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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.

We present a technical description of the Top-Down Solvency Assessment (TDSA) tool. As a solvency stress-testing tool, TDSA is used to assess the banking sector’s capital resilience to hypothetical future risk scenarios.

Is there a risk of de-anchoring of inflation expectations in the near term? We estimate the inflation risk premium using traditional asset pricing models to answer this question. The risk of de-anchoring is elevated compared with the period before the COVID-19 pandemic and is higher in the United States than in Canada.

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.

This paper quantifies the impact of hurricanes on seaborne international trade to the United States. Matching the timing of hurricane–trade route intersections with monthly U.S. port-level trade data, we isolate the unanticipated effects of a hurricane hitting a trade route using two separate identification schemes: an event study and a local projection.

We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

Do technological improvements mitigate the potential damages from extreme weather events? We show that hurricanes lower offshore oil production in the Gulf of Mexico and that stronger storms have larger impacts. Regulations enacted in 1980 that required improved offshore construction standards only modestly mitigated the production losses.

We examine how consumers have adjusted their payment habits during the COVID-19 pandemic. They seem to perform fewer transactions, spend more in each transaction, use less cash at the point of sale and withdraw cash from ATMs linked to their financial institution more often than from other ATMs.

Understanding the size of sectoral links is crucial to predicting the impact of a crisis on the whole economy. We show that statistical learning techniques substantially outperform traditional estimation techniques when measuring large networks of these links.

Are summers getting hotter? Do daily temperatures change more than they used to? Using daily Canadian temperature data from 1960 to 2020 and modern econometric methods, we provide economists and policy-makers evidence on the important climate change issue of evolving temperatures.