A. CEBRIAN-HERNANDEZ, E. JIMÉNEZ-RODRÍGUEZ, A. Díaz
This study aims to utilize Machine Learning techniques to analyze and predict Bitcoin volatility. Unsupervised learning methods like K-Means, DBSCAN, and HDBSCAN are employed to cluster behavioral patterns. The main goal is to determine the relationship between Bitcoin volatility and various exogenous financial variables such as RIOT Blockchain, NVIDIA, KBR, VISA, the EUR/USD exchange rate, and the S&P500 index. These variables serve as exogenous inputs for clustering. The data will be grouped by similar characteristics to assess the impact on cryptocurrency price movements. Preliminary findings suggest that DBSCAN outperforms in terms of cluster separation and noise resistance. However, all models face limitations due to Bitcoin's volatile and unpredictable nature, indicating a need for additional approaches to enhance forecasting accuracy.
Palabras clave: Bitcoin, Volatility, Machine Learning, Clustering Algorithms
Programado
AR1 Análisis de Riesgos II
12 de junio de 2025 15:30
MR 1