Combining systemic and non-systemic risk scores

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.