Statistical Methods of Credit Risk Analysis

  • Terence M. Yhip
  • Bijan M. D. Alagheband


This chapter represents a big leap from expert-judgement modelling to purely quantitative/statistical modelling. The two approaches are vital and complementary tools in a bank’s risk assessment toolbox. The chapter examines the structure of the linear probability model and probit and logit analysis, shows the similarity and differences, and applies the methods to a sample of companies. It also provides step-by-step guidance to formulate a logit model, and explains how to perform a logit regression using actual data and interpret the logit regression results. As with all models, including expert-judgement models, the stability or reliability of the estimated parameters, descriptors, and weights is not a constant, which makes model validation necessary and essential. Poor validation can be costly to a lender.


Statistical modelling Linear probability model Probit and logit analysis Step-by-step guidance Estimated parameters Model validation 

Supplementary material

485627_1_En_8_MOESM1_ESM.pptx (2.3 mb)
Statistical methods of credit risk analysis (PPTX 2346 kb)

Copyright information

© The Author(s) 2020

Authors and Affiliations

  • Terence M. Yhip
    • 1
  • Bijan M. D. Alagheband
    • 2
  1. 1.University of the West IndiesMississaugaCanada
  2. 2.McMaster University and Hydro One Networks Inc.TorontoCanada

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