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Non-default losses of financial market infrastructures (FMIs) have gained attention due to their potential impacts on FMIs and FMI participants, and the lack of a common approach to address them. A key question is, who should absorb these losses?

We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

We examine how changes in the Bank of Canada’s balance sheet impact the banking system. Quantitative easing contributed to an increase in the size of the banking system’s balance sheet and an improvement in bank liquidity coverage ratios. Quantitative tightening is expected to partially reverse these impacts. The banking system will have to adjust its liquidity management strategy in response.

The COVID-19 pandemic uncovered policy challenges related to the economic measures that were taken to support the economy. Two years later, we attempt to identify the broader impact of these measures and research that needs to follow.

The BoC–BoE database of sovereign debt defaults, published and updated annually by the Bank of Canada and the Bank of England, provides comprehensive estimates of stocks of government obligations in default.

Using a nonlinear New Keynesian model with a financial accelerator, we show that financial frictions generate large state-dependent amplification effects. Shocks propagate more strongly in periods of financial stress. We propose an endogenous regime-switching DSGE framework for efficient estimation and improved model fit.

We develop a principal-agent model of cyber-attacking with fee-paying clients who delegate security decisions to financial platforms. We derive testable implications about clients’ vulnerability to cyber attacks and about the fees charged.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

We assess whether more central clearing would enhance the resilience of Canadian fixed-income markets. Our analysis estimates the potential benefits of balance sheet netting under scenarios where central clearing is expanded to new participants.

We examine the potential impacts of a severe economic shock on the resilience of major banks in Canada. We find these banks would suffer significant financial losses but nevertheless remain resilient. This underscores the role well-capitalized banks and sound underwriting practices play in supporting economic activity in a downturn.