Dataset for "Entropy-Based Analysis of Green and Dirty Cryptocurrencies During Geopolitical Crises"

This dataset supports the article “Green vs Dirty Cryptocurrencies Under Geopolitical Risk: An Entropy-Based Analysis Using MI, ApEn, and RLNNEE.”It contains daily price and return series for eight major cryptocurrencies classified into green (XRP, MATIC, XLM, ADA) and dirty (BTC, ETH, BCH, ETC) categories based on their consensus mechanisms and environmental footprint. The dataset covers the period from April 28, 2019, to October 5, 2023, which includes two major crisis phases: the COVID-19 pandemic, the Russia–Ukraine war, beginning on 24 February 2022. These events allow a detailed examination of market behavior under stress conditions. The data have been cleaned, aligned, and winsorised at the 1% and 99% levels to reduce the impact of extreme outliers. The dataset is suitable for advanced empirical analyses in market efficiency, information theory, sustainable finance, and systemic risk. The study applies three entropy-based methodologies: Mutual Information (MI), Approximate Entropy (ApEn), RLNNEE – Rolling Local Nearest Neighbour Entropy Estimator, a recently developed non-parametric estimator capturing local complexity and information sharing. This dataset enables replication of all empirical results in the article and can also be used for further research on adaptive efficiency, green finance, and cryptocurrency market dynamics.