Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network

Foued Saâdaoui and Sami Ben Jabeur

Energy Economics, 2023, vol. 124, issue C

Abstract: This paper presents a rigorous investigation into the multiresolution cross-correlation and causality between European energy markets and geopolitical risk (GPR). Using daily electricity spot prices from January 2015 to January 2023, the study employs the variational mode decomposition (VMD) method to uncover the underlying patterns and reveal the interaction between European power prices and GPR. The paper further develops a VMD-assisted multi-scaled causal neural network (VMD-M-CNN) as a multivariable forecasting approach, benchmarking it against other competing models. The results demonstrate a strong dependence between electricity markets and GPR across different investment time-scale horizons. Additionally, the relationship between the two has taken different forms over the crisis period in contrast with calm periods. The proposed VMD-M-CNN approach proves to be more effective than other benchmark models, providing valuable insights for market participants and policymakers to make informed decisions. The rigorous methodology employed and the novel approach developed enhance the credibility and robustness of the study, contributing to the growing body of literature on energy markets and GPR.

Keywords: Multiresolution machine learning; Causal neural network; Variational mode decomposition; Forecasting; Electricity prices; Geopolitical risks (search for similar items in EconPapers)
JEL-codes: C45 C55 C63 C87 D72 Q47 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:124:y:2023:i:c:s0140988323002918

DOI: 10.1016/j.eneco.2023.106793

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