Abstract:

The modern face of finance is closely linked to virtual currency. However, as geopolitical developments and environmental concerns grow, will cryptocurrency values remain stable in the future? This study empirically investigates the unknown green and dirty cryptocurrency space through a novel model, i.e., Rolling Local Nearest Neighbor Entropy Estimator (RLNNEE), by collecting and studying this data stream daily from April 28, 2019, to October 05, 2023. Interestingly, dirty cryptocurrencies dominate their pristine counterparts, which were favoured during the Russia-Ukraine war. In contrast, green cryptocurrencies have lower — but consistent – mutual information, indicating their constant market resistance. In addition, the approximate entropy provides statistical evidence that cryptocurrency change complexity is reduced during the war. This study contributes valuable information to considering how cryptocurrencies should be regulated and measures of potential risk. More importantly, they provide practical advice for entrepreneurs who launch or develop their cryptocurrency to attract investment and make way for a long-term business.

Keywords: green cryptocurrency, dirty cryptocurrency, RLNNEE, MI, Approximate entropy, sharing information, and the Russia-Ukraine conflict.