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One argument for central bank digital currencies (CBDCs) is that without them, private and foreign digital monies could displace domestic currencies, threatening the central bank’s monetary policy and lender of last resort capabilities. I revisit this monetary sovereignty rationale and offer a wider view—one that considers a broader set of currency functions and captures important cross-country variation.

The Bank of Canada considers a wide range of information and analysis before making a monetary policy decision and uses carefully articulated models to produce economic projections and to examine alternative scenarios. This article describes an ongoing research agenda at the Bank to develop models in which financial variables play an active role in the transmission of monetary policy actions to economic activity. Such models can help to analyze information from the financial side of the economy and to provide an overall view of the implications of financial developments for the current economic outlook. The authors also explain how this research can help address other issues relevant to the objectives of monetary policy, including how asset-price movements should be taken into account in the monetary policy framework.

When a bank holds a lot of safe assets, it is well situated to deal with funding stress. But when all banks hold a lot of safe assets, a pecuniary externality implies that their (wholesale) funding costs increase. This reduces banks’ ability to hold capital buffers and thus, paradoxically, increases the frequency of funding stress.

Central banks face considerable uncertainty when conducting monetary policy. The COVID-19 pandemic brought this issue back to the forefront of policy discussions. We draw from academic literature to review key sources of uncertainty and how they affect the conduct of monetary policy.

One of the most important factors that must be considered if countries are thinking about lowering the target level of inflation much below 2 per cent is the zero interest bound. Targeting inflation rates that are too low, the authors note, may restrict the ability of monetary policy to respond to economic shocks by limiting the amount by which interest rates can be eased.

This article focuses on a quantitative method to identify financial system vulnerabilities, specifically, an imbalance indicator model (IIM) and its application to Canada. An IIM identifies potential vulnerabilities in a financial system by comparing current economic and financial data with data from periods leading up to past episodes of financial stress. It complements other sources of information - including market intelligence and regular monitoring of the economy - that policy-makers use to assess vulnerabilities.

This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data.

We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.