On options-driven realized volatility forecasting: Information gains via rough volatility model

Zheqi Fan, Meng Melody Wang and Yifan Ye

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Abstract: We examine whether model-based spot volatility estimators extracted from traded options data enhance the predictive power of the Heterogeneous Autoregressive (HAR) model for realized volatility. Specifically, we infer spot volatility under the rough stochastic volatility model via an iterative two-step approach following Andersen et al. (2015a) and adopt a deep learning surrogate to accelerate model estimation from large-scale options panels. Benchmarked against traditional stochastic volatility models (Heston, Bates, SVCJ) and the VIX index, our results demonstrate that the augmented HAR-RV-RHeston model improves daily realized volatility forecasting accuracy and sustains superior performance across horizons up to one month.

Date: 2026-04, Revised 2026-04
New Economics Papers: this item is included in nep-ets, nep-for and nep-rmg
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