Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data-Reference-Cited by-同舟云学术

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Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data

Published:2023-11 Issue: Volume:90 Page:102938
ISSN:1057-5219
Container-title:International Review of Financial Analysis
language:en
Short-container-title:International Review of Financial Analysis

Author:

Huang Wenyang,Wang Huiwen,Wei Yigang

Funder

Humanities and Social Science Fund of Ministry of Education of the People's Republic of China

Beijing Municipal Office of Philosophy and Social Science Planning

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

Elsevier BV

Subject

Economics and Econometrics,Finance

Reference76 articles.

1. Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals;Aatola;Energy Economics,2013

2. Partial least squares methods: Partial least squares correlation and partial least square regression;Abdi;Computational Toxicology,2013

3. Price drivers and structural breaks in European carbon prices 2005-07;Alberola;Energy Policy,2008

4. Does weather, or energy prices, affect carbon prices?;Batten;Energy Economics,2021

5. Modeling the price dynamics of CO2 emission allowances;Benz;Energy Economics,2009

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1. The Efficiency of China’s Carbon Trading Schemes: A Tale of Seven Pilot Markets;Commodities;2024-08-29

2. Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model;Applied Soft Computing;2024-05

3. Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices;Energy Economics;2024-05

4. Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price;Energy Economics;2024-04

5. Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach;Finance Research Letters;2024-03

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