quantumquirkxyz Contract
- Input: problem description and inputs defined by the skill body.
- Output: Markdown artifact with completed process steps.
- Side effects: none.
- Dependencies: none.
- Stop condition: all process steps executed; artifact saved with required sections.
- Risk: low.
- Boundary: produces reasoning artifact only; no system changes.
Credit Risk Modeling
Model credit risk at the instrument and portfolio level — PD, LGD, EAD, EL, UL — with default correlation and aggregation.
When to use
- The user wants to model credit exposure, expected loss, or portfolio credit risk.
- A loan book, bond portfolio, or corporate credit desk needs risk quantification.
- A Basel / IFRS 9 / CECL calculation is needed.
Process
- Define scope — single name / portfolio; rating grade; time horizon (1Y PD, lifetime PD).
- Estimate PD — historical default frequency,迁移矩阵, KMV-Merton model, or logistic regression on financial ratios.
- Estimate LGD — workout LGD vs market LGD; recovery rate; downturn LGD (regulatory).
- Estimate EAD — drawn amount + credit conversion factor (CCF) for off-balance-sheet.
- Compute Expected Loss (EL) = PD × LGD × EAD; Unexpected Loss (UL) = √(variance of loss).
- Default correlation — Basel rho formula (equity-based), or copula (Gaussian, t-copula). Aggregate portfolio loss distribution.
- Stress testing — PD + 2 grades, LGD × 1.5, portfolio loss at 99.9%.
- Deliver — artifact: PD/LGD/EAD estimates with methodology, EL/UL, correlation, stress test, and regulatory compliance note (Basel III / IFRS 9).