Analysis of bitcoin prices using a heavy-tailed version of Dagum distribution and machine learning methods

【Author】 Ting, Lai; Abd El-Raouf, M. M.; Bakr, M. E.; Alsahangiti, Arwa M.

【Source】ALEXANDRIA ENGINEERING JOURNAL

【影响因子】6.626

【Abstract】Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted TX family approach. The proposed model is called the weighted TX Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods.

【Keywords】Dagum distribution; Heavy-tailed characteristics; Bitcoin prices; Macroeconomic variables; Machine learning and robust regression; methods

【发表时间】2023 1-Oct

【收录时间】2023-10-23

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测