The Internet Appendix consists of three sections. Section A shows data sources and detailed data processing procedures. In Section B, we outline seven forecasting models. Last, Section C represents the empirical results

This paper explores the possibility of the potential usage of machine learning models in the field of realized volatility forecasting of crude oil with a vast variety of empirical analyses and robustness checks. Although the conventional heterogeneous autoregressive (HAR) model is widely...

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...

Futures contracts on the New York Mercantile Exchange are the most liquid instruments for trading crude oil, which is the world’s most actively traded physical commodity. Under normal market conditions, traders can easily find counterparties for their trades, resulting in an efficient market...

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only...