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This discussion paper represents the work of the inter-agency Canadian OTC Derivatives Working Group (OTCD WG), formed in December 2009, that is chaired by the Bank of Canada and composed of members from the Office of the Superintendent of Financial Institutions (OSFI), the federal Department of Finance, the Ontario Securities Commission, the Autorité des marchés financiers, the Alberta Securities Commission and the Bank of Canada.

The conduct of monetary policy within an inflation-targeting framework requires the establishment of an inflation-target horizon, which is the average time it takes inflation to return to the target. Policy-makers have an interest in communicating this horizon, since it is likely to help anchor inflation expectations. This article focuses on the determination of the appropriate policy horizon by reporting on two recent Bank of Canada studies. The evidence suggests that the current target horizon of six to eight quarters remains appropriate. It is important to note that the duration of the optimal inflation-target horizon varies widely, depending on the combination of shocks to the economy. In rare cases when the financial accelerator is triggered by a persistent shock, such as an asset-price bubble, it may be appropriate to take a longer view of the inflation-target horizon.

We create a theoretical model of central bank asset purchases. The model helps explain how, in a crisis, these purchases ease pressures on investment dealers.

BoC-GEM, an adaptation of the Global Economy Model, initially developed at the International Monetary Fund and the New York Federal Reserve, is a very useful tool to tackle a broad range of issues pertinent to the current economic context, such as the recent movements in commodity prices and the adjustment of global imbalances. This article describes the structure and functioning of BoC-GEM and details some examples of recent application in the areas of monetary policy and issues in the real economy and questions of financial stability and describes ongoing research into introducing a financial sector into the model.

To what extent does a central bank digital currency (CBDC) compete with bank deposits? To answer this question, we develop and estimate a structural model where each household chooses which financial institution to deposit their digital money with.

This article by the Bank's visiting economist examines the role of money in the transmission of monetary policy. Professor Laidler argues against the view of money as a passive variable that reacts to changes in prices, output, and interest rates but has no direct causative effect on them. He maintains that the empirical evidence supports the view of money playing an active role in the transmission mechanism. While he agrees that individual monetary aggregates can be difficult to read because of instabilities in the demand-for-money function, he argues that monetary aggregates, particularly those relating to transactions money, should have a more significant place in the hierarchy of policy variables that the Bank considers when formulating monetary policy.

The premise that exchange rate pass-through has declined is critically reassessed; intensity in the negative feedback process between financial sector developments and the real economy during the recent global crisis is examined; update on past decade’s changing trends in debt issuance in Canada relative to those in other capital markets.

Examination of progress in the Bank’s exploration of two alternatives to the current inflation-targeting framework: (i) lowering the inflation target and (ii) shifting to a price-level target; a review of arguments for and against price-level targeting, focusing on its costs and benefits compared with inflation targeting; assessing the merits of price-level targeting vis-à-vis inflation targeting from a debt-revaluation perspective; quantifying redistribution of wealth in Canada in the face of unexpected inflation.

Can artificial intelligence (AI) think and act like a cash manager? In this paper we explore how generative AI agents can help manage liquidity, prioritize payments and optimize efficiency in real-time gross settlement systems.