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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.

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.

Under the consumer-merchant bipartite network, we apply the indirect sampling approach to estimate merchant payment acceptance through a consumer payment diary.

We examine the macro implications of commodity price shocks in a model with multiple production sectors that are interconnected within a commodity-exporting small open economy.

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of different economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time subjective measure of uncertainty in a simple framework.

Can monetary policy influence the neutral real interest rate (r-star)? Using a new statistical model, we show that interest rate hikes tend to lower r-star and long-run growth, but that monetary policy explains only a small share of the long-run decline in r-star.

Non-default losses of financial market infrastructures (FMIs) have gained attention due to their potential impacts on FMIs and FMI participants, and the lack of a common approach to address them. A key question is, who should absorb these losses?

We develop and estimate a search model in which identical consumers trade with price-setting firms that differ in productivity. We use the estimated model to characterize the qualitative and quantitative differences in prices and markups across firms. We explore how individual firms respond to changes in cost and demand and how they pass these through to their prices and markup.

We construct a dataset on Federal Reserve and Bank of Canada non-rate announcement events to provide novel insights into how foreign and domestic monetary policy communications affect the financial markets of open economies. We find that Fed non-rate communications have a stronger impact on long-term interest rates and stock futures, while Bank of Canada communications are relatively more important for short-term interest rates and the exchange rate.

We show how Treasury demand shocks transmit to foreign exchange and bond markets globally. Higher Treasury demand weakens the U.S. dollar and raises foreign bond prices, with effects persisting for two weeks. The transmission varies predictably across countries based on their monetary policy alignment with the United States.