The primary objective of this study is to examine the volatility dynamics of Ethereum, a highly renown cryptocurrency, using time-series econometric models. Utilising a dataset comprising 2,700 observations, this study employs ARCH and GARCH-type volatility models, namely ARCH (1), GARCH (1,1), GJR-GARCH, and EGARCH (1,1), to essentially capture the patterns of Ethereum volatility. The models undergo thorough testing to assess their goodness-of-fit, employing criteria such as AIC, BIC, and HQIC, in addition to conducting residual diagnostics to identify conditional heteroskedasticity and autocorrelation. The EGARCH (1,1) model was found to be the best-fitted model, providing insights into the leverage effects observed in the Ethereum market. The forecasting performance of the model was evaluated using out-of-sample data for a period of 31 trading days in August 2023. The results demonstrated the model's strong out-of-sample predictive ability, as indicated by a Mean Absolute Percentage Error (MAPE) and percentage of correct sign prediction methods. The study concludes by highlighting the limitations pertaining to the research and potential directions for future studies.