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This article examines what market conditions lead to extreme losses in global financial systems. Using a reverse stress testing approach, it introduces two measures of systemic risk by starting from the tail losses and working backward to identify the events most closely associated with them.

This paper studies how financial shocks shape the distribution of output growth by introducing a quantile-augmented vector autoregression (QAVAR), which integrates quantile regressions into a structural VAR framework. The QAVAR preserves standard shock identification while delivering flexible, nonparametric forecasts of conditional moments and tail risk measures for gross domestic product.

Trading flows affect Government of Canada bond prices. Our estimates suggest a sale of 1% of the available supply of bonds typically lowers bond prices by 0.2%. From 2000 to 2025, demand from institutional investors, such as Canadian pension funds and foreign investors, explains 69% of quarterly price variation, with the remainder explained by changes in the supply of bonds.

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions.

We propose a novel approach to estimating consumer demand for differentiated products. We eliminate the need for instrumental variables by assuming demand shocks are sparse. Our empirical applications reveal strong evidence of sparsity in real-world datasets.

We analyze the dynamic and heterogeneous responses of small-business loan performance to a monetary-policy shock using loan-level data in Canada. We find evidence of monetary policy transmission through the cash-flow channel and the aggregate demand channel as well as some, though limited, impact of collateral to discipline loan repayment.

When it comes to understanding the influence of labour costs on inflation, average wage growth is a misleading indicator because it is affected by composition effects. We propose an alternative measure that corrects for these effects by using microdata from the Labour Force Survey. Our new measure has many desirable properties, including reduced volatility and a better relationship with labour market fundamentals.

We analyze 40 years’ worth of natural disasters using a local projection framework to assess their impact on provincial labour markets in Canada. We find that disasters decrease hours worked within a week and lower wage growth in the medium run. Our study highlights that disasters affect vulnerable workers through the income channel.

We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes.

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.