Statistical Methods of Credit Risk Analysis

Terence M. Yhip and Bijan M. D. Alagheband
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Terence M. Yhip: University of the West Indies
Bijan M. D. Alagheband: McMaster University and Hydro One Networks Inc.

Chapter 8 in The Practice of Lending, 2020, pp 351-381 from Springer

Abstract: Abstract 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.

Keywords: Statistical modelling; Linear probability model; Probit and logit analysis; Step-by-step guidance; Estimated parameters; Model validation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-32197-0_8

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