Forecasting commodity returns: a comparative study of linear machine learning models’ predictive performance
Rizzitano, Paola (A.A. 2024/2025) Forecasting commodity returns: a comparative study of linear machine learning models’ predictive performance. Tesi di Laurea in Financial economics, Luiss Guido Carli, relatore Nicola Borri, pp. 53. [Master's Degree Thesis]
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Abstract/Index
Data presentation and analysis. Commodity futures prices. Forecasting methodology. Autoregressive models. Multivariate ordinary least squares. Regularized predictive regressions: lasso. Regularized predictive regression: ridge. Results. Autoregressive models as a time-series benchmark. Predictive signals and preliminary evidence. Forecasting results: expanding window-OLS. Forecasting results: expanding window–LASSO. Forecasting results: expanding window–ridge. Discussion. Heterogeneity in commodity returns’ predictability. Gold: financialized asset and safe heaven dynamics. Corn: inventory cycles and gradual information diffusion. Natural gas: volatility, regime shifts and model instability.
References
Bibliografia: pp. 45-46.
| 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: | Financial economics |
| Thesis Supervisor: | Borri, Nicola |
| Thesis Co-Supervisor: | Patnaik, Megha |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 16 Jun 2026 14:05 |
| Last Modified: | 16 Jun 2026 14:05 |
| URI: | https://tesi.luiss.it/id/eprint/46177 |
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