Dependencies and volatility spillovers among carbon, energy, and stock markets in the EU

Projektinformationen

ECS

ID Finanzhilfevereinbarung: 101064650

DOI

10.3030/101064650

Projekt abgeschlossen

EK-Unterschriftsdatum 19 Juli 2022

Startdatum 15 Dezember 2022

Enddatum 14 März 2025

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 172 750,08

Koordiniert durch

FONDAZIONE ENI ENRICO MATTEI
Italy

Periodic Reporting for period 1 - ECS (Dependencies and volatility spillovers among carbon, energy, and stock markets in the EU)

Berichtszeitraum: 2022-12-15 bis 2025-03-14

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The research focuses on understanding the interconnections and spillover effects between carbon, energy, metal, and financial markets within the European Union Emissions Trading System (EU-ETS). Given the growing importance of carbon pricing in mitigating climate change, this study aims to provide a comprehensive analysis of market dynamics influencing the EU-ETS. The primary objectives are:

- To analyze the causality direction and dependency structure between carbon allowances (EUA) and key market assets such as oil, gas, coal, metals (gold, silver, copper), and financial indices (EuroStoxx600).
- To explore how these relationships evolve over different trading phases of the EU-ETS (from 2005 to 2022).
- To quantify time-varying spillovers and volatility transmission mechanisms affecting carbon markets.
- To offer policy-relevant insights that can help in designing more resilient carbon pricing strategies.

To achieve these goals, the study employs advanced econometric techniques, including Directed Acyclic Graph (DAG) analysis, Canonical Vine Copula (C-Vine) models, and Time-Varying Parameter Vector Auto Regressive models with Stochastic Volatility (TVP-VAR-SV). By integrating these methodologies, the research aims to provide a novel perspective on the interconnectedness of the EU carbon market with broader economic and financial variables​

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The study was conducted using a large dataset covering daily trading data from April 25, 2005, to December 30, 2022, encompassing all phases of the EU-ETS. The key methodological steps and findings include:

- Causality and Dependency Analysis: Using DAG models, the study identified that energy, metal, and financial markets exert a significant influence on the EU carbon market. Specifically, oil and copper prices show the highest dependency on EUA prices.
- Spillover Effects: TVP-VAR-SV models revealed that carbon markets are net receivers of shocks from energy, metal, and financial markets, with spillover effects lasting between 2 to 15 days.
- Time-Variation in Market Relationships: The strength of these relationships varies across different market phases, with higher volatility observed during economic crises (e.g. the Global Financial Crisis, COVID-19, and the Russia-Ukraine war).
- Robustness Checks: Alternative econometric techniques, including FCI and FGES algorithms, were used to validate the findings. The results consistently demonstrated that carbon price dynamics are heavily influenced by external market factors​.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

This research advances previous studies by adopting a multidimensional approach that integrates econometric models capable of capturing dynamic, non-linear, and high-frequency interactions among markets. The key contributions include:

- Multi-Market Perspective: Unlike traditional studies that focus on bilateral relationships (e.g. carbon-energy or carbon-financial linkages), this study provides a comprehensive, simultaneous analysis of all relevant markets.
- Time-Varying Dependency Analysis: By using DAG and C-Vine Copula models, the study moves beyond static correlation analyses, allowing for a deeper understanding of evolving market dependencies.
- Spillover Effect Characterization: The study identifies not only the magnitude but also the direction and duration of spillover effects, which is crucial for risk assessment and investment strategies.
- Policy-Relevant Insights: The findings suggest that policy interventions should consider the cross-market dynamics that impact carbon price volatility, particularly in times of economic uncertainty​