FEDS Paper: Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting

Hyung Joo Kim and Dong Hwan OhDespite documented heterogeneity in volatility dynamics across the option surface, standard implied volatility forecasting models apply homogeneous parameters throughout. We introduce a machine-learning framework that uses regression trees to partition the surface along both moneyness and maturity dimensions, identifying data-driven regions where distinct forecasting models perform best.

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