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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.

I study the links between Canadian banks and non-bank financial intermediaries (NBFIs) by observing co-movements in stock prices. Perceived interconnections increased before the COVID-19 pandemic but have since stabilized, with the strongest ties seen between large banks and NBFIs. The secured credit line extended to Home Trust, a non-bank mortgage lender that experienced severe funding stress in 2017, significantly reduced banks' risk exposure to NBFIs during this episode.

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.

Trading flows affect Government of Canada bond prices. Our estimates suggest a sale of 1% of the available supply of bonds typically lowers bond prices by 0.2%. From 2000 to 2025, demand from institutional investors, such as Canadian pension funds and foreign investors, explains 69% of quarterly price variation, with the remainder explained by changes in the supply of bonds.

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.

During the COVID-19 financial market crisis, central banks introduced programs to support liquidity in important core funding markets. As well as acting as a backstop to market prices, these programs produce useful trading data on prevailing market conditions. When summary information from this data is shared publicly, it can help market participants understand current conditions and aid the recovery of market functioning.

Changes in the term premium can reflect uncertainty about inflation, growth and monetary policy. Understanding the key factors that influence the term premium is important when central banks make decisions about monetary policy. In this paper, we derive the real term premium from the nominal term premium in Canada.

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

Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.

This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.