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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.

Extreme weather events contribute to increased volatility in both economic activity and prices, interfering with the assessment of the true underlying trends of the economy. With this in mind, we conduct a timely assessment of the impact of natural disasters on Canadian gross domestic product (GDP).

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.

We study the degree to which China’s 11th Five-Year Plan softens trade-offs between emissions and output. Our model suggests efficient regulation could have further increased aggregate productivity by 3.5% and output by 4.7% without any increase in aggregate emissions.

We study whether the credit derivatives of firms reflect the risk from climate transition. We find that climate transition risk has asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on other firms.

We use detailed consumer credit data to investigate the impact of the 2016 Fort McMurray wildfire, the costliest wildfire disaster in Canadian history, on consumers’ financial stress. We focus on the arrears of insured mortgages because of their important implications for financial institutions and insurers’ business risk and relevant management practices.

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.

Does consumption smoothing fundamentally decrease during macroeconomic disasters? This paper uses a large historical dataset (1870–2016) for 16 industrial economies to show that during macroeconomic disasters (e.g., wars, pandemics, depressions) aggregate consumption and income are significantly less decoupled than during normal times.