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We show how participants in Canada’s new high-value payment system save liquidity by exploiting the new gridlock resolution arrangement. The findings have important implications for the design of these systems and shed light on financial institutions’ liquidity preference.

Investors who trade based on good research are said to be the backbone of stock markets: They conduct research to discover the value of stocks and, through their trading, guide financial prices to reflect true value. What can make their job difficult is that high-speed, short-term traders could use machine learning and other technologies to infer when informed investors are trading.

Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

Consumers, businesses and banks make millions of payments each day using a variety of instruments, such as debit cards, cheques and wires. Canada is currently developing three new systems to process these transactions: Lynx, Settlement Optimization Engine (SOE) and Real-Time Rail (RTR).

An anonymous credential mechanism is a set of protocols that allows users to obtain credentials from an organization and demonstrate ownership of these credentials without compromising users’ privacy. In this work, we construct the first secret-free and quantum-safe credential mechanism.

We develop a model in which a financial intermediary’s investment in risky assets—risk taking—is excessive due to limited liability and deposit insurance and characterize the policy tools that implement efficient risk taking.

We investigate whether hedge funds contributed to Government of Canada bond market illiquidity at the onset of the Middle East war in early 2026. We find no evidence they did. Hedge funds remained active in primary auctions, while their secondary market trading remained broadly consistent with their usual relative-value strategies.

We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.

We introduce a model to detect periods of extrapolative house price expectations across Canadian cities. The House Price Exuberance Indicator can be updated on a quarterly basis to support the Bank of Canada’s broader assessment of housing market imbalances.