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09:45 (ET)
On eight scheduled dates each year, the Bank of Canada announces the setting for the overnight rate target in a press release explaining the factors behind the decision.

On Wednesday, March 18, 2026, the Bank of Canada will announce its decision on the target for the overnight rate. A press release will provide a brief explanation of the decision.

This analytical paper examines the organization of Government of Canada bond dealing. We focus on dealers’ hedging and funding practices, the market infrastructures that support those practices, and trading costs across the yield curve. This paper builds on earlier work discussing Canada’s fixed-income market: "Government of Canada Fixed-Income Market Ecology."

How can Surveys of Professional Forecasters (SPF) be used to improve inflation forecasts? By using US historical quarterly data on SPF forecasts, we provide better understanding of how we can use forecast disagreement to improve our own forecasts.

We develop an approach to quantify transaction costs in the repo market using OTC transaction data, where quoted bid-ask spreads are not observable. By estimating effective spreads at the level of individual trades, we construct a novel metric to evaluate intermediation costs across different segments of the market.

We study the competition between a welfare-maximizing public payment platform (e.g., CBDC or fast payment system) and a profit-maximizing private platform in a two-sided market, deriving optimal public pricing and showing how network effects, fragmentation, and policy mandates like zero fees or cost recovery shape welfare, usage, and fee incidence.

The Governor’s Challenge (GC) by the Bank of Canada marked its tenth year as a successful outreach and education initiative, designed to strengthen public understanding of macroeconomics and monetary policy.

This paper examines the interaction between quantitative easing (QE) and the securities lending facility (SLF) using a detailed dataset on Riksbank QE purchases, Swedish DMO SLF transactions and OTC repo deals. A theoretical model further shows how excess demand for assets and search frictions shift the SLF from a backstop to a first-resort tool.

Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models.