Document Type : Research Paper

Authors

• Mandana Shiravand ¹
• mahdi zolfaghari ²
• samaneh abedi ³
• narges khosravinejad ⁴

¹ tarbiat modares university

² Assistant Professor of Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

³ Associate Professor, Faculty of Economics, Allameh Tabataba’i University

⁴ Graduated Master's degree in Economics, Faculty of Economics, Alzahra University, Tehran, Iran.

Abstract

This study investigates the relationship between the Global Economic Policy Uncertainty (GEPU) index and global crude oil prices, employing a combination of classical econometric models—such as VAR, BEKK, and DCC—and advanced machine learning techniques including LSTM, GRU, and XGBoost. The results indicate a bidirectional and predictable relationship between GEPU and oil prices, along with volatility spillovers. However, the immediate and short-term effects of shocks between the two variables are generally weak and often statistically insignificant. The relationship is more evident at the level of volatility correlation, and in the long run, rising oil prices can contribute to increased global economic instability. Bidirectional Granger causality between the two variables is confirmed, yet impulse responses within the VAR framework are largely insignificant. Volatility-based models and wavelet analysis reveal that co-movements in volatility intensify during crisis periods, and volatility spillovers between GEPU and oil prices are substantial. The application of advanced machine learning methods enhances the accuracy of forecasting the long-term and noisy relationships between the variables. The findings underscore the complexity and multidimensional nature of the interaction between global economic policy uncertainty and oil prices.

Keywords

• Oil Price Volatility
• Wavelet Analysis
• Deep Neural Networks
• Machine Learning
• Economic Uncertainty

Main Subjects

• مطالعات اقتصادی مرتبط با حامل‌های انرژی (فسیلی، تجدیدپذیر و برق)