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2191 Results

Financial Intermediaries and the Macroeconomy: Evidence from a High-Frequency Identification

Staff working paper 2022-24 Pablo Ottonello, Wenting Song
We provide empirical evidence of effects to the aggregate economy from surprises about financial intermediaries’ net worth based on a high-frequency identification strategy. We estimate that news of a 1% decline in intermediaries’ net worth leads to a 0.2%–0.4% decrease in the market value of nonfinancial firms.

What COVID-19 May Leave Behind: Technology-Related Job Postings in Canada

Staff working paper 2022-17 Alejandra Bellatin, Gabriela Galassi
COVID-19 affects technology adoption: online job postings for technology-related occupations fall less during pandemic lockdowns and pick up faster during reopenings than postings for more traditional occupations.

DeFi Lending: Returns, Leverage, and Liquidation Risk

Staff analytical paper 2026-13 Jonathan Chiu, Furkan Danisman
DeFi lending with proper governance is operationally viable, but it also faces constraints related to capital efficiency, liquidation risk, and systemic fragility within the crypto ecosystem.

Does US or Canadian Macro News Drive Canadian Bond Yields?

Staff analytical note 2018-38 Bruno Feunou, Rodrigo Sekkel, Morvan Nongni-Donfack
We show that a large share of low-frequency (quarterly) movements in Canadian government bond yields can be explained by macroeconomic news, even though high-frequency (daily) changes are driven by other shocks. Furthermore, we show that US macro news—not domestic news— explains most of the quarterly variation in Canadian bond yields.

Macroeconomic Predictions Using Payments Data and Machine Learning

Staff working paper 2022-10 James Chapman, Ajit Desai
We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.

The Dynamics of Capital Flow Episodes

Staff working paper 2016-9 Christian Friedrich, Pierre Guérin
This paper proposes a novel methodology for identifying episodes of strong capital flows based on a regime-switching model. In comparison with the existing literature, a key advantage of our methodology is to estimate capital flow regimes without the need for context- and sample-specific assumptions.
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