RT Dissertation/Thesis
T1 Bitcoin time-series volatility forecast: a comparative analysis of the performance of machine learning algorithms
A1 Cebrian Hernandez, María Ángeles
K1 Bitcoin
K1 Criptomonedas
K1 Volatilidad económica
K1 Modelos de predicción
K1 Inteligencia artificial
AB This Thesis undertakes a comprehensive examination of Bitcoin volatility, investigating its connections with economic, financial, technological variables and various market dynamics. Multivariate statistical time series models (GARCH family) and Artificial Intelligence techniques (supervised and unsupervised learning) are employed to unravel these intricate relationships.The Conditional Correlation approach, in particular the CCC-MGARCH model, emerges as a frontrunner in modelling Bitcoin volatility, taking into account the variables considered. Among the machine learning models, Ridge Regression stands out, outperforming Ensemble and K-Nearest Neighbors in predicting Bitcoin volatility. This finding underlines the effectiveness of internal regularisation in capturing complex interdependencies. The study is in line with previous research, which suggests the accuracy of Random Forest and Ridge Regression in predicting Bitcoin volatility.The unsupervised learning algorithms DBSCAN and HDBSCAN do not perform optimally due to the inherent instability of Bitcoin data. The K-Means analysis, however, reveals valuable data, such as volatility declines around the exponential moving average (EMA) and correlations between the relative strength indices (RSI) of the selected assets.In conclusion, supervised learning models excel in predicting Bitcoin volatility, outperforming unsupervised clustering approaches in the complex financial landscape. However, some limitations remain, such as the difficulty of encompassing all influencing factors and the inherent unpredictability of Bitcoin volatility. The study advocates exploring additional variables associated with news, market sentiment and external events to refine predictions, recognising that Bitcoin volatility remains a difficult task.
YR 2024
FD 2024
LK https://hdl.handle.net/10433/21660
UL https://hdl.handle.net/10433/21660
LA en
NO Programa de Doctorado en Administración y Dirección de EmpresasLínea de Investigación: Control de Gestión y FinanzasClave Programa: DAECódigo Línea: 102
NO Universidad Pablo de Olavide. Departamento de Economía Financiera y Contabilidad
DS RIO
RD Apr 25, 2026