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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Pratas, T. E., Ramos, F. R. & Rubio, L. J. (2023). Forecasting bitcoin volatility: Exploring the potential of deep learning. Eurasian Economic Review. 13, 285-305
Exportar Referência (IEEE) 
T. E. Pratas et al.,  "Forecasting bitcoin volatility: Exploring the potential of deep learning", in Eurasian Economic Review, vol. 13, pp. 285-305, 2023
Exportar BibTeX 
@article{pratas2023_1730118345961,
	author = "Pratas, T. E. and Ramos, F. R. and Rubio, L. J.",
	title = "Forecasting bitcoin volatility: Exploring the potential of deep learning",
	journal = "Eurasian Economic Review",
	year = "2023",
	volume = "13",
	number = "",
	doi = "10.1007/s40822-023-00232-0",
	pages = "285-305",
	url = "https://doi.org/10.1007/s40822-023-00232-0"
}
Exportar RIS 
TY  - JOUR
TI  - Forecasting bitcoin volatility: Exploring the potential of deep learning
T2  - Eurasian Economic Review
VL  - 13
AU  - Pratas, T. E.
AU  - Ramos, F. R.
AU  - Rubio, L. J.
PY  - 2023
SP  - 285-305
SN  - 1309-422X
DO  - 10.1007/s40822-023-00232-0
UR  - https://doi.org/10.1007/s40822-023-00232-0
AB  - This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold-Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.
ER  -