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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.00445 |
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| _version_ | 1866909090090844160 |
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| author | Arai, Takuji Imai, Yuto |
| author_facet | Arai, Takuji Imai, Yuto |
| contents | This paper aims to develop a supervised deep-learning scheme to compute call option prices for the Barndorff-Nielsen and Shephard model with a non-martingale asset price process having infinite active jumps. In our deep learning scheme, teaching data is generated through the Monte Carlo method developed by Arai and Imai (2024). Moreover, the BNS model includes many variables, which makes the deep learning accuracy worse. Therefore, we will create another input variable using the Black-Scholes formula. As a result, the accuracy is improved dramatically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Option pricing for Barndorff-Nielsen and Shephard model by supervised deep learning Arai, Takuji Imai, Yuto Computational Finance This paper aims to develop a supervised deep-learning scheme to compute call option prices for the Barndorff-Nielsen and Shephard model with a non-martingale asset price process having infinite active jumps. In our deep learning scheme, teaching data is generated through the Monte Carlo method developed by Arai and Imai (2024). Moreover, the BNS model includes many variables, which makes the deep learning accuracy worse. Therefore, we will create another input variable using the Black-Scholes formula. As a result, the accuracy is improved dramatically. |
| title | Option pricing for Barndorff-Nielsen and Shephard model by supervised deep learning |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2402.00445 |