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The applicability of statistical techniques to credit portfolios with specific reference to the use of risk theory in banking

O'Connor, R.B. (1996). The applicability of statistical techniques to credit portfolios with specific reference to the use of risk theory in banking. (Unpublished Doctoral thesis, City University London)


This thesis examines the use of statistical techniques in credit portfolio management, with emphasis on actuarial and risk theoretic pricing and reserving measures. The bank corporate loan portfolio is envisaged as an insurance collective, with margins charged for credit risk forming premium income, provisions made forming claims outgo, and variation over time in provisioning and profitability producing a need for reserves. The research leads to the computation of portfolio specific measures of risk, and suggests that a value-at-risk (VAR) based reserve computation has several advantages over current regulatory reserving methodology in bank capital adequacy regulation (CAR) with respect to non-residential private sector loan portfolios. These latter, current CAR practices are invariably used by banks to compute the respective capital adequacy backing required on loans. A loan pricing model is developed that allocates capital required by reference to observed provisioning rates across a total of 64 differing combinations of rating factors. This represents a statistically rigorous return on risk adjusted capital (RORAC) approach to loan pricing. The suggested approach is illustrated by reference to a particular portfolio of loans. The reserving and pricing measures computed are portfolio specific, but the methodology developed and tested on the specific portfolio (dataset) of loans has a wider, more general applicability. A credit market comprising portfolios which are both more and less risky than the original particular portfolio is hypothesised, and existing regulation is compared to VAR regulation in the context of the hypothesised credit market. Fewer insolvencies are observed using the VAR framework than under existing regulation, and problem portfolios are identified earlier than under existing regulation. For the particular portfolio of loans, existing algorithm-based loan pricing is compared with the proposed loan pricing model. Significant differences are observed in loan pricing by reference to gearing and collateral, and the elimination of observed inefficiencies in pricing is recommended. Although the proposed model has some limitations, it is argued to be an improvement on existing regulatory and banking practice.

Publication Type: Thesis (Doctoral)
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School > Actuarial Science & Insurance > Statistical Research Reports
Doctoral Theses
Bayes Business School > Bayes Business School Doctoral Theses
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