Search

Across several dimensions of lender of last resort policy, I highlight broad changes that have occurred since the 2008–09 global financial crisis and discuss some of the key challenges, choices and considerations facing the designers of central bank liquidity tools today.

Given the Institute's membership and its focus on financial stability, I feel safe in saying that all of us here today watched this summer's turbulence in credit markets with interest, to put it mildly. What began in the spring as a repricing of credit risk turned into dislocations that have yet to fully run their course.

A model of over-the-counter markets is proposed. Some asset buyers are informed in that they can identify high quality assets. Heterogeneous sellers with private information choose what type of buyers they want to trade with.

The turbulence in financial markets did not come about against a backdrop of economic weakness. Indeed, over the past number of years, the global economy has shown remarkable strength. We were also seeing encouraging signs of growth being spread more evenly.

DeFi lending with proper governance is operationally viable, but it also faces constraints related to capital efficiency, liquidation risk, and systemic fragility within the crypto ecosystem.

In our latest Monetary Policy Report, which we released last Thursday, we noted that Canada's economic growth did indeed slow, but recently, inflation has been higher than expected. After considering the full range of indicators, the Bank now judges that the Canadian economy was operating just above its production capacity in the first quarter of this year.

In our latest Monetary Policy Report, which we released last Thursday, we noted that Canada's economic growth did indeed slow, but recently, inflation has been higher than expected. After considering the full range of indicators, the Bank now judges that the Canadian economy was operating just above its production capacity in the first quarter of this year.

Deputy Governor Toni Gravelle provides an assessment of risks to the stability of the Canadian financial system, including risks stemming from the COVID-19 pandemic.

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.