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Central banks play a pivotal role in well-functioning payments systems by providing liquidity via collateralized lending. This article discusses the role of collateral and haircut policy in central bank lending, as well as the distinguishing features of the central bank’s policy relative to private sector practices. It presents a model that explicitly incorporates the unique role of central banks in the payments system and argues that central banks must consider how their haircut policies affect the relative price and liquidity of assets, the market’s asset allocation, and the likelihood of participants to default. Furthermore, under extraordinary circumstances, there is a rationale for the central bank to temporarily reduce haircuts or broaden the list of eligible collateral to mitigate the shortage of liquidity in the market.

The authors present the four common cyclical vulnerabilities that appear in financial systems, providing examples of qualitative and quantitative indicators used to monitor these vulnerabilities across different sectors. They also discuss other inputs to the vulnerability assessment and to the internal process used at the Bank of Canada for identifying, evaluating and communicating vulnerabilities and risks, and highlight some of the key challenges in assessing financial system vulnerabilities and risks.

This paper analyzes the optimal quantity of central bank reserves in an economy where reserves and other financial assets provide liquidity benefits. Using a static model, I derive a constrained Friedman rule that characterizes the socially optimal level of reserves, demonstrating that this quantity is neither necessarily large nor small but depends on the marginal benefits of reserves relative to alternative safe assets.

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

We present a two-stage model of mobile phone and mobile payment usage that controls for selectivity. This reveals unobserved factors that work against having a mobile phone and toward mobile paying. Therefore, people who are unable to acquire or choose not to own a mobile device might have unmet payment needs.

Commodity prices are once again making headlines. Some commodity prices, such as those for copper and cattle, have reached record highs; others are rising quickly and approaching previous peaks.

We analyze micro-level data from the Canadian Survey of Consumer Expectations through the lens of a heterogeneous-expectations model to study how inflation expectations form over the business cycle. We provide new insights into how households form expectations, documenting that forecasting behaviours, attention and noise in beliefs vary across socio-demographic groups and correlate with views about monetary policy.

This paper studies how the outcome of Bayesian persuasion depends on a sender’s information. I study a game in which, prior to the sender’s information disclosure, the designer can restrict the most informative signal that the sender can generate.

This report provides a detailed technical description of the updated MacroFinancial Risk Assessment Framework (MFRAF), which replaces the version described in Gauthier, Souissi and Liu (2014) as the Bank of Canada’s stress-testing model for banks with a focus on domestic systemically important banks (D-SIBs).

In recent years, the Bank has put renewed emphasis on analyzing monetary variables and on developing models that incorporate money as an active part of the transmission mechanism. In this article, Dinah Maclean describes how the monetary aggregates are used in the formulation of monetary policy analysis at the Bank, outlining the key tools and models used. The most important money-based model currently in use is the M1-VECM. In this model, deviations in the money supply from the long-term demand for money cause changes in inflation. The author briefly describes the "active-money" paradigm underlying this model and explains the key equations within it. Other simpler empirical models are also outlined, including single-equation indicator models for output based on the narrow aggregates, a neural network, and a model based on the broader aggregate M2++. A detailed technical annex provides details on model equations and coefficient values.