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Statistical Methods of Credit Risk Analysis

In: The Practice of Lending

Author

Listed:
  • Terence M. Yhip

    (University of the West Indies)

  • Bijan M. D. Alagheband

    (McMaster University and Hydro One Networks Inc.)

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.

Suggested Citation

  • Terence M. Yhip & Bijan M. D. Alagheband, 2020. "Statistical Methods of Credit Risk Analysis," Springer Books, in: The Practice of Lending, chapter 8, pages 351-381, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-32197-0_8
    DOI: 10.1007/978-3-030-32197-0_8
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    Cited by:

    1. Huang, Yu-Ju & Mohr, Gabriela & Cheung, Monit & Leung, Patrick, 2024. "Parental access to ‘Sexual Conviction Record Check’ sex offender registry in Hong Kong," Children and Youth Services Review, Elsevier, vol. 156(C).
    2. Ogawa, Keishi & Garrod, Guy & Yagi, Hironori, 2023. "Sustainability strategies and stakeholder management for upland farming," Land Use Policy, Elsevier, vol. 131(C).

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