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This paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties.

This note uses Charbonneau and Landry’s (2018) framework to assess the direct impact of the current trade tensions on the Canadian and global economies, as well as possible implications if the conflict escalates further. Overall, my findings show that the estimated impact of current tariffs on real gross domestic product (GDP) remains relatively small, which is in line with the literature on gains from trade, but the impact on trade is much larger.

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

We present the annual update of the Bank of Canada staff estimates for global potential output growth. These estimates served as key inputs to the analysis supporting the April 2026 Monetary Policy Report.

Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms.

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 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.

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.

The secular decline in real interest rates has created a challenge for monetary policy, now confronting the zero lower bound more often. An increase in the supply of safe assets reduces downward pressure on the natural interest rate. This allows monetary policy to reach price stability and full employment, but not without cost—permanently lower investment.