​We develop and estimate a multifactor affine model of commodity futures that allows for stochastic variations in seasonality. We show conditions under which the yield curve and the cost-of-carry curve adopt augmented Nelson and Siegel functional forms. This restricted version of the model is...

We study the state-dependent trading behavior of financial intermediaries in the oil futures market, using structural vector autoregressions with Markov switching in heteroskedasticity. We decompose changes in futures price volatility into changes in the slopes of traders' demand curves and in...

Prior research has failed to document a consistent association between oil prices and stock prices. We propose and examine whether that failure is due to the need to link oil price changes to firm-level changes in earnings and investments. We find that the impact of oil prices on a firm's...

We study the state-dependent trading behavior of financial intermediaries in the oil futures market, using structural vector autoregressions with Markov switching in heteroskedasticity. We decompose changes in futures price volatility into changes in the slopes of traders' demand curves and in...

Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market...

This study attempts to discover the nexus between crude oil price fluctuation after heavy oil upgrading and stock returns of petroleum companies in the U.S. Stock Exchange for the years 2008 to 2018. One of the methods of upgrading heavy crude oil is to extract asphaltene from crude oil....