credit-risk-modelslisted

Estimate a default probability and price credit, and keep the two probabilities apart - the risk-neutral one that prices and the physical one that forecasts. TRIGGER - Merton model, structural credit model, KMV, distance to default, asset value and asset volatility from equity, N(-d2), solve the two Merton equations; hazard rate, intensity, reduced form, survival probability, constant hazard, credit curve bootstrapping; CDS par spread, premium leg, protection leg, risky PV01, RPV01, accrual on default, "spread = lambda times one minus recovery", implied hazard from a CDS spread, recovery assumption, 40% recovery; risk-neutral vs physical default probability, rating agency default table, "my CDS spread is too low", credit spread from a bond price, expected loss, CVA default probability. SKIP for option pricing and Greeks (option-pricing-models, derivatives-pricing), for interest-rate curves and short-rate models (term-structure-models), and for portfolio risk and VaR (portfolio-and-risk).
howard-lynn-ye/fin-skills · ★ 1 · AI & Automation · score 77

Install: claude install-skill howard-lynn-ye/fin-skills

# Credit risk models **Two model families each produce a number called "the probability of default", and they are not the same number.** Merton's `N(−d2)` is **risk-neutral**; a rating-agency default table is **physical**. Using one where the other belongs is the most expensive mistake in this domain, and neither one carries a label. Every figure below is printed by `scripts/credit_models.py` (runs in **1.8 s**; QuantLib optional, imported inside `quantlib_cds_cross_check`). ✅ Measured means this file produced it on 2026-09-09 with QuantLib 1.43, scipy 1.13.0, Python 3.11.3. > **The rule:** `N(−d2)` is risk-neutral; a rating-table default rate is physical. **Price with > the first, forecast with the second**, and never quote a hazard without its recovery. ## 1. ✅ Merton (1974), reproducing a textbook example Equity is a call on the firm's assets struck at the face value of debt. Two equations pin the two unobservables: E = V N(d1) - D e^{-rT} N(d2) sigma_E = N(d1) sigma_V V / E ⚠️ **The reference:** Hull, *Options, Futures, and Other Derivatives*, the worked example in the credit-risk chapter on using equity prices to estimate default probabilities. **Inputs: E = 3, sigma_E = 80%, D = 10 payable in 1 year, r = 5%.** The book reports V = 12.40, sigma_V = 0.2123, N(−d2) = 12.7%, debt worth 9.40 against a promised PV of 9.51, and an expected loss of about 1.2%. Those published values are secondhand; everything in the "solved" column is ✅ measured here. | qua