Volatility model against deep learning techniques: avoiding the one–size–fit–all model trap for predicting future financial volatility
Mazzocco, Aurelio (A.A. 2022/2023) Volatility model against deep learning techniques: avoiding the one–size–fit–all model trap for predicting future financial volatility. Tesi di Laurea in Econometric theory, Luiss Guido Carli, relatore Paolo Santucci de Magistris, pp. 49. [Master's Degree Thesis]
|
PDF (Full text)
Download (863kB) | Preview |
Abstract/Index
Data. Theoretical framework and previous work. Data download and features. Evidence from studying volatility: the SP500. Descriptive statistics. Testing normality of return. Methodology. GARCH model. Asymmetric GARCH models. Machine learning model. Scaling variables and data splitting. Results. Strategy donstruction.
References
Bibliografia: pp. 46-48.
| 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: | Econometric theory |
| Thesis Supervisor: | Santucci de Magistris, Paolo |
| Thesis Co-Supervisor: | Morelli, Giacomo |
| Academic Year: | 2022/2023 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 27 Jun 2024 07:50 |
| Last Modified: | 27 Jun 2024 07:50 |
| URI: | https://tesi.luiss.it/id/eprint/39108 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
