Probit PD Model

Install and import#

bash

npm install fintech-algorithms

import { probitPdModel } from "fintech-algorithms/credit-risk-and-default/probability-of-default/probit-pd-model";

Signature#

probitPdModel(intercept, coefficients, features, alert_threshold)

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

intercept

-1.5

coefficients

[0.7, -0.5, 0.9]

features

[0.6, -0.2, 0.3]

alert_threshold

0.25

Call#

probitPdModel(intercept, coefficients, features, alert_threshold)

Returns#

object with 7 fields: latent_score, feature_contributions, probability_of_default, survival_probability, alert_threshold, state, reason

{
  "latent_score": -0.71,
  "feature_contributions": [0.42, 0.1, 0.27],
  "probability_of_default": 0.23885206809,
  "survival_probability": 0.76114793191,
  "alert_threshold": 0.25,
  "state": "below-alert",
  "reason": "supplied-probit-score-transformed"
}

Other exports#

This module also exports logisticPdModel, throughTheCyclePd, pointInTimePd, mertonDistanceToDefault, campbellHilscherSzilagyiDistressProbability, bharathShumwayNaiveDistanceToDefault, calculate. Every module additionally exports run as an alias of its primary function, and a meta object carrying its catalog id, domain, family, shape and article URL.