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This note analyzes different economic models of a central bank digital currency (CBDC) ecosystem where the central bank chooses different levels of market involvement and usage of policy levers. The analysis suggests that there are trade-offs between the costs to the central bank and its ability to achieve policy goals like universal access.

We investigate the risks and opportunities to the mandates of central banks arising from fintech developments.

We assess how rising exports of US liquefied natural gas affect the convergence of natural gas prices worldwide. Our results may have implications for the development of future LNG export capacity in Canada.

This paper develops a general equilibrium model to assess central bank digital currency (CBDC) design in a monetary system where traditional banks and “crypto banks” (i.e., banks that issue stablecoins) coexist. We compare tokenized and non-tokenized CBDC, showing that their desirability depends on the reliability of private money provision, the availability of collateral assets and the features of the crypto sector.

In Monitoring Shadow Banking in Canada: A Hybrid Approach, Bo Young Chang, Michael Januska, Gitanjali Kumar and André Usche discuss how lending that occurs outside the traditional banking system provides benefits to the economy but must be monitored carefully for potential financial sector vulnerabilities. They describe how the Bank defines and measures shadow banking and how it assesses vulnerabilities in the sector, using an approach that examines both markets and entities.

We study access to Canadian retail banking and cash services in rural, localized markets using the Bresnahan-Reiss entry threshold framework. We find that the first retail banking services branch entry requires about 500 residents in an average market, whereas the first cash services location requires about 80 residents.

As part of modernizing its core payments infrastructure, Canada will replace the Large Value Transfer System (LVTS) with a new Real-Time Gross Settlement (RTGS) system called Lynx. An important question for policy-makers is how Lynx should be designed.

Privacy-enhancing technologies (PETs) could offer solutions to safeguard end-user privacy and meet rigorous data protection standards for central bank digital currencies. We consider how PETs can transform privacy design in financial systems and the implications of their broader adoption.

This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.