Forecasting bitcoin time series with ARIMA GARCH and recurrent neural networks
Cuomo, Daniele (A.A. 2019/2020) Forecasting bitcoin time series with ARIMA GARCH and recurrent neural networks. Tesi di Laurea in Asset pricing, Luiss Guido Carli, relatore Nicola Borri, pp. 96. [Master's Degree Thesis]
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Abstract/Index
The bitcoin technology. History of bitcoin. Cryptography. The bitcoin network. Criticism. Bitcoin economics. Time series analysis of bitcoin. Theoretical framework. Conditional heteroscedasticity modeling. Parameter estimation & forecasting. Data analysis. Sequential learning with recurrent neural networks.
References
Bibliografia: pp. 82-85.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56) |
| Chair: | Asset pricing |
| Thesis Supervisor: | Borri, Nicola |
| Thesis Co-Supervisor: | Reichlin, Pietro |
| Academic Year: | 2019/2020 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 07 Jul 2021 07:13 |
| Last Modified: | 23 Jan 2023 11:23 |
| URI: | https://tesi.luiss.it/id/eprint/29995 |
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