Machine learning for volatility forecasting

Romano, Simone (A.A. 2019/2020) Machine learning for volatility forecasting. Tesi di Laurea in Asset pricing, Luiss Guido Carli, relatore Emilio Barone, pp. 90. [Master's Degree Thesis]

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Volatility. Defining and measuring volatility. Realized volatility. Implied volatility. The VIX index. Stylized facts: evidence from the S&P 500. Econometric models. The EWMA model. The ARCH model. The GARCH model. Maximum likelihood estimation. Machine learning models. Artificial neural networks. Gradient descent and backpropagation. Recurrent neural networks. Long short-term memory (LSTM). Experimental setup and results. Data description. Methodology. Hyperparameter tuning. Results.


Bibliografia: pp. 55-57.

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)
Outstanding Thesis: Department of Economics and Finance
Chair: Asset pricing
Thesis Supervisor: Barone, Emilio
Thesis Co-Supervisor: Borri, Nicola
Academic Year: 2019/2020
Session: Extraordinary
Additional Information: La tesi è vincitrice del Premio "Tesi d'Eccellenza" 2019/2020 ed è pubblicata online dalla Luiss University Press nella collana Working Paper.
Deposited by: Alessandro Perfetti
Date Deposited: 02 Jul 2021 07:00
Last Modified: 14 Jul 2021 09:26


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