Combining systemic and non-systemic risk scores

Robert M Oliver
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Robert M Oliver: University of California, Berkeley, CA, USA

Journal of the Operational Research Society, 2015, vol. 66, issue 1, 148-159

Abstract: This paper proposes a proportional odds model to combine systemic and non-systemic risk for prediction of default and prepay performance in cohorts of booked loan accounts. We assume that performance odds is proportional to two independent factors, one based on age-dependent systemic, possibly external, global disruptions to a cohort of individual accounts, the other on traditional non-systemic information odds based on demographic, behavioural and financial payment patterns of the individual accounts. A proportional odds model provides a natural formulation that can combine hazard rate predictions of baseline defaults, prepayments and active accounts with traditional non-systemic risk scores of individuals within the cohort. Theoretical comparisons with proportional hazard models are illustrated. Although our model is developed in terms of Good/Bad performance, it can include late payments, prepayments, defaults, as well as responses to offers and other classifications. We make 60-month default and prepay forecasts under two different systemic risk scenarios for a portfolio of Alt A mortgages with 24-month ‘teaser rates’ originated in 2004.

Date: 2015
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