A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models

Abstract

This study proposes a novel deep-learning convolution neural network (CNN) to forecast crude oil prices based on historical prices of five precious metals (Gold, Silver, Platinum, Palladium, and Rhodium) in context of green financing. The proposed deep learning CNN has three components: a convolution block called a group block, a novel convolutional neural network architecture called GroupNet, and a regression layer. The proposed model is tested against seven machine learning models and three traditional statistical models for predicting oil price volatility using the same independent variables (5 precious metals). A comparison of the deep learning model (our proposed model) with machine learning/deep learning models and statistical methods indicates that the proposed deep learning model has the highest prediction accuracy. A feature selection technique is also applied using the WEKA ML tool to improve the accuracy of the proposed model and existing machine learning and traditional statistical models. The findings indicate a non-linear correlation between oil price volatility and prices of precious metals. Moreover, statistical analysis indicates that deep learning can be used to predict oil price volatility with greater accuracy than machine learning and statistical methods while using precious metals as predictors. The results also indicate that machine learning models (Decision Tables and M5rules) can be used to predict oil price volatility with considerable accuracy. Moreover, the study proves that traditional statistical models can perform better than a few machine learning models (Lazy LWL and GPR).