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This paper studies how the impact of a cyber attack that paralyzes one or multiple banks' ability to send payments would transmit to other banks through the Canadian wholesale payment system. Based on historical payment data, we simulate a wide range of scenarios and evaluate the total payment disruption in the system.

Using an exclusive data set of payment times for retail transactions made in Canada, I show that cash is the most time-efficient method of payment (MOP) when compared with payments by debit and credit cards. I model payment efficiency using Cox proportional hazard models, accounting for consumer choice of MOP.

This note presents a composite indicator of Canadian financial system vulnerabilities—the Vulnerabilities Barometer. It aims to complement the Bank of Canada’s vulnerabilities assessment by adding a quantitative and synthesized perspective to the more granular (distributional) analysis presented in the Financial System Review.

A number of central banks publish their own business conditions surveys based on complex non random sampling methods. The results of these surveys influence monetary policy decisions and thus affect expectations in financial markets. To date, however, no one has computed the accuracy of these surveys because their respective non-random sampling method renders this assessment non-trivial. This paper describes a methodology for modeling complex non-random sampling behaviour, and computing relevant measures of statistical confidence, based on a given survey’s historical selection practice. We apply this framework to the Bank of Canada’s Business Outlook Survey by describing the sampling method in terms of rules-based criteria, historical practices, and Bayesian probabilities. This allows us to replicate the firm selection process using Monte Carlo simulations on a comprehensive micro-dataset of Canadian firms. We find, under certain assumptions, no evidence that the Bank’s firm selection process results in biased estimates and/or wider confidence intervals.