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Non-parametric Causal Discovery for EU Allowances Returns Through the Information Imbalance

Cristiano Salvagnin,Vittorio del Tatto,M. E. Giuli,Antonietta Mira,Aldo Glielmo

Published 2025 in Unknown venue

ABSTRACT

We propose to use a recently introduced non-parametric tool named Differentiable Information Imbalance (DII) to identify variables that are causally related -- potentially through non-linear relationships -- to the financial returns of the European Union Allowances (EUAs) within the EU Emissions Trading System (EU ETS). We examine data from January 2013 to April 2024 and compare the DII approach with multivariate Granger causality, a well-known linear approach based on VAR models. We find significant overlap among the causal variables identified by linear and non-linear methods, such as the coal futures prices and the IBEX35 index. We also find important differences between the two causal sets identified. On two synthetic datasets, we show how these differences could originate from limitations of the linear methodology.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-08-21

  • Fields of study

    Economics, Environmental Science

  • Identifiers
    arXiv 2508.15667
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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