Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches Staff analytical note 2018-34 Andrew Lee-Poy In this note, I use two multivariate frequency filtering approaches to characterize the Canadian financial cycle by capturing fluctuations in the underlying variables with respect to a long-term trend. The first approach is a dynamically weighted composite, and the second is a stochastic cycle model. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C0, C01, C1, C13, C14, C18, C3, C32, C5, C51, C52, E, E3, E32, E6, E66, G, G0, G01, G1, G18 Research Theme(s): Financial system, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods, Economic models
Asymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities Staff analytical note 2018-6 Thibaut Duprey When financial system vulnerabilities are elevated, they can give rise to asymmetric risks to the economic outlook. To illustrate this, I consider the economic outlook presented in the Bank of Canada’s October 2017 Monetary Policy Report in the context of two key financial system vulnerabilities: high levels of household indebtedness and housing market imbalances. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C0, C01, C1, C11, C15, E, E1, E17, E3, E32, E37, E4, E44, E47, E5, E58, E6, E66, G, G0, G01, G1, G18 Research Theme(s): Financial system, Financial stability and systemic risk, Household and business credit, Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
A Barometer of Canadian Financial System Vulnerabilities Staff analytical note 2017-24 Thibaut Duprey, Tom Roberts This note presents a composite indicator of Canadian financial system vulnerabilities—the Vulnerabilities Barometer. It aims to complement the Bank of Canada’s vulnerabilities assessment by adding a quantitative and synthesized perspective to the more granular (distributional) analysis presented in the Financial System Review. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C1, C14, C4, C40, D, D1, D14, E, E3, E32, E6, E66, F, F0, F01, G, G0, G01, G1, G15, G2, G21, H, H6, H63 Research Theme(s): Financial markets and funds management, Market functioning, Financial system, Financial stability and systemic risk, Household and business credit, Models and tools, Econometric, statistical and computational methods
The Case of Serial Disappointment Staff analytical note 2016-10 Justin-Damien Guénette, Nicholas Labelle, Martin Leduc, Lori Rennison Similar to those of other forecasters, the Bank of Canada’s forecasts of global GDP growth have shown persistent negative errors over the past five years. This is in contrast to the pre-crisis period, when errors were consistently positive as global GDP surprised to the upside. All major regions have contributed to the forecast errors observed since 2011, although the United States has been the most persistent source of notable errors. Content Type(s): Staff research, Staff analytical notes JEL Code(s): E, E2, E27, E6, E66, F, F0, F01 Research Theme(s): Models and tools, Economic models, Monetary policy, Real economy and forecasting
November 14, 2013 Assessing Financial System Vulnerabilities: An Early Warning Approach Bank of Canada Review - Autumn 2013 Gurnain Pasricha, Tom Roberts, Ian Christensen, Brad Howell This article focuses on a quantitative method to identify financial system vulnerabilities, specifically, an imbalance indicator model (IIM) and its application to Canada. An IIM identifies potential vulnerabilities in a financial system by comparing current economic and financial data with data from periods leading up to past episodes of financial stress. It complements other sources of information - including market intelligence and regular monitoring of the economy - that policy-makers use to assess vulnerabilities. Content Type(s): Publications, Bank of Canada Review articles JEL Code(s): E, E6, E66, G, G0, G01