Authors: Dr. RAJIB BHATTACHARYA¹, Dr. SHUVASHISH ROY², ROKHSHANA PARVEEN³, Dr. JEET MUKHERJEE⁴ and MD TUHIN RANA⁵

Abstract

Purpose: The increasing environmental concerns associated with traditional cryptocurrencies led to a growing interest in eco-friendly alternatives and green cryptocurrencies, which utilize energy-efficient consensus mechanisms, offer a sustainable solution to this problem. This research focuses on interconnectedness of price movements of the selected six popular green cryptocurrencies i.e. Cardano (ADA), Algorand (ALG), Solana (SOL), Chia (XCH), Stellar Lumens (XLM), and Nano (XNO). The primary objective is to investigate the non linear relationships and patterns within the price time series of green cryptocurrencies in order to study which green cryptocurrency performs best under 2 cluster and 3 cluster models by following the time series clustering techniques under Dynamic Time Warping (DTW). Design/methodology/approach: Dynamic Time Warping (DTW) is the methodology usedfor flexible alignment of time series. By applying DTW to the price data of the six green cryptocurrencies, we aim to uncover hidden patterns and similarities that may not be apparent through traditional linear analysis. The research methodology involves several steps. Firstly, the historical price data for each cryptocurrency is collected and pre-processed. Secondly, DTW is employed to calculate pairwise distances between the time series, revealing the degree of similarity and dissimilarity among them. Thirdly, clustering of price series of selected cryptocurrencies is done for efficient creation of portfolios with green cryptocurrencies.This study has been carried out on the basis of secondary data. Day-end closing prices of the selected six green cryptocurrencies have been collected from 03rd May 2021 to 10th August 2024 i.e. that longest time window during which prices for all the selected green cryptocurrencies were available at finance.yahoo.com up to 10th August 2024. Findings: This study has explored the potential of time series clustering techniques to analyse and categorize green cryptocurrencies based on their historical price and volume data. Out of 2-Clusters and 3-Clusters methodology,3- Clusters Hierarchical methodology is giving the better results where the study reflects Solana (SOL) and Chia (XCH) can be included in a portfolio of cryptocurrencies with Cardano (ADA), Algorand (ALG), Stellar Lumens (XLM), and Nano (XNO) and they are forming the best clusters of green cryptocurrencies. Originality/value: Dynamic Data Warping along with the Time Series Clusters methodology is first used in the study of green cryptocurrencies where by studying the non-linear relationships and patterns, investors can make informed decisions and mitigate risks. Additionally, policymakers can leverage these insights to formulate effective regulatory frameworks that promote the sustainable growth of the cryptocurrency industry.

Keywords: Green Cryptocurrencies; Dynamic Time Warping; Historical Prices; Non-Linear Relationships; Time Series Clustering.

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