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arXiv · 2510.19126

An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps

Keyuan Wu·Tenghan Zhong·Yuxuan Ouyang
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Abstract

We present a fast and robust calibration method for stochastic volatility models that admit Fourier-analytic transform-based pricing via characteristic functions. The design is structure-preserving: we keep the original pricing transform and (i) split the pricing formula into data-independent inte- grals and a market-dependent remainder; (ii) precompute those data-independent integrals with GPU acceleration; and (iii) approximate only the remaining, market-dependent pricing map with a small neural network. We instantiate the workflow on a rough volatility model with tempered-stable jumps tailored to power-type volatility derivatives and calibrate it to VIX options with a global-to-local search. We verify that a pure-jump rough volatility model adequately captures the VIX dynamics, consistent with prior empirical findings, and demonstrate that our calibration method achieves high accuracy and speed.

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Keyuan Wu, Tenghan Zhong, Yuxuan Ouyang. 2025-10-21. An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps. https://arxiv.org/abs/2510.19126

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