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We study liquidity requirements in a framework with fire sales. The framework nests three common pricing mechanisms and produces the same observables. Absent risk-sharing considerations, the equilibrium is efficient with cash-in-the-market pricing; a liquidity requirement is optimal with second-best-use pricing; and a liquidity ceiling (i.e., a cap on liquid assets) is optimal with adverse selection.

We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking.

Coholder households simultaneously carry high-cost credit card debt and low-yield cash. We study the implications of this behavior for fiscal and monetary policy, finding that coholder households have smaller consumption responses in the short run but larger responses in the long run.

Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems.

We quantitively assess the risks of a wage-price spiral occurring in Canada over history. We find the risk of a wage-price spiral increases when the inflation expectations become unanchored and the credibility of central banks declines.

I develop a population-based Markov chain Monte Carlo algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors.

Using two complementary approaches, we investigate the importance of U.S. macroeconomic news in driving low-frequency fluctuations in the term structure of interest rates in Canada, Sweden and the United Kingdom. We find that U.S. macroeconomic news is particularly important to explain changes in the expectation components of the nominal, real and break-even inflation rates of small open economies.

We investigate the unintended consequences of the Home Affordable Refinance Program (HARP). Originally designed to help borrowers refinance after the 2008–09 global financial crisis, HARP inadvertently strengthened the market power of incumbent lenders by creating a cost advantage for them. Despite a 2013 policy rectifying this cost advantage, we still find significant welfare losses for borrowers.

We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature.

We examine the optimal amount of user anonymity in a central bank digital currency in the context of bank lending. Anonymity, defined as the lender’s inability to discern an entrepreneur’s actions that enable fund diversion, influences the choice of payment instrument due to its impact on a bank’s lending decisions.