Modeling futures market risk simultaneously influenced by macro low-frequency information and daily risk factors is a valuable challenge. We propose a new general framework for it based on the flexible GARCH-MIDAS model. It uses a skewed t distribution to describe the asymmetry of long and short...

I exploit deep learning techniques trained on a set of common accounting items and constructed to mimic features of the human brain to predict future earnings. I show that this model offers incremental explanatory power in predicting future earnings and in estimating the associated implied cost...

We study a two-period supply chain consisting of a manufacturer, who participates in a cap-and-trade scheme and faces an uncertain emission permit price, and a retailer, who sells the product from the manufacturer to consumer and faces a price-sensitive demand. In the face of uncertain future...