Sigauke, CastonRavele, ThakhaniRambevha, Vhukhudo Ronny2026-01-242026-01-242025-09-05Rambevha, V.R. 2025. Predicting price volatility crytocurrency ethereum. . .https://univendspace.univen.ac.za/handle/11602/3103MSc (e-Science)Department of Mathematical and Computational SciencesVolatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309.1 online resource (ix, 52 leaves): color illustrationsenUniversity of VendaCrytocurrencyEthereumUCTDRecurrent neural networkSupport vector regressionVolatility forecastingDissertationRambevha VR. Predicting price volatility crytocurrency ethereum. []. , 2025 [cited yyyy month dd]. Available from:Rambevha, V. R. (2025). <i>Predicting price volatility crytocurrency ethereum</i>. (). . Retrieved fromRambevha, Vhukhudo Ronny. <i>"Predicting price volatility crytocurrency ethereum."</i> ., , 2025.TY - Thesis AU - Rambevha, Vhukhudo Ronny AB - Volatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309. DA - 2025-09-05 DB - ResearchSpace DP - Univen KW - Crytocurrency KW - Ethereum KW - Recurrent neural network KW - Support vector regression KW - Volatility forecasting LK - https://univendspace.univen.ac.za PY - 2025 T1 - Predicting price volatility crytocurrency ethereum TI - Predicting price volatility crytocurrency ethereum UR - ER -