Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting

Hyung Joo Kim and Dong Hwan Oh
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Hyung Joo Kim: https://www.federalreserve.gov/econres/hyung-joo-kim.htm
Dong Hwan Oh: https://www.federalreserve.gov/econres/dong-hwan-oh.htm

No 2026-049, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Despite 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. Extending the Surface Heterogeneous Autoregressive (SHAR) framework of Dufays, Jacobs, and Rombouts (2025), we develop tree-based SHAR specifications that preserve interpretable structure while allowing model parameters to vary across the surface. Empirical analysis using S&P 500 options demonstrates that the boosted tree-based specification achieves the lowest out-of-sample forecast errors across all horizons, reducing one-month-ahead RMSE by 13 percent versus the benchmark SHAR model. The improvements are statistically significant and particularly pronounced during stress periods. The estimated tree presents economically interpretable segmentation: short-dated options exhibit higher daily persistence but lower monthly persistence than long-dated options, while deep out-of-the-money calls or puts display distinct dynamics from near-the-money contracts.

Keywords: implied volatility forecasting; option surface; machine learning; regression trees; ensemble methods; heterogeneous autoregressive models (search for similar items in EconPapers)
JEL-codes: C14 C22 C32 C51 C53 C58 G12 (search for similar items in EconPapers)
Pages: 53 p.
Date: 2026-07-06
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:103519

DOI: 10.17016/FEDS.2026.049

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