We adopt the semicovariance decomposition method and machine-learning models to forecast the realized correlation and realized volatility of oil and gold futures markets. The general framework consists of three steps: data preprocessing, accumulating window cross-validation, and performance...

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...

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...