   • Construct timing vectors: distribute defaults and recoveries across months with appropriate lag (typically 12-24 months from default to liquidation for RMBS)

4. Assemble scenario matrix

   • Define at minimum three scenarios: base, upside (low default / high prepay), downside (high default / low prepay)
   • For each scenario, pair a CPR vector with a CDR vector and a severity assumption
   • Optionally add a rating agency stress case using prescribed vectors (e.g., Fitch Deerfield, Moody's MILAN)
   • Run each scenario through the deal waterfall to produce tranche-level cash flows, WAL, yield, and principal window

Output

• Scenario summary table: for each scenario, display CPR (annualized), CDR (annualized), loss severity, resulting WAL, yield to maturity, and principal payment window
• Monthly vector schedules: time-series of SMM, MDR (monthly default rate), and recovery amounts for each scenario
• Tranche impact analysis: show how each tranche (AAA through equity) performs under each scenario — expected loss, credit enhancement erosion, trigger breaches
• Sensitivity heat map: matrix showing tranche yield or WAL as a function of CPR (rows) x CDR (columns)
• Key assumptions log: document every assumed input — rate curves, macro variables, model parameters, data cutoff date

Quality Checks

• Confirm CPR vectors are internally consistent: SMM × 12 annualization should reconcile with stated CPR
• Ensure CDR + CPR does not exceed 100% of performing balance in any period
• Verify severity assumptions reflect post-foreclosure costs (legal, REO maintenance, broker fees) not just collateral value haircuts
• Cross-check base-case WAL against Bloomberg or Intex model output for the same deal [VERIFY]
• Confirm that stressed scenarios produce credit enhancement shortfalls at the appropriate rating level
• Flag any input marked [VERIFY] before delivering final output — do not present unconfirmed assumptions as given
• Validate that recovery lag timing is realistic for the jurisdiction and collateral type (judicial vs. non-judicial foreclosure states produce materially different timelines)