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We model the asset-opacity choice of an intermediary subject to rollover risk in wholesale funding markets. Greater opacity means investors form more dispersed beliefs about an intermediary’s profitability.

Mutual funds employ a host of tools to manage redemption run risk. However, our results suggest that Canadian corporate bond funds may be vulnerable to redemption runs, especially when they are less liquid and when market volatility is high.

A regulator would want to restrict dividends to force banks to rebuild capital during a crisis. But such a policy is not time-consistent. A time-consistent policy would let banks gradually rebuild capital and pay dividends even when their equity remains below pre-crisis levels.

We propose a range of benchmarks for assessing labour market strength for monetary policy. This work builds on a previous framework that considers how diverse and segmented the labour market is. We apply these benchmarks to the Canadian labour market and find that it has more than recovered from the COVID-19 shock.

The classical dichotomy predicts that all of the time-series variance in the aggregate real exchange rate is accounted for by non-traded goods in the consumer price index (CPI) basket because traded goods obey the Law of One Price. In stark contrast, Engel (1999) claimed the opposite: that traded goods accounted for all of the variance.

The COVID-19 pandemic highlighted the need for policy-makers to closely monitor disruptions to the retail and food business sectors. We present a new method to measure business opening and closing rates using real-time data from Google Places, the dataset behind the Google Maps service.

Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system.

We construct a macroeconomic estimate of total AI production in the United States, combining inference and R&D/training activities with quality adjustments to account for algorithmic progress. We then develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole, complementing traditional national accounts.

One year later, we review the events that took place in Canadian fixed-income markets at the beginning of the COVID-19 crisis and propose potential policy research questions for future work.