D21 / Territory 21 of 49

Credit Risk and Default

Estimate borrower and issuer loss.

7 released builds 1 algorithm familyIn release
D21 · CREDIT RISK AND DEFAULT
D21Canonical / Tested / Open

Published work

Released builds
in this territory.

Every card below is included in the current governed release with its article, sources, Python, TypeScript, and available labs.

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D21-F01

Probability of Default

7 builds
01

Credit Risk and Default / Probability of Default

Logistic PD Model

Turn a versioned linear credit score into an auditable probability, odds, contribution trace, and threshold state without pretending that scoring is calibration.
10 min Python + TypeScriptAdvanced
02

Credit Risk and Default / Probability of Default

Probit PD Model

Build an auditable probit inference path and compare its link geometry with logistic scoring without treating the two coefficient scales as interchangeable.
10 min Python + TypeScriptAdvanced
03

Credit Risk and Default / Probability of Default

Through-the-Cycle PD

Calculate a transparent long-run average one-year PD, retain the annual rates and pooled-rate comparison, and expose the representativeness assumptions behind through-the-cycle calibration.
10 min Python + TypeScriptAdvanced
04

Credit Risk and Default / Probability of Default

Point-in-Time PD

Build a log-odds PIT bridge that exposes borrower and macro shifts separately while making clear that IFRS 9 and regulatory implementations require much more than this teaching formula.
10 min Python + TypeScriptAdvanced
05

Credit Risk and Default / Probability of Default

Merton Distance-to-Default

Solve the coupled equity equations, separate physical and risk-neutral measures, and expose convergence, drift, default-point, and model-assumption risk.
10 min Python + TypeScriptAdvanced
06

Credit Risk and Default / Probability of Default

Campbell-Hilscher-Szilagyi Distress Probability

Reproduce the published score contribution by contribution while preserving the paper's variable definitions, lag, failure label, sample boundary, and recalibration warning.
10 min Python + TypeScriptAdvanced
07

Credit Risk and Default / Probability of Default

Bharath-Shumway Naive Distance-to-Default

Calculate naïve firm value, debt volatility, asset volatility, distance, and normal-CDF probability while preserving every approximation and empirical-use boundary.
10 min Python + TypeScriptAdvanced

Territory coverage

What this domain
is mapped to cover.

The published builds above are the released slice of this map. Every subject below belongs to this territory's charter.

Credit scoringPD/LGD/EADtransition matricesstructural default modelsreduced-form hazard modelssurvival analysisexpected credit loss