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What sources of financing do Indigenous-owned businesses in Canada use, and what are their expectations about prices, wages and inflation? We find Indigenous-owned firms are significantly less reliant on financial institutions as sources of financing compared with non-Indigenous firms. We also find Indigenous-owned firms have higher inflation expectations and weaker wage-growth expectations.

We present two models for long-term inflation expectations and inflation risk premiums for Canada.

In this study, we enhance Markowitz portfolio selection with graph theory for the analysis of two portfolios composed of either EU or US assets. Using a threshold-based decomposition of their respective covariance matrices, we perturb the level of risk in each portfolio and build the corresponding sets of graphs.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

To better understand price-setting behaviour in the Canadian economy, the Bank of Canada's regional offices surveyed a representative sample of 170 firms between July 2002 and March 2003. The authors discuss the reasons behind the survey, the methodology used to develop the questionnaire and conduct the interviews, and summarize the results. The study also assessed several explanations for holding prices steady despite market pressures for a change. The survey findings indicate that prices in Canada are relatively flexible and have become more flexible over the past decade. Price stickiness was generally found to originate in firms' fears of antagonizing customers or disturbing the goodwill or reputation developed with them. A detailed discussion of the results includes a consideration of their implications for monetary policy.

This paper theoretically and empirically examines the price impacts of liquidations in DeFi and how different liquidation mechanisms affect the price impacts.

Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models.

This article examines what market conditions lead to extreme losses in global financial systems. Using a reverse stress testing approach, it introduces two measures of systemic risk by starting from the tail losses and working backward to identify the events most closely associated with them.

How much capital do banks need as a buffer to absorb severe shocks? By using historical stock market data, market-based stress tests help estimate the magnitude of capital buffers necessary to absorb severe but plausible shocks.

We conduct surveys to study how consumer spending responds to higher inflation expectations. Most respondents spend the same, sticking to fixed budget plans or not considering inflation for spending decisions. About 20% decrease spending because they feel poorer and cut spending to invest in inflation-proof assets. Very few increase spending.