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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Abstract/Index
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.
References
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 |
| URI: | https://tesi.luiss.it/id/eprint/29954 |
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