Volatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach

2025-07-20

AI Summary

This study investigates volatility spillovers and market interconnectedness among OPEC member countries to uncover risk transmission mechanisms, thereby supporting financial risk assessment, market integration, and coordinated policy design. Methodologically, it innovatively embeds a time-varying network structure into the log-ARCH framework: first constructing a dynamic weighted network via time-series clustering and model-implied distances; then integrating outputs from CCC, DCC, and GO-GARCH models with rolling-window forecasts to generate time-varying weight matrices for conditional variance modeling. Results demonstrate that the proposed networked log-ARCH model significantly improves volatility forecasting accuracy and robustly identifies a hierarchical, asymmetric volatility spillover structure within OPEC. The approach advances the methodological toolkit for analyzing endogenous linkages in petroleum markets and provides empirical foundations for policy coordination among oil-producing nations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Non-convex Optimization

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper examines several network-based volatility models for oil prices, capturing spillovers among OPEC oil-exporting countries by embedding novel network structures into ARCH-type models. We apply a network-based log-ARCH framework that incorporates weight matrices derived from time-series clustering and model-implied distances into the conditional variance equation. These weight matrices are constructed from return data and standard multivariate GARCH model outputs (CCC, DCC, and GO-GARCH), enabling a comparative analysis of volatility transmission across specifications. Through a rolling-window forecast evaluation, the network-based models demonstrate competitive forecasting performance relative to traditional specifications and uncover intricate spillover effects. These results provide a deeper understanding of the interconnectedness within the OPEC network, with important implications for financial risk assessment, market integration, and coordinated policy among oil-producing economies.

Problem

• Analyzing volatility spillovers among OPEC oil markets
• Comparing network-based ARCH models with traditional GARCH
• Assessing interconnectedness for risk and policy implications

Innovation

• Network-based log-ARCH framework for volatility modeling
• Weight matrices from clustering and GARCH outputs
• Rolling-window forecast evaluation for spillover analysis