Predicting price volatility crytocurrency ethereum

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dc.contributor.advisorSigauke, Caston
dc.contributor.advisorRavele, Thakhani
dc.contributor.authorRambevha, Vhukhudo Ronny
dc.date2023
dc.date.accessioned2026-01-24T12:05:48Z
dc.date.available2026-01-24T12:05:48Z
dc.date.issued2025-09-05
dc.descriptionMSc (e-Science)
dc.descriptionDepartment of Mathematical and Computational Sciences
dc.description.abstractVolatility 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.
dc.format.extent1 online resource (ix, 52 leaves): color illustrations
dc.identifier.apacitationRambevha, V. R. (2025). <i>Predicting price volatility crytocurrency ethereum</i>. (). . Retrieved from en_ZA
dc.identifier.chicagocitationRambevha, Vhukhudo Ronny. <i>"Predicting price volatility crytocurrency ethereum."</i> ., , 2025. en_ZA
dc.identifier.citationRambevha, V.R. 2025. Predicting price volatility crytocurrency ethereum. . . en_ZA
dc.identifier.ris 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 - en_ZA
dc.identifier.urihttps://univendspace.univen.ac.za/handle/11602/3103
dc.identifier.vancouvercitationRambevha VR. Predicting price volatility crytocurrency ethereum. []. , 2025 [cited yyyy month dd]. Available from: en_ZA
dc.language.isoen
dc.relation.requiresPDF
dc.rightsUniversity of Venda
dc.subjectCrytocurrency
dc.subjectEthereum
dc.subjectRecurrent neural network
dc.subjectSupport vector regression
dc.subjectVolatility forecasting
dc.titlePredicting price volatility crytocurrency ethereum
dc.typeDissertation
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