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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.19126 |
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| _version_ | 1866917032249786368 |
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| author | Wu, Keyuan Zhong, Tenghan Ouyang, Yuxuan |
| author_facet | Wu, Keyuan Zhong, Tenghan Ouyang, Yuxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps Wu, Keyuan Zhong, Tenghan Ouyang, Yuxuan Computational Finance Pricing of Securities 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. |
| title | An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps |
| topic | Computational Finance Pricing of Securities |
| url | https://arxiv.org/abs/2510.19126 |