Bouncing Back: How Mothballing Curbs Prices Staff Working Paper 2024-51 Thibaut Duprey, Artur Kotlicki, Daniel E. Rigobon, Philip Schnattinger We investigate the macroeconomic impacts of mothballed businesses—those that closed temporarily—on sectoral equilibrium prices after a negative demand shock. Our results suggest that pandemic fiscal support for temporary closures may have eased inflationary pressures. Content Type(s): Staff research, Staff working papers Topic(s): Central bank research, Firm dynamics, Fiscal policy, Inflation and prices JEL Code(s): C, C5, C55, C8, C81, D, D2, D22, E, E3, E32
Familiarity with Crypto and Financial Concepts: Cryptoasset Owners, Non-Owners, and Gender Differences Staff Working Paper 2024-48 Daniela Balutel, Walter Engert, Christopher Henry, Kim Huynh, Doina Rusu, Marcel Voia Measuring cryptoasset knowledge alongside financial knowledge enhances our understanding of individuals' decisions to purchase cryptoassets. This paper uses microdata from the Bank of Canada’s Bitcoin Omnibus Survey to examine gender differences and the interrelationship between crypto and financial knowledge through an empirical joint analysis. Content Type(s): Staff research, Staff working papers Topic(s): Central bank research, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C8, C81, D, D1, D14, D9, D91, G, G5, G53, O, O5, O51
January 15, 2024 Flood risk and residential lending Craig Johnston, Geneviève Vallée, Hossein Hosseini Jebeli, Miguel Molico, Marie-Christine Tremblay, Aidan Witts We present key findings of a recent study that evaluates the credit risk that flooding poses to the residential lending activities of Canadian banks and credit unions. Results show that such risk currently appears modest but could become larger with climate change. Content Type(s): Publications, Financial System Hub articles Topic(s): Central bank research, Climate change, Credit risk management, Econometric and statistical methods, Financial institutions, Financial stability JEL Code(s): C, C8, C81, G, G2, G21, Q, Q5, Q54
Climate-Related Flood Risk to Residential Lending Portfolios in Canada Staff Discussion Paper 2023-33 Craig Johnston, Geneviève Vallée, Hossein Hosseini Jebeli, Brett Lindsay, Miguel Molico, Marie-Christine Tremblay, Aidan Witts We assess the potential financial risks of current and projected flooding caused by extreme weather events in Canada. We focus on the residential real estate secured lending (RESL) portfolios of Canadian financial institutions (FIs) because RESL portfolios are an important component of FIs’ balance sheets and because the assets used to secure such loans are immobile and susceptible to climate-related extreme weather events. Content Type(s): Staff research, Staff discussion papers Topic(s): Central bank research, Climate change, Credit risk management, Econometric and statistical methods, Financial institutions, Financial stability JEL Code(s): C, C8, C81, G, G2, G21, Q, Q5, Q54
Identifying Nascent High-Growth Firms Using Machine Learning Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Firm dynamics JEL Code(s): C, C5, C55, C8, C81, L, L2, L25
Cryptoasset Ownership and Use in Canada: An Update for 2022 Staff Discussion Paper 2023-14 Daniela Balutel, Christopher Henry, Doina Rusu We find that Bitcoin ownership declined from 13% in 2021 to 10% in 2022. This drop occurred against a background of steep price declines and an increasingly tight regulatory atmosphere. Content Type(s): Staff research, Staff discussion papers Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C8, C81, E, E4, O, O5, O51
Historical Data on Repurchase Agreements from the Canadian Depository for Securities Technical Report No. 121 Maxim Ralchenko, Adrian Walton We develop an algorithm that extracts information about sale and repurchase agreements (repos) from disaggregated settlement data in order to generate a new historical dataset for research. Content Type(s): Staff research, Technical reports Topic(s): Econometric and statistical methods, Financial markets JEL Code(s): C, C5, C55, C8, C81, G, G1, G10
Business Closures and (Re)Openings in Real Time Using Google Places Staff Working Paper 2022-1 Thibaut Duprey, Daniel E. Rigobon, Philip Schnattinger, Artur Kotlicki, Soheil Baharian, T. R. Hurd The COVID-19 pandemic highlighted the need for policy-makers to closely monitor disruptions to the retail and food business sectors. We present a new method to measure business opening and closing rates using real-time data from Google Places, the dataset behind the Google Maps service. Content Type(s): Staff research, Staff working papers Topic(s): Firm dynamics, Recent economic and financial developments JEL Code(s): C, C5, C55, C8, C81, D, D2, D22, E, E3, E32
Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning Staff Working Paper 2020-33 Diego Rojas, Juan Estrada, Kim Huynh, David T. Jacho-Chávez Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash. Content Type(s): Staff research, Staff working papers Topic(s): Bank notes, Econometric and statistical methods, Payment clearing and settlement systems JEL Code(s): C, C5, C52, C6, C65, C8, C81, E, E4, E42, E5, E51
Sample Calibration of the Online CFM Survey Technical Report No. 118 Marie-Hélène Felt, David Laferrière The Canadian Financial Monitor (CFM) survey uses non-probability sampling for data collection, so selection bias is likely. We outline methods for obtaining survey weights and discuss the conditions necessary for these weights to eliminate selection bias. We obtain calibration weights for the 2018 and 2019 online CFM samples. Content Type(s): Staff research, Technical reports Topic(s): Econometric and statistical methods JEL Code(s): C, C8, C81, C83