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...

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...

This paper documents the existence of a slowly evolving trend in the dividend-price ratio, dp, determined by a demographic variable, MY: the middle-aged to young ratio. Deviations of the dividend-price ratio from this slowly evolving long-run component explain transitory but persistent...

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,...

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 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...

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 study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as...