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

TL;DR

This paper introduces a non-parametric causal discovery method using Differentiable Information Imbalance to identify variables causally related to EU Allowances returns, capturing non-linear relationships often missed by traditional linear methods.

Contribution

The paper applies the DII method to financial data, demonstrating its ability to detect causal variables and differences from linear approaches like Granger causality in EU ETS returns.

Findings

DII identifies causal variables including coal futures and stock indices.

Significant overlap and differences found between non-linear and linear causality methods.

Differences highlight limitations of linear models in capturing complex causal relationships.

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.