Masoud Eshaghinasrabadi, California State University, USA

This article undertakes an extensive statistical examination of significant cryptocurrencies, expanding on the groundwork in the previous report, "A Statistical Analysis of Cryptocurrencies." Our study delves into the dynamics of Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, and Dogecoin, utilizing trading prices from 2017 to 2022 and considering significant events like the COVID-19 pandemic. Employing correlation analysis, our investigation aims to unravel the intricate relationships between these leading cryptocurrencies. The findings underscore the necessity of achieving greater independence among candidate distributions to accurately model the return of all popular cryptos, suggesting an enhanced correlation among some. The generalized hyperbolic and generalized t distributions emerged as top-performing models despite limitations in overall fitness that varied across cryptocurrencies, with Tether exhibiting the least favorable fit. Using the fitted models, we forecasted average daily returns from January 1st to February 1st, 2023, demonstrating generally reliable predictive validity. These insights are pivotal in understanding cryptocurrency movements and mitigating the associated trading risks.

Cryptocurrency Dynamics, StatisticalExamination, GroundworkExpansion, CorrelationAnalysis, CandidateDistributions, Independence Modeling, Generalized Hyperbolic Distribution, Generalized t Distribution., Predictive Validity, Trading Risks, Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, Dogecoin, Trading Prices, COVID-19 Impa