Campbell-Hilscher-Szilagyi Distress Probability

Install and import#

npm install fintech-algorithms

import { campbellHilscherSzilagyiDistressProbability } from "fintech-algorithms/credit-risk-and-default/probability-of-default/campbell-hilscher-szilagyi-distress-probability";

Signature#

campbellHilscherSzilagyiDistressProbability(nimtaavg, tlmta, exretavg, sigma, rsize, cashmta, market_to_book, log_price)

Evaluates the published Campbell, Hilscher and Szilagyi twelve-month-lag logit with its eight accounting and market inputs, returning the failure log-odds, the distress probability, and each variable's contribution. The coefficients are fixed inside the function, not supplied by the caller.

Parameters#

Returns#

{ published_intercept: number; score_contributions: Record<string, number>; failure_log_odds: number; distress_probability: number; state: string; reason: string }

published_intercept is the paper's constant of -9.164 and score_contributions maps each input name to its value times the published coefficient. failure_log_odds is the intercept plus those contributions and distress_probability is the logistic transform of it. state is published-score-replication and reason names the table the coefficients come from.

Errors#

• When any argument is not a finite number — throws Error
• When sigma, tlmta, or cashmta is negative — throws Error

Complexity: time O(1), space O(1).

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#

-0.015

0.62

-0.04

0.48

-8.5

0.08

1.8

2.3

Call#

campbellHilscherSzilagyiDistressProbability(nimtaavg, tlmta, exretavg, sigma, rsize, cashmta, market_to_book, log_price)

Returns#

object with 6 fields: published_intercept, score_contributions, failure_log_odds, distress_probability, state, reason

{
  "published_intercept": -9.164,
  "score_contributions": {
    "nimtaavg": 0.30396,
    "tlmta": 0.87792,
    "exretavg": 0.28516,
    "sigma": 0.67728,
    "rsize": 0.3825,
    "cashmta": -0.17056,
    "market_to_book": 0.135,
    "log_price": -0.1334
  },
  "failure_log_odds": -6.80614,
  "distress_probability": 0.00110573352,
  "state": "published-score-replication",
  "reason": "chs-table-4-twelve-month-lag-coefficients"
}

Other exports#

This module also exports logisticPdModel, probitPdModel, throughTheCyclePd, pointInTimePd, mertonDistanceToDefault, 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.

Diagrams#

Calculation flow#

flowchart LR
    S1["Verify the paper version failure label coefficient row"]
    S2["Validate all eight inputs and their scoretime availabi"]
    S3["Multiply each variable by its published coefficient"]
    S4["Add the 9164 intercept and apply the logistic transfor"]
    S5["Return contributions score probability and an explicit"]
    S1 --> S2
    S2 --> S3
    S3 --> S4
    S4 --> S5
    S5 --> D{"any lag feature winsorization or coefficient mismatch inva"}
    D --> O["distress_probability + diagnostics"]
    O --> A["Audit: failurelogodds equals 9164 plus every reported contributio"]

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

• In Search of Distress Risk — John Y. Campbell, Jens Hilscher, and Jan Szilagyi
• Supervisory Guidance on Model Risk Management — OCC, Board of Governors of the Federal Reserve System, and FDIC
• Evidence boundary

The rest of the Probability of Default family#

• Logistic PD ModelApplies a logistic link to a supplied linear credit score and reports the resulting default probability alongside the per-featu…
• Probit PD ModelApplies the standard normal cumulative distribution to a supplied linear latent score and reports the resulting default probabi…
• Through-the-Cycle PDTurns a multi-year history of obligor counts and default counts into annual default rates, averages them into a through-the-cyc…
• Point-in-Time PDOverlays a borrower-specific shift and a macro shift on the log-odds of a through-the-cycle default probability, returning the …
• Merton Distance-to-DefaultSolves the two-equation Merton system iteratively for the unobserved asset value and asset volatility implied by observed equit…
• Bharath-Shumway Naive Distance-to-DefaultComputes the Bharath and Shumway naive distance to default, which approximates the Merton solve with closed-form substitutes fo…