Fire Sales and Liquidity Requirements Staff Working Paper 2024-18 Yuteng Cheng, Roberto Robatto We study liquidity requirements in a framework with fire sales. The framework nests three common pricing mechanisms and produces the same observables. Absent risk-sharing considerations, the equilibrium is efficient with cash-in-the-market pricing; a liquidity requirement is optimal with second-best-use pricing; and a liquidity ceiling (i.e., a cap on liquid assets) is optimal with adverse selection. Content Type(s): Staff research, Staff working papers Research Topic(s): Asset pricing, Financial markets, Financial system regulation and policies JEL Code(s): G, G1, G12, G2, G23, G28
Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm Staff Working Paper 2024-17 Sofia Priazhkina, Samuel Palmer, Pablo Martín-Ramiro, Román Orús, Samuel Mugel, Vladimir Skavysh We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking. Content Type(s): Staff research, Staff working papers Research Topic(s): Central bank research, Digital currencies and fintech, Digitalization, Economic models, Financial institutions, Payment clearing and settlement systems, Sectoral balance sheet JEL Code(s): C, C6, C7, C71, D, D4, D8, D85, G, L, L2, L22
The Macroeconomic Implications of Coholding Staff Working Paper 2024-16 Michael Boutros, Andrej Mijakovic Coholder households simultaneously carry high-cost credit card debt and low-yield cash. We study the implications of this behavior for fiscal and monetary policy, finding that coholder households have smaller consumption responses in the short run but larger responses in the long run. Content Type(s): Staff research, Staff working papers Research Topic(s): Central bank research, Economic models, Fiscal policy, Monetary policy JEL Code(s): E, E2, E21, E4, E44, E6, E62, G, G5, G51
Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems Staff Working Paper 2024-15 Ajit Desai, Anneke Kosse, Jacob Sharples Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems. Content Type(s): Staff research, Staff working papers Research Topic(s): Digital currencies and fintech, Financial institutions, Financial services, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): C, C4, C45, C5, C55, D, D8, D83, E, E4, E42
Endogenous Credibility and Wage-Price Spirals Staff Working Paper 2024-14 Olena Kostyshyna, Tolga Özden, Yang Zhang We quantitively assess the risks of a wage-price spiral occurring in Canada over history. We find the risk of a wage-price spiral increases when the inflation expectations become unanchored and the credibility of central banks declines. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Credibility, Inflation and prices, Monetary policy JEL Code(s): C, C2, C22, E, E0, E00, E4, E47, E7
Parallel Tempering for DSGE Estimation Staff Working Paper 2024-13 Joshua Brault I develop a population-based Markov chain Monte Carlo algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors. Content Type(s): Staff research, Staff working papers Research Topic(s): Econometric and statistical methods, Economic models JEL Code(s): C, C1, C11, C15, E, E1, E10
U.S. Macroeconomic News and Low-Frequency Changes in Small Open Economies’ Bond Yields Staff Working Paper 2024-12 Bingxin Ann Xing, Bruno Feunou, Morvan Nongni-Donfack, Rodrigo Sekkel Using two complementary approaches, we investigate the importance of U.S. macroeconomic news in driving low-frequency fluctuations in the term structure of interest rates in Canada, Sweden and the United Kingdom. We find that U.S. macroeconomic news is particularly important to explain changes in the expectation components of the nominal, real and break-even inflation rates of small open economies. Content Type(s): Staff research, Staff working papers Research Topic(s): Central bank research, Econometric and statistical methods JEL Code(s): E, E4, E43, E44, E47, G, G1, G14
Unintended Consequences of the Home Affordable Refinance Program Staff Working Paper 2024-11 Phoebe Tian, Chen Zheng We investigate the unintended consequences of the Home Affordable Refinance Program (HARP). Originally designed to help borrowers refinance after the 2008–09 global financial crisis, HARP inadvertently strengthened the market power of incumbent lenders by creating a cost advantage for them. Despite a 2013 policy rectifying this cost advantage, we still find significant welfare losses for borrowers. Content Type(s): Staff research, Staff working papers Research Topic(s): Financial institutions JEL Code(s): G, G2, G21, G5, G51, L, L5, L51
Forecasting Recessions in Canada: An Autoregressive Probit Model Approach Staff Working Paper 2024-10 Antoine Poulin-Moore, Kerem Tuzcuoglu We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C5, C51, C53, E, E3, E32
CBDC: Banking and Anonymity Staff Working Paper 2024-9 Yuteng Cheng, Ryuichiro Izumi We examine the optimal amount of user anonymity in a central bank digital currency in the context of bank lending. Anonymity, defined as the lender’s inability to discern an entrepreneur’s actions that enable fund diversion, influences the choice of payment instrument due to its impact on a bank’s lending decisions. Content Type(s): Staff research, Staff working papers Research Topic(s): Digital currencies and fintech JEL Code(s): E, E4, E42, E5, E58, G, G2, G28