Non-parametric Causal Discovery for EU Allowances Returns Through the Information Imbalance
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908497363337216 |
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| author | Salvagnin, Cristiano del Tatto, Vittorio De Giuli, Maria Elena Mira, Antonietta Glielmo, Aldo |
| author_facet | Salvagnin, Cristiano del Tatto, Vittorio De Giuli, Maria Elena Mira, Antonietta Glielmo, Aldo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-parametric Causal Discovery for EU Allowances Returns Through the Information Imbalance Salvagnin, Cristiano del Tatto, Vittorio De Giuli, Maria Elena Mira, Antonietta Glielmo, Aldo Computational Finance 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. |
| title | Non-parametric Causal Discovery for EU Allowances Returns Through the Information Imbalance |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2508.15667 |