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 Style AAA Style APA 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, September 3). Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform?. Econometrie. Retrieved from http://hdl.handle.net/2105/72899

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