PD Segmentation (3/3): Python Implementation and Validation Tests
Implement fixed PD bands in Python, calculate segment calibration and uncertainty, test boundaries and ordering, and validate the design with time-based holdouts.
Credit risk topic
Probability of default (PD)
Define, estimate, calibrate, and project default probabilities.
Guides and worked examples
PD Segmentation (2/3): Monitoring Calibration, Drift, and Migration
A practical PD-segment monitoring framework for assignment quality, population shifts, grade migration, observed-versus-expected defaults, uncertainty, and escalation.
PD Segmentation (1/3): Common Methods and How to Choose
A practical guide to product splits, score bands, expert rules, trees, model-based grades, and clustering—with a step-by-step process for choosing and testing PD segments.
Default Rate, Observation Window, and Performance Period: Choosing 60 or 90 DPD
Define default-rate targets, distinguish observation and performance windows, compare 60- and 90-day delinquency labels, and monitor PD outcomes with synthetic examples.
Estimating a One-Year Probability of Default
A practical guide to defining the default horizon, fitting a logistic PD model, calibrating its output, and validating estimates with matured outcomes.
Default events and observation windows
Define the event, unit, and horizon before estimating probability of default.
One-year PD and lifetime PD
Keep the risk horizon and survival conditioning explicit when comparing PD measures.
PD calibration: point-in-time and through-the-cycle
Separate ranking from the calibration that assigns a probability scale to grades or scores.
Transition matrices and rating migration
Use rating migration to describe movement and derive horizon risk only under explicit assumptions.
Survival, hazard, and macro-conditioned PD term structures
Build period-by-period risk while accounting for surviving accounts and changing conditions.