In this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimated using two main statistical approaches based on extreme value models: Block-Maxima for different window lengths (daily, weekly and monthly) and Peak-over-Threshold. Our statistical model implies that the largest daily net debit position across participants exceeds roughly $1.5 billion once a year. Despite relying on extreme-value theory, the out of sample forecasts may still underestimate an actual exposure given the absence of observed data on defaults and financial stress in Canada. Our results are informative for optimal collateral management and system design of pre-funded retail-payment schemes.

Additional Metadata
Keywords bias correction, Big Data, credit risk, extreme value models
Persistent URL dx.doi.org/10.1515/jbnst-2018-0024
Journal Journal of Economics and Statistics
Citation
Sabetti, L. (Leonard), Jacho-Chávez, D.T. (David T.), Petrunia, R. (Robert), & Voia, M.-C. (2018). Tail Risk in a Retail Payments System. Journal of Economics and Statistics. doi:10.1515/jbnst-2018-0024