Cash and COVID-19: The impact of the pandemic on demand for and use of cash Staff discussion paper 2020-6 Heng Chen, Walter Engert, Kim Huynh, Gradon Nicholls, Mitchell Nicholson, Julia Zhu Consumer spending declined significantly during the recent COVID-19 pandemic. This negative shock likely reduced spending across all methods of payment (cash, debit, credit, etc.). The mix of payment methods consumers use could also be affected. We study how the pandemic has influenced the demand for and use of cash. We also offer insights into the use of other payment methods, such as debit and credit cards. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C12, C9, E, E4, O, O5, O54 Research Theme(s): Money and payments, Cash and bank notes, Retail payments
Endogenous Time Variation in Vector Autoregressions Staff working paper 2020-16 Danilo Leiva-Leon, Luis Uzeda We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C3, C32, E, E3, E31, E5, E52 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission
Identifying Consumer-Welfare Changes when Online Search Platforms Change Their List of Search Results Staff working paper 2020-5 Ryan Martin Online shopping is often guided by search platforms. Consumers type keywords into query boxes, and search platforms deliver a list of products. Consumers' attention is limited, and exhaustive searches are often impractical. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, D, D1, D11, D12, D6, D8, D83, L, L4, L40 Research Theme(s): Financial markets and funds management, Market structure, Models and tools, Econometric, statistical and computational methods
Extreme Downside Risk in Asset Returns Staff working paper 2019-46 Lerby Ergun Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, G, G1, G11, G12 Research Theme(s): Financial markets and funds management, Market functioning, Financial system, Financial stability and systemic risk
2018 Bitcoin Omnibus Survey: Awareness and Usage Staff discussion paper 2019-10 Christopher Henry, Kim Huynh, Gradon Nicholls, Mitchell Nicholson The Bank of Canada continues to use the Bitcoin Omnibus Survey (BTCOS) to monitor trends in Canadians’ awareness, ownership and use of Bitcoin. The most recent iteration was conducted in late 2018, following an 85 percent decline in the price of Bitcoin throughout the year. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C12, E, E4, O, O5, O51 Research Theme(s): Money and payments, Cash and bank notes, Digital assets and fintech
Tail Index Estimation: Quantile-Driven Threshold Selection Staff working paper 2019-28 Jon Danielsson, Lerby Ergun, Casper G. de Vries, Laurens de Haan The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C0, C01, C1, C14, C5, C58 Research Theme(s): Financial markets and funds management, Market functioning, Financial system, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods
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
Monetary Policy Uncertainty: A Tale of Two Tails Staff working paper 2018-50 Tatjana Dahlhaus, Tatevik Sekhposyan We document a strong asymmetry in the evolution of federal funds rate expectations and map this observed asymmetry into measures of monetary policy uncertainty. We show that periods of monetary policy tightening and easing are distinctly related to downside (policy rate is higher than expected) and upside (policy rate is lower than expected) uncertainty. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C18, C3, C32, E, E0, E02, E4, E43, E5, E52 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Monetary policy framework and transmission, Monetary policy tools and implementation, Real economy and forecasting
Challenges in Implementing Worst-Case Analysis Staff working paper 2018-47 Jon Danielsson, Lerby Ergun, Casper G. de Vries Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C0, C01, C1, C14, C5, C58 Research Theme(s): Financial system, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods
A Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions Staff working paper 2018-29 Marie-Hélène Felt This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, D, D1, D14, E, E4, E41 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Cash and bank notes