Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913017924419584 |
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| author | Sakuma, Takayuki |
| author_facet | Sakuma, Takayuki |
| contents | We present a differential machine learning method for zero-days-to-expiry (0DTE) options under a stochastic-volatility jump-diffusion model. To handle the ultra-short-maturity regime, we express the option price in Black-Scholes form with a maturity-gated variance correction, combining supervision on prices and Greeks with a PIDE-residual penalty. Prices and Greeks are derived from a single trained pricing network, while jump-term identifiability is ensured by a jump-operator network fitted jointly in a three-stage procedure. The method improves jump-term approximation relative to one-stage baselines while maintaining comparable pricing errors. Furthermore, it reduces errors in Greeks, produces stable one-day delta hedges, and offers significant speedups over Fourier-based benchmarks. Calibration experiments demonstrate the network's efficiency as a pricer; notably, incorporating jump-intensity price sensitivity into the learning process further improves the overall model fit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07600 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps Sakuma, Takayuki Computational Finance We present a differential machine learning method for zero-days-to-expiry (0DTE) options under a stochastic-volatility jump-diffusion model. To handle the ultra-short-maturity regime, we express the option price in Black-Scholes form with a maturity-gated variance correction, combining supervision on prices and Greeks with a PIDE-residual penalty. Prices and Greeks are derived from a single trained pricing network, while jump-term identifiability is ensured by a jump-operator network fitted jointly in a three-stage procedure. The method improves jump-term approximation relative to one-stage baselines while maintaining comparable pricing errors. Furthermore, it reduces errors in Greeks, produces stable one-day delta hedges, and offers significant speedups over Fourier-based benchmarks. Calibration experiments demonstrate the network's efficiency as a pricer; notably, incorporating jump-intensity price sensitivity into the learning process further improves the overall model fit. |
| title | Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2603.07600 |