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: 07 Jul 2021 07:13
URI: https://tesi.luiss.it/id/eprint/29995

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