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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 analyze the value proposition and limitations of decentralized finance (DeFi). Based on a distributed ledger and smart contracts, DeFi can guarantee the execution of financial contracts, potentially lowering the costs of intermediation and improving financial inclusion.

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.

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.

We summarize the theoretical model of central bank asset purchases developed in Cimon and Walton (2022). The model helps us understand how asset purchases ease pressures on investment dealers to restore market conditions in a crisis.

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.

This paper evaluates the effectiveness of macroprudential policies when regulations are uneven across mortgage lender types. We look at credit tightening that results from macroprudential regulations and examine how much of it is counteracted by credit shifting to unregulated lenders. We also study the impact of monetary policy tightening when some lenders are unregulated.

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.