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This paper shows that regional disparities in house price growth are more pronounced during house price busts than during booms. To explain this observation we construct a two-region currency union model incorporating a housing sector and extrapolative belief updating regarding house prices. To solve the model, we propose a new method that efficiently handles extrapolative belief updating in a wide class of structural models.

Security is an important element in ensuring public confidence in a central bank digital currency (CBDC). This note highlights the required security properties of a CBDC system and the challenges encountered with existing solutions, should the Bank of Canada choose to issue one.

This note analyzes different economic models of a central bank digital currency (CBDC) ecosystem where the central bank chooses different levels of market involvement and usage of policy levers. The analysis suggests that there are trade-offs between the costs to the central bank and its ability to achieve policy goals like universal access.

Cryptoassets that reference a national currency (commonly known as stablecoins) aim to peg their value to the reference currency and typically use a reserve of traditional financial assets to maintain the peg. The market value of these fiat-referenced cryptoassets has grown more than thirtyfold between early 2020 and mid-2022. We explore some of their potential benefits and key risks.

We examine how intermediary capitalization affects asset prices in a framework that allows for intermediary market power. We introduce a model in which capital-constrained intermediaries buy or trade an asset in an imperfectly competitive market, and we show that weaker capital constraints lead to both higher prices and intermediary markups.

Understanding the size of sectoral links is crucial to predicting the impact of a crisis on the whole economy. We show that statistical learning techniques substantially outperform traditional estimation techniques when measuring large networks of these links.

Building on our earlier quantum algorithm, this paper shows that reordering queued payments can significantly reduce liquidity needs. The algorithm performs well on smaller payment batches, while traditional algorithms can process larger batches and deliver greater savings. Machine learning also helps identify which payment patterns offer the greatest potential for improvement.

We investigate the risks and opportunities to the mandates of central banks arising from fintech developments.

We offer a theory of how inefficiently lax financial regulation could arise in a democratic society.

We assess how rising exports of US liquefied natural gas affect the convergence of natural gas prices worldwide. Our results may have implications for the development of future LNG export capacity in Canada.