modeling-credit-enhancement-requirements
Calculates required credit enhancement levels with loss modeling, attachment/detachment points, and rating agency methodology. Use when sizing credit enhancement, modeling loss scenarios, or determining tranche subordination.
Best use case
modeling-credit-enhancement-requirements is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Calculates required credit enhancement levels with loss modeling, attachment/detachment points, and rating agency methodology. Use when sizing credit enhancement, modeling loss scenarios, or determining tranche subordination.
Teams using modeling-credit-enhancement-requirements should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/modeling-credit-enhancement-requirements/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How modeling-credit-enhancement-requirements Compares
| Feature / Agent | modeling-credit-enhancement-requirements | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Calculates required credit enhancement levels with loss modeling, attachment/detachment points, and rating agency methodology. Use when sizing credit enhancement, modeling loss scenarios, or determining tranche subordination.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Modeling Credit Enhancement Requirements ## When To Use - Sizing subordination levels for new ABS, MBS, or CLO issuances - Determining attachment and detachment points for rated tranches - Stress-testing existing credit enhancement against revised loss assumptions - Responding to rating agency feedback on proposed capital structures - Evaluating whether overcollateralization, excess spread, or reserve accounts provide sufficient protection at target rating levels ## Inputs To Gather - **Collateral pool data**: loan-level tape with balances, rates, LTVs, FICOs, seasoning, geographic concentration, and obligor industry (CLO) - **Historical performance**: static pool loss curves, delinquency roll rates, prepayment speeds, recovery rates and recovery lag by vintage - **Target rating levels**: desired ratings per tranche (e.g., AAA/Aaa senior, BBB/Baa2 mezz) - **Rating agency methodology**: applicable criteria document and version (e.g., S&P LEVELS model for RMBS, Moody's CDOROM for CLO, Fitch multiples approach for ABS) [VERIFY methodology version is current] - **Deal structural features**: waterfall priority, interest/principal payment mechanics, triggers (OC tests, delinquency triggers), turbo provisions, liquidity facilities - **Market benchmarks**: comparable deal credit enhancement levels by asset class and rating tier ## Workflow 1. **Analyze the collateral pool** - Stratify the pool by key risk drivers (LTV bands, FICO buckets, geographic/industry concentration) - Compute weighted-average collateral characteristics - Identify tail-risk concentrations (single obligor, single geography, vintage clustering) 2. **Build the base-case loss model** - Select loss methodology: frequency × severity, loss curve extrapolation, or transition-matrix approach depending on asset class - Calibrate default frequency using historical static pool data; apply seasoning and vintage adjustments - Set loss severity assumptions using historical recovery data, haircut for liquidation lag and costs - For CLO: model using Monte Carlo simulation with correlated default (asset correlation by industry pair) - For RMBS: apply loan-level loss model with HPI stress overlays [VERIFY applicable HPI stress scenarios per agency] 3. **Determine stressed loss scenarios by rating level** - Apply rating-agency-specific stress multiples to base-case losses (e.g., Fitch AAAsf typically 4–5× base case for prime auto ABS) [VERIFY current multiples per asset class] - For S&P LEVELS-based analysis: run the LEVELS model to produce break-even loss levels per rating category - For Moody's: compute expected loss and Moody's idealized loss rate mapping to target rating - Layer in timing stress: front-loaded vs. back-loaded loss curves and their impact on excess spread availability 4. **Size credit enhancement components** - **Subordination**: set attachment point for each tranche so that stressed cumulative losses at target rating do not breach the tranche - **Overcollateralization (OC)**: size initial OC and OC floor; model OC build-up from excess spread over time - **Excess spread**: project net WAC minus cost of funds minus servicing fees; stress for rising defaults and prepayments reducing gross WAC - **Reserve account**: size funded reserve (typically 0.25%–1.0% of initial pool balance); determine draw and replenishment mechanics - **External enhancement**: size any LOC, surety bond, or guaranty if applicable; note counterparty rating dependency [VERIFY counterparty minimum rating requirements] 5. **Set attachment and detachment points** - Map total required credit enhancement to tranche subordination percentages - Confirm each tranche detachment point equals the next senior tranche attachment point (no gaps) - Validate that the equity/first-loss piece absorbs expected losses plus a margin before impacting rated notes 6. **Run sensitivity and stress analysis** - Vary default rate (±25%, ±50%), severity (±10 pp), prepayment speed (0.5× to 2× base CPR/CDR), and recovery lag - Test trigger breaches: at what loss level do OC or IC triggers divert cash from junior to senior tranches - Run break-even analysis: determine the maximum cumulative default rate each tranche survives at par - For CLO: test WARF migration, CCC bucket concentration, and par erosion scenarios 7. **Document and present** - Summarize base-case and stressed loss assumptions with sources - Present credit enhancement waterfall showing each component's contribution - Include tranche-level break-even table and sensitivity matrix - Flag any areas where enhancement levels are tight relative to comparable deals ## Output - **Credit enhancement summary table**: tranche name, rating, attachment %, detachment %, total CE %, CE composition (subordination + OC + excess spread + reserve) - **Loss model outputs**: base-case cumulative loss, stressed losses by rating tier, loss timing curves - **Sensitivity matrix**: tranche survival under varied default, severity, prepayment, and recovery assumptions - **Break-even analysis**: maximum default rate each tranche absorbs before principal impairment - **Structural waterfall diagram**: priority of payments with trigger levels annotated - **Comparables benchmarking**: CE levels vs. recent comparable issuances ## Quality Checks - Confirm attachment/detachment points are contiguous and sum correctly to 100% of the capital structure - Verify that AAA/Aaa CE exceeds the stressed loss at the corresponding rating level with adequate cushion - Cross-check CE levels against at least 3 comparable recent deals in the same asset class [VERIFY deal comps are within 12 months] - Validate that excess spread projections account for collateral WAC compression from prepayments and defaults - Ensure loss model inputs tie to auditable source data (servicer reports, trustee reports, static pool supplements) - Confirm trigger levels are internally consistent with waterfall mechanics (OC trigger should breach before IC trigger in a stress) - Review whether the model handles reinvestment period mechanics correctly (CLO) or amortization profiles (ABS/RMBS) - Flag any credit enhancement level below the minimum observed in comparable rated transactions