Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach

Abstract

Measuring the strength and direction of information flow between markets plays a vital role for investors and policymakers. In this study, we propose a novel approach: the empirical mode decomposition variable lag relative transfer entropy (EMD-VL relative transfer entropy) to assess the pairwise relative causal information transfer over time and scale. Using clean and dirty energy, carbon, and precious metals, we show strong evidence of bidirectional causal information transmission between markets over time-frequency domain.