Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

This paper proposes a new approach to measure premiums for volatility and jump risks in option markets. These risks are captured by a multi-factor jump-diffusion model for the joint evolution of the underlying and the implied volatility surface. This market-based approach enables us to carefully...

This paper builds and implements a multifactor stochastic volatility model for the latent (and observable) volatility from the quarter and year forward contracts at the NASDAQ OMX Commodity Exchanges, applying Bayesian Markov chain Monte Carlo simulation methodologies for estimation, inference,...

Volatility is usually considered as a synonym for risk. Mainstream financial theory states that higher portfolio volatility is translated into higher expected returns while diversification helps eliminate idiosyncratic risks. This leaves us with an apparent anomaly as low-risk (low-beta) stocks...

We apply a new methodology for identifying pervasive and discrete changes (``breaks'') in cross-sectional risk premia and find empirical evidence that these are economically important for understanding returns on US stocks. Size and value risk premia have fallen off to the point where they are...

We introduce a novel dynamic Bayesian model combination approach for predicting aggregate stock returns. Our method involves combining predictive densities in a data-adaptive fashion and simultaneously features (i) uncertainty about relevant predictor variables, (ii) parameter instability, (iii)...

We quantify crash risk in currency returns. To accomplish this task, we develop and estimate an empirical model of exchange rate dynamics using daily data for four currencies relative to the US dollar: the Australian dollar, the British pound, the Swiss franc, and the Japanese yen. The model...

This paper investigates model dynamics and risk premia in the short term market for crude oil futures. Stochastic volatility models, with and without jumps, are estimated using data on both futures and option prices. As an economic application we apply the estimated models to the pricing of...

This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors....