Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models. The results show that the ML and regularization methods are efficient, even when there are only three predictors: daily, weekly, and monthly realized volatility (RV) lags. In addition, when the ML and regularization methods are applied, the results become more pronounced over longer forecasting horizons, as well as for weekly and monthly horizons. These ML methods are effective in approximating long‐term realized volatility. Furthermore, we found that additional explanatory variables for real‐time currency exchange contain valuable information to forecast the RV of crude oil prices.