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.本数据集为论文《地缘政治风险下的绿色与肮脏加密货币：基于互信息（Mutual Information, MI）、近似熵（Approximate Entropy, ApEn）与滚动局部最近邻熵估计器（Rolling Local Nearest Neighbour Entropy Estimator, RLNNEE）的熵分析》提供数据支持。数据集包含8种主流加密货币的每日价格与收益率序列，依据其共识机制与环境足迹被划分为绿色加密货币（XRP、MATIC、XLM、ADA）与肮脏加密货币（BTC、ETH、BCH、ETC）两类。 数据集覆盖2019年4月28日至2023年10月5日的时段，涵盖两大危机阶段：新冠疫情（COVID-19 pandemic），以及2022年2月24日爆发的俄乌冲突。上述事件可支持对压力环境下的市场行为开展精细化考察。 数据已完成清洗、对齐，并在1%与99%分位进行缩尾处理，以降低极端异常值的影响。本数据集适用于市场有效性、信息论、可持续金融与系统性风险领域的高级实证分析。 该研究采用了三种基于熵的分析方法：互信息（Mutual Information, MI）、近似熵（Approximate Entropy, ApEn），以及滚动局部最近邻熵估计器（Rolling Local Nearest Neighbour Entropy Estimator, RLNNEE）——这是一种新近提出的非参数估计器，可捕捉局部复杂性与信息共享特征。 本数据集可复现论文中的全部实证结果，同时也可用于自适应效率、绿色金融与加密货币市场动态等方向的后续研究。