Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting

Abstract

This study aims to forecast crude oil and natural gas volatility across various forecasting horizons, from daily to quarterly, using diverse machine learning models. It critically analyzes eleven models, including Linear Regression, Elastic Regression, Ridge Regression, Lasso Regression, Huber Regression, Random Forest Regression, SVM, LSTM, GRU, ANN, and XGBoost, using RMSE for accuracy. The study reveals that model performance significantly varies with forecasting horizons; a phenomenon attributed to each model's inherent capabilities in processing short-term versus long-term market trends. This detailed understanding aids in selecting the most appropriate models for specific forecasting needs, essential for policymakers and practitioners in managing volatility effectively. The study concludes with a recommendation for using Random Forest Regression and XGBoost for natural gas volatility forecasting, providing key insights for enhancing economic resilience and stability.