Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform?

Additional Metadata
Keywords	Option pricing; S&P 500 index; European Options; Modeling; Forecasting; Implied Volatility Surface Characteristics; Machine Learning; Random Forest regression; Neural Networks; Long Short-Term Memory (LSTM); Ordinary Least Squares (OLS); Autoregressive model (AR); Elastic Net (ENet); Hyperparameter Tuning; Out-of-Sample R-squared; Root mean squared error (RMSE); Cumulative Sum of Squared Error Difference (CSSED); Permuted Variable Importance (VI)
Thesis Advisor	Freire, G
Persistent URL	hdl.handle.net/2105/72899
Series	Econometrie
Organisation	Erasmus School of Economics
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Noort, T.C.W. van de. (2024). Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform?. In Econometrie.http://hdl.handle.net/2105/72899

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